1. A deep learning-based image processing technique for detecting diseases in leaves of oilseed crops, comprising: a) an image acquisition system, configured to capture high-resolution digital images of oilseed crop leaves, wherein said images provide visual data for the analysis and identification of disease presence; b) a pre-processing module, configured to process said captured images by performing operations including but not limited to resizing, normalization, and augmentation, in order to prepare the images for analysis by a convolutional neural network (CNN); c) a convolutional neural network (CNN), comprising a series of layers including input, convolutional, pooling, and fully connected layers, wherein said CNN is trained on a dataset of labeled oilseed crop leaf images, allowing it to learn and recognize features indicative of disease presence; d) a classification module, coupled to said CNN, configured to analyze the output of the CNN and classify the captured oilseed crop leaf images based on the likelihood of disease presence; e) a user interface, configured to display the results of the classification, including the identification of disease presence, type, and severity, as well as recommendations for disease management and mitigation; and f) wherein said deep learning-based image processing technique enables the rapid and accurate detection of diseases in oilseed crop leaves, allowing for timely intervention and treatment, ultimately leading to improved crop yield and quality.

2. The deep learning-based image processing technique of claim 1, wherein said image acquisition system comprises a mobile device, drone, or remote sensing platform, enabling the efficient and non-destructive collection of oilseed crop leaf images in a variety of field conditions.

3. The deep learning-based image processing technique of claim 1, wherein said CNN is trained using transfer learning techniques, leveraging pre-trained models and adapting them to the specific task of oilseed crop leaf disease detection, resulting in increased training efficiency and improved classification accuracy.

4. The deep learning-based image processing technique of claim 1, wherein said user interface includes a geographical information system (GIS) component, which displays the spatial distribution of disease presence and severity across an oilseed crop field, enabling targeted and efficient disease management interventions.

5. A method for detecting diseases in leaves of oilseed crops using a deep learning-based image processing technique, the method comprising the steps of: a) acquiring high-resolution digital images of oilseed crop leaves, wherein said images provide visual data for the analysis and identification of disease presence; b) pre-processing said acquired images by performing operations including but not limited to resizing, normalization, and augmentation, in order to prepare the images for analysis by a convolutional neural network (CNN); c) inputting the pre-processed images into a convolutional neural network (CNN) comprising a series of layers including input, convolutional, pooling, and fully connected layers, wherein said CNN is trained on a dataset of labeled oilseed crop leaf images, allowing it to learn and recognize features indicative of disease presence; d) analyzing the output of the CNN and classifying the captured oilseed crop leaf images based on the likelihood of disease presence; e) displaying the results of the classification, including the identification of disease presence, type, and severity, as well as recommendations for disease management and mitigation through a user interface; and f) utilizing said deep learning-based image processing technique to enable the rapid and accurate detection of diseases in oilseed crop leaves, allowing for timely intervention and treatment, ultimately leading to improved crop yield and quality.

6. The method of claim 5, wherein said step of acquiring high-resolution digital images of oilseed crop leaves comprises using a mobile device, drone, or remote sensing platform, enabling the efficient and non-destructive collection of oilseed crop leaf images in a variety of field conditions.

7. The method of claim 5, wherein said step of training the CNN involves using transfer learning techniques, leveraging pre-trained models and adapting them to the specific task of oilseed crop leaf disease detection, resulting in increased training efficiency and improved classification accuracy.

8. The method of claim 1, further comprising the step of displaying the spatial distribution of disease presence and severity across an oilseed crop field through a geographical information system (GIS) component within the user interface, enabling targeted and efficient disease management interventions. Deep Learning-Based Image Processing Technique for Disease Detection in Oilseed Crop Leaves Abstract The present invention relates to a deep learning-based image processing technique for rapid and accurate detection of diseases in oilseed crop leaves. The technique utilizes a convolutional neural network (CNN) trained on a large dataset of labeled images of healthy and diseased oilseed crop leaves, representing various diseases and stages of infection. The CNN automatically extracts and learns disease-specific features from the input images, allowing for efficient identification and classification of diseases. This deep learning-based approach offers a non-destructive, real-time, and cost-effective solution for disease detection in oilseed crops, facilitating timely intervention and improved crop management. , C , Claims:Claims :

1. A deep learning-based image processing technique for detecting diseases in leaves of oilseed crops, comprising: a) an image acquisition system, configured to capture high-resolution digital images of oilseed crop leaves, wherein said images provide visual data for the analysis and identification of disease presence; b) a pre-processing module, configured to process said captured images by performing operations including but not limited to resizing, normalization, and augmentation, in order to prepare the images for analysis by a convolutional neural network (CNN); c) a convolutional neural network (CNN), comprising a series of layers including input, convolutional, pooling, and fully connected layers, wherein said CNN is trained on a dataset of labeled oilseed crop leaf images, allowing it to learn and recognize features indicative of disease presence; d) a classification module, coupled to said CNN, configured to analyze the output of the CNN and classify the captured oilseed crop leaf images based on the likelihood of disease presence; e) a user interface, configured to display the results of the classification, including the identification of disease presence, type, and severity, as well as recommendations for disease management and mitigation; and f) wherein said deep learning-based image processing technique enables the rapid and accurate detection of diseases in oilseed crop leaves, allowing for timely intervention and treatment, ultimately leading to improved crop yield and quality.

2. The deep learning-based image processing technique of claim 1, wherein said image acquisition system comprises a mobile device, drone, or remote sensing platform, enabling the efficient and non-destructive collection of oilseed crop leaf images in a variety of field conditions.

3. The deep learning-based image processing technique of claim 1, wherein said CNN is trained using transfer learning techniques, leveraging pre-trained models and adapting them to the specific task of oilseed crop leaf disease detection, resulting in increased training efficiency and improved classification accuracy.

4. The deep learning-based image processing technique of claim 1, wherein said user interface includes a geographical information system (GIS) component, which displays the spatial distribution of disease presence and severity across an oilseed crop field, enabling targeted and efficient disease management interventions.

5. A method for detecting diseases in leaves of oilseed crops using a deep learning-based image processing technique, the method comprising the steps of: a) acquiring high-resolution digital images of oilseed crop leaves, wherein said images provide visual data for the analysis and identification of disease presence; b) pre-processing said acquired images by performing operations including but not limited to resizing, normalization, and augmentation, in order to prepare the images for analysis by a convolutional neural network (CNN); c) inputting the pre-processed images into a convolutional neural network (CNN) comprising a series of layers including input, convolutional, pooling, and fully connected layers, wherein said CNN is trained on a dataset of labeled oilseed crop leaf images, allowing it to learn and recognize features indicative of disease presence; d) analyzing the output of the CNN and classifying the captured oilseed crop leaf images based on the likelihood of disease presence; e) displaying the results of the classification, including the identification of disease presence, type, and severity, as well as recommendations for disease management and mitigation through a user interface; and f) utilizing said deep learning-based image processing technique to enable the rapid and accurate detection of diseases in oilseed crop leaves, allowing for timely intervention and treatment, ultimately leading to improved crop yield and quality.

6. The method of claim 5, wherein said step of acquiring high-resolution digital images of oilseed crop leaves comprises using a mobile device, drone, or remote sensing platform, enabling the efficient and non-destructive collection of oilseed crop leaf images in a variety of field conditions.

7. The method of claim 5, wherein said step of training the CNN involves using transfer learning techniques, leveraging pre-trained models and adapting them to the specific task of oilseed crop leaf disease detection, resulting in increased training efficiency and improved classification accuracy.

8. The method of claim 1, further comprising the step of displaying the spatial distribution of disease presence and severity across an oilseed crop field through a geographical information system (GIS) component within the user interface, enabling targeted and efficient disease management interventions.

Description:Deep Learning-Based Image Processing Technique for Disease Detection in Oilseed Crop Leaves
Field of the Invention
[0001] The present invention relates generally to the field of agriculture and plant disease management, and more specifically, to the application of deep learning and image processing techniques for the detection and classification of diseases in oilseed crop leaves. The invention provides a non-destructive, rapid, and accurate method for monitoring the health of oilseed crops, enabling timely intervention and improved crop management.
[0001]
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Oilseed crops are an essential source of vegetable oils and proteins, which are vital components in human nutrition and animal feed. They also play a significant role in the production of biofuels and various industrial products. The most common oilseed crops include soybean, rapeseed (canola), sunflower, peanut, and cottonseed. Given their economic and nutritional importance, extensive research has been conducted to understand their growth, development, yield optimization, and disease management.
[0004] Understanding the genetic makeup of oilseed crops is crucial for breeding programs aimed at developing new varieties with desirable traits. Researchers have been exploring the genomes of various oilseed crops to identify genes responsible for high yield, disease resistance, and other agronomically important characteristics. Advances in molecular biology, genetic engineering, and marker-assisted selection have enabled the development of improved oilseed crop varieties with higher oil content, better nutritional profiles, and increased resistance to pests and diseases.
[0005] Researchers have also investigated the best agronomic practices for oilseed crop production. This includes studying the effects of different planting densities, nutrient management strategies, irrigation schedules, and crop rotation systems on crop yield and quality. Studies have focused on identifying the optimal conditions for plant growth and development while minimizing the impact of biotic and abiotic stress factors.
[0006] Disease management is a critical aspect of oilseed crop production as diseases can significantly reduce crop yields and quality. Research on oilseed crop diseases has aimed at understanding the causal agents, epidemiology, and host-pathogen interactions. This knowledge has guided the development of integrated disease management strategies, which include the use of resistant varieties, chemical control measures, and cultural practices such as crop rotation, sanitation, and timely planting.
[0007] Disease detection in oilseed crops is crucial for maintaining crop productivity, quality, and overall yield. Timely identification and management of diseases can help prevent significant losses and ensure a steady supply of oilseeds for human consumption, animal feed, and industrial uses. Various techniques have been developed and employed for disease detection in oilseed crops, ranging from traditional visual inspections to advanced technological approaches. The most common method for disease detection in oilseed crops is visual inspection. This approach involves trained personnel examining the plants for any signs of diseases, such as leaf discoloration, lesions, necrosis, or abnormal growth patterns. Visual inspection can be time-consuming and labor-intensive, and its accuracy largely depends on the experience and expertise of the personnel conducting the inspection.
[0008] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
Summary
[0009] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00010] The present invention relates generally to the field of agriculture and plant disease management, and more specifically, to the application of deep learning and image processing techniques for the detection and classification of diseases in oilseed crop leaves. The invention provides a non-destructive, rapid, and accurate method for monitoring the health of oilseed crops, enabling timely intervention and improved crop management.
[00011] The present patent introduces a comprehensive deep learning-based image processing technique designed for detecting diseases in leaves of oilseed crops. The technique enables timely intervention and treatment, ultimately leading to improved crop yield and quality. The proposed system consists of the following components:
[00012] Image Acquisition System: This system is responsible for capturing high-resolution digital images of oilseed crop leaves, providing visual data for analysis and identification of disease presence. The image acquisition system can be implemented using various platforms such as mobile devices, drones, or remote sensing platforms, which allows for efficient and non-destructive collection of oilseed crop leaf images in diverse field conditions.
[00013] Pre-processing Module: This module processes the captured images by performing operations including resizing, normalization, and augmentation. These operations prepare the images for analysis by a convolutional neural network (CNN), ensuring that the images are in a suitable format for feature extraction and disease identification.
[00014] Convolutional Neural Network (CNN): The CNN consists of a series of layers, including input, convolutional, pooling, and fully connected layers. The CNN is trained on a dataset of labeled oilseed crop leaf images, enabling it to learn and recognize features indicative of disease presence. Transfer learning techniques can be employed, leveraging pre-trained models and adapting them to the specific task of oilseed crop leaf disease detection. This results in increased training efficiency and improved classification accuracy.
[00015] Classification Module: This module is coupled to the CNN and is configured to analyze the output of the CNN. It classifies the captured oilseed crop leaf images based on the likelihood of disease presence, enabling the system to make informed decisions about the health of the crop.
[00016] User Interface: The user interface displays the results of the classification, including the identification of disease presence, type, and severity. It also provides recommendations for disease management and mitigation, empowering users to take appropriate action in response to the detected diseases. Furthermore, the user interface includes a geographical information system (GIS) component, which displays the spatial distribution of disease presence and severity across an oilseed crop field. This feature enables targeted and efficient disease management interventions.
[00017] The disclosed deep learning-based image processing technique provides rapid and accurate detection of diseases in oilseed crop leaves, allowing farmers and agronomists to make timely decisions regarding intervention and treatment measures. This ultimately contributes to improved crop yield and quality, benefiting both producers and consumers.
Brief Description of the Drawings
[00018] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00019] FIG. 1 illustrate block diagram of oilseed palnt disease detection system, according to some embodiments of the present disclosure.
[00020] Fig. 2 depicts flow diagram of disclosed method 200 for detecting diseases in leaves of oilseed crops using a deep learning-based image processing technique.
[00021] FIG. 3 is a flowchart outlines a procedure for developing a model for identifying diseases in oilseed plants using image processing and machine learning techniques, in accrodance with embodiment of presetnt disclosure.
Detailed Description
[00022] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00023] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00024] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00025] The present invention relates generally to the field of agriculture and plant disease management, and more specifically, to the application of deep learning and image processing techniques for the detection and classification of diseases in oilseed crop leaves. The invention provides a non-destructive, rapid, and accurate method for monitoring the health of oilseed crops, enabling timely intervention and improved crop management.
[00026] The present invention relates to a deep learning-based image processing technique for the detection of diseases in leaves of oilseed crops, such as canola, soybean, sunflower, and others. This system 100 provides an efficient, accurate, and cost-effective method for early disease detection and management in oilseed crop cultivation, ultimately promoting increased crop yield and quality.
[00027] Fig. 1 illustrate block diagram of oilseed palnt disease detection system 100. The invention comprises several components and steps, which are described below in detail.
[00028] Image Acquisition System 102:
[00029] In this embodiment, the image acquisition system comprises a mobile device, such as a smartphone or tablet, equipped with a high-resolution camera. Alternatively, the image acquisition system could be implemented using a drone or a remote sensing platform. The image acquisition system is configured to capture high-resolution digital images of oilseed crop leaves under various environmental conditions, including different lighting and weather conditions. These images provide visual data for the analysis and identification of disease presence in the leaves.
[00030] Pre-Processing Module 104:
[00031] The pre-processing module is responsible for processing the captured images before they are input into the convolutional neural network (CNN). This may include resizing the images to a consistent size, normalizing the pixel values, and augmenting the dataset by applying various transformations, such as rotations, flips, and translations. The pre-processing module ensures that the images are in a suitable format for analysis by the CNN and improves the robustness of the system to variations in image quality and lighting conditions.
[00032] Convolutional Neural Network (CNN) 106:
[00033] The convolutional neural network (CNN) is the core component of the deep learning-based image processing technique. The CNN comprises a series of layers, including input, convolutional, pooling, and fully connected layers. In this embodiment, the CNN is trained on a dataset of labeled oilseed crop leaf images, which includes examples of healthy leaves and leaves affected by various diseases.
[00034] Transfer learning techniques may be employed to leverage pre-trained models and adapt them to the specific task of oilseed crop leaf disease detection. This can result in increased training efficiency and improved classification accuracy.
[00035] Classification Module 108:
[00036] The classification module is coupled to the CNN and is responsible for analyzing the output of the CNN. Based on the features extracted by the CNN, the classification module classifies the captured oilseed crop leaf images into different categories, such as healthy, diseased, or specific disease types. In addition, the classification module may estimate the severity of the disease, if present.
[00037] User Interface 110:
[00038] The user interface is designed to display the results of the classification in an intuitive and easy-to-understand manner. The interface may include visual representations of the captured leaf images, along with the identified disease presence, type, and severity. The user interface may also provide recommendations for disease management and mitigation based on the detected disease type and severity.
[00039] In some embodiments, the user interface may include a geographical information system (GIS) component, which displays the spatial distribution of disease presence and severity across an oilseed crop field. This information can be valuable for targeted and efficient disease management interventions, such as the application of pesticides or the implementation of other disease control measures.
[00040] The deep learning-based image processing technique for disease detection in oilseed crop leaves provides a rapid and accurate method for early disease detection, enabling timely intervention and treatment. By facilitating improved disease management, this invention ultimately contributes to increased crop yield and quality.
[00041] In this embodiment, a drone equipped with a high-resolution multispectral camera is used to acquire aerial images of oilseed crop fields. The drone captures images at various wavelengths, providing additional information about the health and disease status of the plants. The acquired images are transmitted to a cloud-based server for pre-processing, analysis, and classification using the deep learning-based image processing technique. This approach enables rapid and large-scale assessment of oilseed crop fields, potentially detecting disease outbreaks at an early stage and facilitating efficient and targeted disease management interventions.
[00042] In this embodiment, the deep learning-based image processing technique for disease detection in oilseed crop leaves is integrated with IoT (Internet of Things) devices and smart farming systems. Various sensors, such as soil moisture sensors, weather stations, and crop health monitoring devices, collect data that can be used to provide context and additional information to the image processing technique. By combining the image analysis results with data from IoT devices, the system can provide more accurate and comprehensive disease detection and management recommendations, ultimately improving crop yield and quality.
[00043] In this embodiment, the deep learning-based image processing technique for disease detection in oilseed crop leaves is combined with an automated disease management and intervention system. Upon detecting the presence of a disease in the leaves, the system automatically triggers the appropriate disease management actions, such as adjusting irrigation, applying pesticides or other treatments, or implementing cultural control measures. This embodiment allows for a more efficient and timely response to disease outbreaks, minimizing crop loss and maximizing yield.
[00044] In this embodiment, the deep learning-based image processing technique for disease detection in oilseed crop leaves is integrated with plant phenotyping platforms. These platforms typically consist of advanced imaging and sensing technologies that capture detailed information about the growth, development, and health of plants under various environmental conditions. By incorporating the image processing technique into the phenotyping platform, researchers and breeders can efficiently screen large populations of oilseed crops for disease resistance and other desirable traits, accelerating the development of improved crop varieties.
[00045] The proposed image processing technique is designed to detect and classify diseases in oilseed crop leaves, such as soybean, rapeseed, sunflower, and canola, using digital images of the leaves. This technique aims to provide an accurate, efficient, and non-invasive method for early disease detection and classification, enabling farmers to implement timely and targeted interventions to control and manage crop diseases, ultimately improving crop yield and quality.
[00046] Image Acquisition: High-resolution digital images of oilseed crop leaves are captured using a digital camera, smartphone, or other imaging devices. Images can be acquired under controlled lighting conditions or natural sunlight, with care taken to minimize shadows, glare, and other artifacts that may affect image quality.
[00047] Image Pre-processing: The acquired images undergo a series of pre-processing steps to enhance their quality and facilitate disease detection. These steps may include noise reduction, contrast enhancement, and color balancing, among others. Pre-processing ensures that the images are suitable for further analysis and helps to minimize the impact of variations in lighting, image resolution, and other factors on disease detection performance.
[00048] Segmentation and Feature Extraction: The pre-processed images are then segmented to separate the regions of interest (ROI), i.e., the leaf areas, from the background. Various segmentation techniques, such as thresholding, edge detection, or region-based methods, can be employed. Once the ROIs are identified, relevant features, such as color, texture, and shape, are extracted from the segmented leaf areas. These features can be used to characterize the disease symptoms and facilitate disease classification.
[00049] Disease Detection and Classification: The extracted features are then used to detect and classify diseases in the oilseed crop leaves. Machine learning algorithms, such as support vector machines (SVM), neural networks, or decision trees, can be employed for this purpose. These algorithms are trained on a labeled dataset of oilseed crop leaf images with known disease types, allowing them to learn the patterns and relationships between the features and disease classes. Once trained, the algorithms can be used to classify new, unlabeled images based on their extracted features, providing an accurate and efficient means of disease detection and classification.
[00050] Disease Severity Assessment: In addition to detecting and classifying diseases, the proposed technique can also be used to assess the severity of the disease symptoms. This can be achieved by analyzing the spatial distribution and extent of the disease symptoms within the segmented leaf areas, providing a quantitative measure of disease severity that can inform disease management decisions.
[00051] User Interface and Reporting: The disease detection and classification results are presented to the user through a user-friendly interface, which may include visualizations of the segmented leaf areas and disease symptoms, as well as textual reports of the detected disease types and severity levels. The interface may also provide recommendations for disease management practices, such as chemical treatments or cultural control measures, based on the detected disease types and severity levels.
[00052] By integrating these embodiments, the proposed image processing technique provides a comprehensive and efficient solution for disease detection and classification in oilseed crop leaves. This can facilitate early and targeted interventions to control and manage crop diseases, ultimately contributing to improved crop yield and quality. The followint table illustrates exemplary suggestions.

Crops Diseases Remedies
Sunflower Head rot Carbendazim foliar spray is advised to control the disease.
Soybean Powdery mildew Using sulfur-containing organic fungicides to control diseases.
Mustard White Rust Treating seeds with metalaxyl seed or thiram seed is highly recommended.
Sesame Phyllody Spray Monocrotophos or Dimethoate combined with intercropping of Sesamum + Redgram
Safflower Alternaria blight Controlled by the spray of mancozeb
Peanut Nematode Diseases Decrease existing infestations through fallowing, crop rotation, and soil solarization

[00053] Fig. 2 depicts flow diagram of disclosed method 200 for detecting diseases in leaves of oilseed crops using a deep learning-based image processing technique involves the following steps:
[00054] At step 202, Acquiring High-Resolution Digital Images: The first step in the method involves acquiring high-resolution digital images of oilseed crop leaves. These images provide visual data essential for the analysis and identification of disease presence. The images can be captured using various platforms such as mobile devices, drones, or remote sensing platforms, which allow for efficient and non-destructive data collection in diverse field conditions.
[00055] At step 204, Pre-processing Acquired Images: Once the images are acquired, they undergo pre-processing operations such as resizing, normalization, and augmentation. Resizing ensures that the images are of a consistent size, while normalization adjusts the intensity values of the images to a standard range. Augmentation involves generating additional images by applying various transformations, such as rotation, flipping, and zooming. These operations help prepare the images for analysis by the convolutional neural network (CNN) and improve the network's ability to generalize to new, unseen data.
[00056] At step 206, Inputting Pre-Processed Images into a CNN: The pre-processed images are then input into a convolutional neural network (CNN) for feature extraction and disease identification. The CNN comprises a series of layers, including input, convolutional, pooling, and fully connected layers. It is trained on a dataset of labeled oilseed crop leaf images, which enables it to learn and recognize features indicative of disease presence. Transfer learning techniques can be employed to leverage pre-trained models and adapt them to the specific task of oilseed crop leaf disease detection, resulting in increased training efficiency and improved classification accuracy.
[00057] At step 208, Analyzing CNN Output and Classifying Images: The output of the CNN is analyzed, and the captured oilseed crop leaf images are classified based on the likelihood of disease presence. This classification process allows the system to make informed decisions about the health of the crop and the presence of diseases.
[00058] At step 210, Displaying Classification Results: The results of the classification are displayed through a user interface, which includes the identification of disease presence, type, and severity. The interface also provides recommendations for disease management and mitigation, empowering users to take appropriate action in response to the detected diseases. Furthermore, the user interface may include a geographical information system (GIS) component that displays the spatial distribution of disease presence and severity across an oilseed crop field, enabling targeted and efficient disease management interventions.
[00059] At step 212, Utilizing the Deep Learning-based Image Processing Technique: By following the above steps, the deep learning-based image processing technique enables rapid and accurate detection of diseases in oilseed crop leaves. This allows for timely intervention and treatment, ultimately leading to improved crop yield and quality. The method provides a powerful tool for farmers and agronomists, helping them monitor crop health and take proactive measures to address diseases before they cause significant damage.
[00060] FIG. 3 is a flowchart outlines a procedure for developing a model for identifying diseases in oilseed plants using image processing and machine learning techniques, in accrodance with embodiment of presetnt disclosure. The process involves taking high-quality photographs of healthy and sick plants, using image augmentation to expand the dataset, applying pre-processing techniques to enhance the images, and performing feature extraction to represent the raw data numerically. The models used for classification include pre-trained parameters such as VGG-16, ResNet50, Alex Net, and Dense Net. The process should result in a model that is accurate, efficient, and cost-effective, providing remedies for any plant diseases that are discovered. The passage also includes a diagram that summarizes each stage of the process. This passage outlines the eight phases of the procedure for developing a model to identify diseases in oilseed plants using image processing and machine learning techniques. The phases include collecting images of healthy and sick plants, expanding the dataset using image augmentation, applying pre-processing techniques, feature extraction, training, testing, and validating the data, using pre-trained models for classification, optimizing the model using optimization techniques, and recommending remedies based on the model's results. The accuracy of the model is measured by its efficiency, and if it is better than the previously generated model, the parameter optimization approach must be changed. Finally, the model can be used to categorize and identify diseases in leaves and suggest remedies.
[00061] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
[00062] The term “memory,” as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage such as physical storage media. Furthermore, a single memory may encompass and in a scenario wherein computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
[00063] Throughout the present disclosure, the term ‘server’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks.
[00064] Throughout the present disclosure, the term “network” relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations.
[00065] Throughout the present disclosure, the term “process”* relates to any collection or set of instructions executable by a computer or other digital system so as to configure the computer or the digital system to perform a task that is the intent of the process.
[00066] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.

Deep Learning-Based Image Processing Technique for Disease Detection in Oilseed Crop Leaves
Field of the Invention
[0001] The present invention relates generally to the field of agriculture and plant disease management, and more specifically, to the application of deep learning and image processing techniques for the detection and classification of diseases in oilseed crop leaves. The invention provides a non-destructive, rapid, and accurate method for monitoring the health of oilseed crops, enabling timely intervention and improved crop management.
[0001]
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Oilseed crops are an essential source of vegetable oils and proteins, which are vital components in human nutrition and animal feed. They also play a significant role in the production of biofuels and various industrial products. The most common oilseed crops include soybean, rapeseed (canola), sunflower, peanut, and cottonseed. Given their economic and nutritional importance, extensive research has been conducted to understand their growth, development, yield optimization, and disease management.
[0004] Understanding the genetic makeup of oilseed crops is crucial for breeding programs aimed at developing new varieties with desirable traits. Researchers have been exploring the genomes of various oilseed crops to identify genes responsible for high yield, disease resistance, and other agronomically important characteristics. Advances in molecular biology, genetic engineering, and marker-assisted selection have enabled the development of improved oilseed crop varieties with higher oil content, better nutritional profiles, and increased resistance to pests and diseases.
[0005] Researchers have also investigated the best agronomic practices for oilseed crop production. This includes studying the effects of different planting densities, nutrient management strategies, irrigation schedules, and crop rotation systems on crop yield and quality. Studies have focused on identifying the optimal conditions for plant growth and development while minimizing the impact of biotic and abiotic stress factors.
[0006] Disease management is a critical aspect of oilseed crop production as diseases can significantly reduce crop yields and quality. Research on oilseed crop diseases has aimed at understanding the causal agents, epidemiology, and host-pathogen interactions. This knowledge has guided the development of integrated disease management strategies, which include the use of resistant varieties, chemical control measures, and cultural practices such as crop rotation, sanitation, and timely planting.
[0007] Disease detection in oilseed crops is crucial for maintaining crop productivity, quality, and overall yield. Timely identification and management of diseases can help prevent significant losses and ensure a steady supply of oilseeds for human consumption, animal feed, and industrial uses. Various techniques have been developed and employed for disease detection in oilseed crops, ranging from traditional visual inspections to advanced technological approaches. The most common method for disease detection in oilseed crops is visual inspection. This approach involves trained personnel examining the plants for any signs of diseases, such as leaf discoloration, lesions, necrosis, or abnormal growth patterns. Visual inspection can be time-consuming and labor-intensive, and its accuracy largely depends on the experience and expertise of the personnel conducting the inspection.
[0008] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
Summary
[0009] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00010] The present invention relates generally to the field of agriculture and plant disease management, and more specifically, to the application of deep learning and image processing techniques for the detection and classification of diseases in oilseed crop leaves. The invention provides a non-destructive, rapid, and accurate method for monitoring the health of oilseed crops, enabling timely intervention and improved crop management.
[00011] The present patent introduces a comprehensive deep learning-based image processing technique designed for detecting diseases in leaves of oilseed crops. The technique enables timely intervention and treatment, ultimately leading to improved crop yield and quality. The proposed system consists of the following components:
[00012] Image Acquisition System: This system is responsible for capturing high-resolution digital images of oilseed crop leaves, providing visual data for analysis and identification of disease presence. The image acquisition system can be implemented using various platforms such as mobile devices, drones, or remote sensing platforms, which allows for efficient and non-destructive collection of oilseed crop leaf images in diverse field conditions.
[00013] Pre-processing Module: This module processes the captured images by performing operations including resizing, normalization, and augmentation. These operations prepare the images for analysis by a convolutional neural network (CNN), ensuring that the images are in a suitable format for feature extraction and disease identification.
[00014] Convolutional Neural Network (CNN): The CNN consists of a series of layers, including input, convolutional, pooling, and fully connected layers. The CNN is trained on a dataset of labeled oilseed crop leaf images, enabling it to learn and recognize features indicative of disease presence. Transfer learning techniques can be employed, leveraging pre-trained models and adapting them to the specific task of oilseed crop leaf disease detection. This results in increased training efficiency and improved classification accuracy.
[00015] Classification Module: This module is coupled to the CNN and is configured to analyze the output of the CNN. It classifies the captured oilseed crop leaf images based on the likelihood of disease presence, enabling the system to make informed decisions about the health of the crop.
[00016] User Interface: The user interface displays the results of the classification, including the identification of disease presence, type, and severity. It also provides recommendations for disease management and mitigation, empowering users to take appropriate action in response to the detected diseases. Furthermore, the user interface includes a geographical information system (GIS) component, which displays the spatial distribution of disease presence and severity across an oilseed crop field. This feature enables targeted and efficient disease management interventions.
[00017] The disclosed deep learning-based image processing technique provides rapid and accurate detection of diseases in oilseed crop leaves, allowing farmers and agronomists to make timely decisions regarding intervention and treatment measures. This ultimately contributes to improved crop yield and quality, benefiting both producers and consumers.
Brief Description of the Drawings
[00018] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00019] FIG. 1 illustrate block diagram of oilseed palnt disease detection system, according to some embodiments of the present disclosure.
[00020] Fig. 2 depicts flow diagram of disclosed method 200 for detecting diseases in leaves of oilseed crops using a deep learning-based image processing technique.
[00021] FIG. 3 is a flowchart outlines a procedure for developing a model for identifying diseases in oilseed plants using image processing and machine learning techniques, in accrodance with embodiment of presetnt disclosure.
Detailed Description
[00022] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00023] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00024] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00025] The present invention relates generally to the field of agriculture and plant disease management, and more specifically, to the application of deep learning and image processing techniques for the detection and classification of diseases in oilseed crop leaves. The invention provides a non-destructive, rapid, and accurate method for monitoring the health of oilseed crops, enabling timely intervention and improved crop management.
[00026] The present invention relates to a deep learning-based image processing technique for the detection of diseases in leaves of oilseed crops, such as canola, soybean, sunflower, and others. This system 100 provides an efficient, accurate, and cost-effective method for early disease detection and management in oilseed crop cultivation, ultimately promoting increased crop yield and quality.
[00027] Fig. 1 illustrate block diagram of oilseed palnt disease detection system 100. The invention comprises several components and steps, which are described below in detail.
[00028] Image Acquisition System 102:
[00029] In this embodiment, the image acquisition system comprises a mobile device, such as a smartphone or tablet, equipped with a high-resolution camera. Alternatively, the image acquisition system could be implemented using a drone or a remote sensing platform. The image acquisition system is configured to capture high-resolution digital images of oilseed crop leaves under various environmental conditions, including different lighting and weather conditions. These images provide visual data for the analysis and identification of disease presence in the leaves.
[00030] Pre-Processing Module 104:
[00031] The pre-processing module is responsible for processing the captured images before they are input into the convolutional neural network (CNN). This may include resizing the images to a consistent size, normalizing the pixel values, and augmenting the dataset by applying various transformations, such as rotations, flips, and translations. The pre-processing module ensures that the images are in a suitable format for analysis by the CNN and improves the robustness of the system to variations in image quality and lighting conditions.
[00032] Convolutional Neural Network (CNN) 106:
[00033] The convolutional neural network (CNN) is the core component of the deep learning-based image processing technique. The CNN comprises a series of layers, including input, convolutional, pooling, and fully connected layers. In this embodiment, the CNN is trained on a dataset of labeled oilseed crop leaf images, which includes examples of healthy leaves and leaves affected by various diseases.
[00034] Transfer learning techniques may be employed to leverage pre-trained models and adapt them to the specific task of oilseed crop leaf disease detection. This can result in increased training efficiency and improved classification accuracy.
[00035] Classification Module 108:
[00036] The classification module is coupled to the CNN and is responsible for analyzing the output of the CNN. Based on the features extracted by the CNN, the classification module classifies the captured oilseed crop leaf images into different categories, such as healthy, diseased, or specific disease types. In addition, the classification module may estimate the severity of the disease, if present.
[00037] User Interface 110:
[00038] The user interface is designed to display the results of the classification in an intuitive and easy-to-understand manner. The interface may include visual representations of the captured leaf images, along with the identified disease presence, type, and severity. The user interface may also provide recommendations for disease management and mitigation based on the detected disease type and severity.
[00039] In some embodiments, the user interface may include a geographical information system (GIS) component, which displays the spatial distribution of disease presence and severity across an oilseed crop field. This information can be valuable for targeted and efficient disease management interventions, such as the application of pesticides or the implementation of other disease control measures.
[00040] The deep learning-based image processing technique for disease detection in oilseed crop leaves provides a rapid and accurate method for early disease detection, enabling timely intervention and treatment. By facilitating improved disease management, this invention ultimately contributes to increased crop yield and quality.
[00041] In this embodiment, a drone equipped with a high-resolution multispectral camera is used to acquire aerial images of oilseed crop fields. The drone captures images at various wavelengths, providing additional information about the health and disease status of the plants. The acquired images are transmitted to a cloud-based server for pre-processing, analysis, and classification using the deep learning-based image processing technique. This approach enables rapid and large-scale assessment of oilseed crop fields, potentially detecting disease outbreaks at an early stage and facilitating efficient and targeted disease management interventions.
[00042] In this embodiment, the deep learning-based image processing technique for disease detection in oilseed crop leaves is integrated with IoT (Internet of Things) devices and smart farming systems. Various sensors, such as soil moisture sensors, weather stations, and crop health monitoring devices, collect data that can be used to provide context and additional information to the image processing technique. By combining the image analysis results with data from IoT devices, the system can provide more accurate and comprehensive disease detection and management recommendations, ultimately improving crop yield and quality.
[00043] In this embodiment, the deep learning-based image processing technique for disease detection in oilseed crop leaves is combined with an automated disease management and intervention system. Upon detecting the presence of a disease in the leaves, the system automatically triggers the appropriate disease management actions, such as adjusting irrigation, applying pesticides or other treatments, or implementing cultural control measures. This embodiment allows for a more efficient and timely response to disease outbreaks, minimizing crop loss and maximizing yield.
[00044] In this embodiment, the deep learning-based image processing technique for disease detection in oilseed crop leaves is integrated with plant phenotyping platforms. These platforms typically consist of advanced imaging and sensing technologies that capture detailed information about the growth, development, and health of plants under various environmental conditions. By incorporating the image processing technique into the phenotyping platform, researchers and breeders can efficiently screen large populations of oilseed crops for disease resistance and other desirable traits, accelerating the development of improved crop varieties.
[00045] The proposed image processing technique is designed to detect and classify diseases in oilseed crop leaves, such as soybean, rapeseed, sunflower, and canola, using digital images of the leaves. This technique aims to provide an accurate, efficient, and non-invasive method for early disease detection and classification, enabling farmers to implement timely and targeted interventions to control and manage crop diseases, ultimately improving crop yield and quality.
[00046] Image Acquisition: High-resolution digital images of oilseed crop leaves are captured using a digital camera, smartphone, or other imaging devices. Images can be acquired under controlled lighting conditions or natural sunlight, with care taken to minimize shadows, glare, and other artifacts that may affect image quality.
[00047] Image Pre-processing: The acquired images undergo a series of pre-processing steps to enhance their quality and facilitate disease detection. These steps may include noise reduction, contrast enhancement, and color balancing, among others. Pre-processing ensures that the images are suitable for further analysis and helps to minimize the impact of variations in lighting, image resolution, and other factors on disease detection performance.
[00048] Segmentation and Feature Extraction: The pre-processed images are then segmented to separate the regions of interest (ROI), i.e., the leaf areas, from the background. Various segmentation techniques, such as thresholding, edge detection, or region-based methods, can be employed. Once the ROIs are identified, relevant features, such as color, texture, and shape, are extracted from the segmented leaf areas. These features can be used to characterize the disease symptoms and facilitate disease classification.
[00049] Disease Detection and Classification: The extracted features are then used to detect and classify diseases in the oilseed crop leaves. Machine learning algorithms, such as support vector machines (SVM), neural networks, or decision trees, can be employed for this purpose. These algorithms are trained on a labeled dataset of oilseed crop leaf images with known disease types, allowing them to learn the patterns and relationships between the features and disease classes. Once trained, the algorithms can be used to classify new, unlabeled images based on their extracted features, providing an accurate and efficient means of disease detection and classification.
[00050] Disease Severity Assessment: In addition to detecting and classifying diseases, the proposed technique can also be used to assess the severity of the disease symptoms. This can be achieved by analyzing the spatial distribution and extent of the disease symptoms within the segmented leaf areas, providing a quantitative measure of disease severity that can inform disease management decisions.
[00051] User Interface and Reporting: The disease detection and classification results are presented to the user through a user-friendly interface, which may include visualizations of the segmented leaf areas and disease symptoms, as well as textual reports of the detected disease types and severity levels. The interface may also provide recommendations for disease management practices, such as chemical treatments or cultural control measures, based on the detected disease types and severity levels.
[00052] By integrating these embodiments, the proposed image processing technique provides a comprehensive and efficient solution for disease detection and classification in oilseed crop leaves. This can facilitate early and targeted interventions to control and manage crop diseases, ultimately contributing to improved crop yield and quality. The followint table illustrates exemplary suggestions.

Crops Diseases Remedies
Sunflower Head rot Carbendazim foliar spray is advised to control the disease.
Soybean Powdery mildew Using sulfur-containing organic fungicides to control diseases.
Mustard White Rust Treating seeds with metalaxyl seed or thiram seed is highly recommended.
Sesame Phyllody Spray Monocrotophos or Dimethoate combined with intercropping of Sesamum + Redgram
Safflower Alternaria blight Controlled by the spray of mancozeb
Peanut Nematode Diseases Decrease existing infestations through fallowing, crop rotation, and soil solarization

[00053] Fig. 2 depicts flow diagram of disclosed method 200 for detecting diseases in leaves of oilseed crops using a deep learning-based image processing technique involves the following steps:
[00054] At step 202, Acquiring High-Resolution Digital Images: The first step in the method involves acquiring high-resolution digital images of oilseed crop leaves. These images provide visual data essential for the analysis and identification of disease presence. The images can be captured using various platforms such as mobile devices, drones, or remote sensing platforms, which allow for efficient and non-destructive data collection in diverse field conditions.
[00055] At step 204, Pre-processing Acquired Images: Once the images are acquired, they undergo pre-processing operations such as resizing, normalization, and augmentation. Resizing ensures that the images are of a consistent size, while normalization adjusts the intensity values of the images to a standard range. Augmentation involves generating additional images by applying various transformations, such as rotation, flipping, and zooming. These operations help prepare the images for analysis by the convolutional neural network (CNN) and improve the network's ability to generalize to new, unseen data.
[00056] At step 206, Inputting Pre-Processed Images into a CNN: The pre-processed images are then input into a convolutional neural network (CNN) for feature extraction and disease identification. The CNN comprises a series of layers, including input, convolutional, pooling, and fully connected layers. It is trained on a dataset of labeled oilseed crop leaf images, which enables it to learn and recognize features indicative of disease presence. Transfer learning techniques can be employed to leverage pre-trained models and adapt them to the specific task of oilseed crop leaf disease detection, resulting in increased training efficiency and improved classification accuracy.
[00057] At step 208, Analyzing CNN Output and Classifying Images: The output of the CNN is analyzed, and the captured oilseed crop leaf images are classified based on the likelihood of disease presence. This classification process allows the system to make informed decisions about the health of the crop and the presence of diseases.
[00058] At step 210, Displaying Classification Results: The results of the classification are displayed through a user interface, which includes the identification of disease presence, type, and severity. The interface also provides recommendations for disease management and mitigation, empowering users to take appropriate action in response to the detected diseases. Furthermore, the user interface may include a geographical information system (GIS) component that displays the spatial distribution of disease presence and severity across an oilseed crop field, enabling targeted and efficient disease management interventions.
[00059] At step 212, Utilizing the Deep Learning-based Image Processing Technique: By following the above steps, the deep learning-based image processing technique enables rapid and accurate detection of diseases in oilseed crop leaves. This allows for timely intervention and treatment, ultimately leading to improved crop yield and quality. The method provides a powerful tool for farmers and agronomists, helping them monitor crop health and take proactive measures to address diseases before they cause significant damage.
[00060] FIG. 3 is a flowchart outlines a procedure for developing a model for identifying diseases in oilseed plants using image processing and machine learning techniques, in accrodance with embodiment of presetnt disclosure. The process involves taking high-quality photographs of healthy and sick plants, using image augmentation to expand the dataset, applying pre-processing techniques to enhance the images, and performing feature extraction to represent the raw data numerically. The models used for classification include pre-trained parameters such as VGG-16, ResNet50, Alex Net, and Dense Net. The process should result in a model that is accurate, efficient, and cost-effective, providing remedies for any plant diseases that are discovered. The passage also includes a diagram that summarizes each stage of the process. This passage outlines the eight phases of the procedure for developing a model to identify diseases in oilseed plants using image processing and machine learning techniques. The phases include collecting images of healthy and sick plants, expanding the dataset using image augmentation, applying pre-processing techniques, feature extraction, training, testing, and validating the data, using pre-trained models for classification, optimizing the model using optimization techniques, and recommending remedies based on the model's results. The accuracy of the model is measured by its efficiency, and if it is better than the previously generated model, the parameter optimization approach must be changed. Finally, the model can be used to categorize and identify diseases in leaves and suggest remedies.
[00061] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
[00062] The term “memory,” as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage such as physical storage media. Furthermore, a single memory may encompass and in a scenario wherein computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
[00063] Throughout the present disclosure, the term ‘server’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks.
[00064] Throughout the present disclosure, the term “network” relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations.
[00065] Throughout the present disclosure, the term “process”* relates to any collection or set of instructions executable by a computer or other digital system so as to configure the computer or the digital system to perform a task that is the intent of the process.
[00066] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.

Claims
I/We Claim:
1. A deep learning-based image processing technique for detecting diseases in leaves of oilseed crops, comprising:
a) an image acquisition system, configured to capture high-resolution digital images of oilseed crop leaves, wherein said images provide visual data for the analysis and identification of disease presence;
b) a pre-processing module, configured to process said captured images by performing operations including but not limited to resizing, normalization, and augmentation, in order to prepare the images for analysis by a convolutional neural network (CNN);
c) a convolutional neural network (CNN), comprising a series of layers including input, convolutional, pooling, and fully connected layers, wherein said CNN is trained on a dataset of labeled oilseed crop leaf images, allowing it to learn and recognize features indicative of disease presence;
d) a classification module, coupled to said CNN, configured to analyze the output of the CNN and classify the captured oilseed crop leaf images based on the likelihood of disease presence;
e) a user interface, configured to display the results of the classification, including the identification of disease presence, type, and severity, as well as recommendations for disease management and mitigation; and
f) wherein said deep learning-based image processing technique enables the rapid and accurate detection of diseases in oilseed crop leaves, allowing for timely intervention and treatment, ultimately leading to improved crop yield and quality.
2. The deep learning-based image processing technique of claim 1, wherein said image acquisition system comprises a mobile device, drone, or remote sensing platform, enabling the efficient and non-destructive collection of oilseed crop leaf images in a variety of field conditions.
3. The deep learning-based image processing technique of claim 1, wherein said CNN is trained using transfer learning techniques, leveraging pre-trained models and adapting them to the specific task of oilseed crop leaf disease detection, resulting in increased training efficiency and improved classification accuracy.
4. The deep learning-based image processing technique of claim 1, wherein said user interface includes a geographical information system (GIS) component, which displays the spatial distribution of disease presence and severity across an oilseed crop field, enabling targeted and efficient disease management interventions.
5. A method for detecting diseases in leaves of oilseed crops using a deep learning-based image processing technique, the method comprising the steps of:
a) acquiring high-resolution digital images of oilseed crop leaves, wherein said images provide visual data for the analysis and identification of disease presence;
b) pre-processing said acquired images by performing operations including but not limited to resizing, normalization, and augmentation, in order to prepare the images for analysis by a convolutional neural network (CNN);
c) inputting the pre-processed images into a convolutional neural network (CNN) comprising a series of layers including input, convolutional, pooling, and fully connected layers, wherein said CNN is trained on a dataset of labeled oilseed crop leaf images, allowing it to learn and recognize features indicative of disease presence;
d) analyzing the output of the CNN and classifying the captured oilseed crop leaf images based on the likelihood of disease presence;
e) displaying the results of the classification, including the identification of disease presence, type, and severity, as well as recommendations for disease management and mitigation through a user interface; and
f) utilizing said deep learning-based image processing technique to enable the rapid and accurate detection of diseases in oilseed crop leaves, allowing for timely intervention and treatment, ultimately leading to improved crop yield and quality.
6. The method of claim 5, wherein said step of acquiring high-resolution digital images of oilseed crop leaves comprises using a mobile device, drone, or remote sensing platform, enabling the efficient and non-destructive collection of oilseed crop leaf images in a variety of field conditions.
7. The method of claim 5, wherein said step of training the CNN involves using transfer learning techniques, leveraging pre-trained models and adapting them to the specific task of oilseed crop leaf disease detection, resulting in increased training efficiency and improved classification accuracy.
8. The method of claim 1, further comprising the step of displaying the spatial distribution of disease presence and severity across an oilseed crop field through a geographical information system (GIS) component within the user interface, enabling targeted and efficient disease management interventions.

Deep Learning-Based Image Processing Technique for Disease Detection in Oilseed Crop Leaves
Abstract
The present invention relates to a deep learning-based image processing technique for rapid and accurate detection of diseases in oilseed crop leaves. The technique utilizes a convolutional neural network (CNN) trained on a large dataset of labeled images of healthy and diseased oilseed crop leaves, representing various diseases and stages of infection. The CNN automatically extracts and learns disease-specific features from the input images, allowing for efficient identification and classification of diseases. This deep learning-based approach offers a non-destructive, real-time, and cost-effective solution for disease detection in oilseed crops, facilitating timely intervention and improved crop management. , C , Claims:Claims
I/We Claim:
1. A deep learning-based image processing technique for detecting diseases in leaves of oilseed crops, comprising:
a) an image acquisition system, configured to capture high-resolution digital images of oilseed crop leaves, wherein said images provide visual data for the analysis and identification of disease presence;
b) a pre-processing module, configured to process said captured images by performing operations including but not limited to resizing, normalization, and augmentation, in order to prepare the images for analysis by a convolutional neural network (CNN);
c) a convolutional neural network (CNN), comprising a series of layers including input, convolutional, pooling, and fully connected layers, wherein said CNN is trained on a dataset of labeled oilseed crop leaf images, allowing it to learn and recognize features indicative of disease presence;
d) a classification module, coupled to said CNN, configured to analyze the output of the CNN and classify the captured oilseed crop leaf images based on the likelihood of disease presence;
e) a user interface, configured to display the results of the classification, including the identification of disease presence, type, and severity, as well as recommendations for disease management and mitigation; and
f) wherein said deep learning-based image processing technique enables the rapid and accurate detection of diseases in oilseed crop leaves, allowing for timely intervention and treatment, ultimately leading to improved crop yield and quality.
2. The deep learning-based image processing technique of claim 1, wherein said image acquisition system comprises a mobile device, drone, or remote sensing platform, enabling the efficient and non-destructive collection of oilseed crop leaf images in a variety of field conditions.
3. The deep learning-based image processing technique of claim 1, wherein said CNN is trained using transfer learning techniques, leveraging pre-trained models and adapting them to the specific task of oilseed crop leaf disease detection, resulting in increased training efficiency and improved classification accuracy.
4. The deep learning-based image processing technique of claim 1, wherein said user interface includes a geographical information system (GIS) component, which displays the spatial distribution of disease presence and severity across an oilseed crop field, enabling targeted and efficient disease management interventions.
5. A method for detecting diseases in leaves of oilseed crops using a deep learning-based image processing technique, the method comprising the steps of:
a) acquiring high-resolution digital images of oilseed crop leaves, wherein said images provide visual data for the analysis and identification of disease presence;
b) pre-processing said acquired images by performing operations including but not limited to resizing, normalization, and augmentation, in order to prepare the images for analysis by a convolutional neural network (CNN);
c) inputting the pre-processed images into a convolutional neural network (CNN) comprising a series of layers including input, convolutional, pooling, and fully connected layers, wherein said CNN is trained on a dataset of labeled oilseed crop leaf images, allowing it to learn and recognize features indicative of disease presence;
d) analyzing the output of the CNN and classifying the captured oilseed crop leaf images based on the likelihood of disease presence;
e) displaying the results of the classification, including the identification of disease presence, type, and severity, as well as recommendations for disease management and mitigation through a user interface; and
f) utilizing said deep learning-based image processing technique to enable the rapid and accurate detection of diseases in oilseed crop leaves, allowing for timely intervention and treatment, ultimately leading to improved crop yield and quality.
6. The method of claim 5, wherein said step of acquiring high-resolution digital images of oilseed crop leaves comprises using a mobile device, drone, or remote sensing platform, enabling the efficient and non-destructive collection of oilseed crop leaf images in a variety of field conditions.
7. The method of claim 5, wherein said step of training the CNN involves using transfer learning techniques, leveraging pre-trained models and adapting them to the specific task of oilseed crop leaf disease detection, resulting in increased training efficiency and improved classification accuracy.
8. The method of claim 1, further comprising the step of displaying the spatial distribution of disease presence and severity across an oilseed crop field through a geographical information system (GIS) component within the user interface, enabling targeted and efficient disease management interventions.