Description:Field of Invention
This Invention leverages advancements in Mobile, Cloud, and AI technologies to provide a comprehensive crop diagnosis solution that replicates the expertise of plant pathologists and delivers it to farmers. It facilitates a collaborative methodology for the ongoing enhancement of the illness database and the procurement of expert counsel when necessary to augment NN classification precision and outbreak monitoring.
Objectives of the Invention
Agriculture plays a Agriculture is essential for the sustenance and development of communities, particularly in heavily populated emerging countries such as India. Given the continually increasing population, maintaining a consistent and high-quality food supply is crucial for public health. Nevertheless, agricultural output is frequently undermined by numerous variables, especially plant diseases. If undiagnosed or untreated, these illnesses can result in substantial declines in both the number and quality of crops, fruits, and vegetables. Timely identification of these illnesses is crucial for averting extensive infection; yet, numerous farmers are deficient in the necessary instruments and resources for prompt diagnosis, leading to reduced crop yields and quality. In India, the extensive agricultural land, combined with low literacy rates among farmers and restricted access to specialized plant pathologists, constitutes a substantial obstacle to successful disease management. Human-assisted diagnosis frequently fails to satisfy the substantial requirements of extensive agriculture, particularly in distant regions where resources are limited. Consequently, numerous farmers are incapable of mitigating plant diseases until it is too late, resulting in irreparable harm to crops. The deficiency in knowledge and absence of prompt intervention intensify food security issues, necessitating the implementation of more effective, accessible, and scalable solutions for disease detection and management in agriculture. criteria.
Background of the Invention
In (US11564357B2), To circumvent the limitations of disease detection through human assistance, there is a need to design automated systems that utilize technology to provide affordable, accurate, and easily accessible plant disease diagnosis for farmers. Automation can alleviate the shortage of qualified plant pathologists and overcome the challenges posed by poor education and limited access to knowledge. By applying technological innovations, such as robotics, machine learning, and image processing, farmers are able to detect and solve issues early enough, thus enhancing agricultural production and quality.
Many technologies have improved agriculture, including the development of robotics and computer vision systems to combat many kinds of agricultural problems. Such technologies have proven to be effective in precision agriculture, weed management, monitoring plant growth, and nutrient management. Image processing has been applied to improve crop monitoring by analyzing plant photos that may reveal the presence of nutrient deficiencies or infestations of weeds. Despite all these innovations, plant disease diagnosis automation remains in its infancy is described in (US11432469B2),
In (US11116145B2), Plant diseases are largely diagnosed through visual checks by expert pathologists who note physical signs like color changes, wilting, lesions, and spots, along with weather and soil conditions. While many diseases are physically recognizable, their automation for diagnosis has yet to achieve an equivalent level of complexity compared to other farm applications. Agricultural technology investment is relatively low compared to more lucrative fields like education and healthcare, thus limiting the development and widespread adoption of automated disease detection systems.
In addition, several challenges prevent the availability of plant disease detection for farmers. These include high implementation costs, the complexity of linking farmers and plant pathologists, and scalability. Successful research initiatives in this area have often been unable to reach the point of economic viability, largely because of these challenges. Therefore, while technological advancements hold promise, there are significant efforts needed to ensure that these systems are cost-effective, scalable, and accessible to farmers, particularly in distant or unadvanced areas are detailed in (US10255387B2).
In (US10185790B2), Current technology, including robotics and image processing, are developing but remain under-exploited in disease diagnosis. Plant disease diagnosis relies heavily on visual examination, which is cumbersome and inefficient. Investment in agri-technology is relatively low compared to sectors like healthcare and education. Lack of understanding, elevated implementation costs, and scalability challenges are issues. There is a global solution with recent advances in mobile, cloud, and AI technology. India and other developing countries have lots of internet-enabled phones. A lot of low-cost GPS-enabled phones can provide geolocated pictures. They can communicate with complex cloud-based backend systems that store a master database, perform computationally demanding tasks, and process data on widely distributed mobile networks. Newly developed AI-based picture analysis can classify images more accurately than humans. The core AI methods utilize Neural Networks (NN) with visual cortex-like neuron layers. Through "training" on a vast dataset of pre-classified "labeled" images, these networks can accurately categorize new photos. Deep CNNs have ruled computer vision and image processing since the ImageNet victory of 2012 [3]. Fast computers, big visual databases, and deeper neural network methods have furthered CNN. Open-source technologies such as TensorFlow make AI more accurate, cheaper, and available. Relevant literature that is already available includes attempts at gathering images of healthy and diseased crops, image analysis via feature extraction, RGB imaging, spectral pattern, and fluorescence imaging spectroscopy. Neural networks have been used before for plant disease detection, with a focus on the recognition of textural features.
Summary of the Invention

This invention offers an affordable, automated, and easy-to-use solution to a major problem in agriculture: the accurate, real-time, and early detection of plant diseases. This device employs advanced technologies such as deep Convolutional Neural Networks (CNNs) to deliver correct real-time disease classification through a cloud-based platform. The use of CNNs, in this case the state-of-the-art "Inception" model, enables farmers to receive illness diagnoses through a mobile app, allowing for timely decision-making on disease control strategies. This solution significantly improves earlier solutions by providing instant, accurate disease detection, which is necessary for timely responses that minimize crop losses.
This platform utilizes a social collaborative method that automatically improves disease classification accuracy. The technology expands its cloud-based training set through the ability to accept photos of crops uploaded by farmers, therefore making it easy to retrain the CNN model. This collaborative method ensures the evolution and adaptation of the platform, making it more diagnostic in nature. Furthermore, the presence of geocoded images on the platform facilitates the creation of disease density maps, providing significant information about disease occurrence and enabling farmers to comprehend disease spread in their areas. Incorporation of an expert interface improves the platform, such as the addition of analysis that enables farmers to have the intelligence to make the correct decisions and provide effective control measures against plant diseases.
Detailed Description of the Invention
Education using the concepts of Deep Learning Convolutional Neural Networks (DL-CNN) are trained and tested by running raw images through several layers for feature extraction and learning. This is done in the pursuit of maximizing the training and test process.
Through the use of a kernel or a filter, the convolution layers can extract simple patterns and features of images. This is done through picture analysis. It is possible for the network to obtain the ability of learning complex patterns and obtain non-linearity through the application of a ReLU activation function.
Max pooling is utilized to down sample the image, which ultimately results in the reduction of the image in size without losing the important features of the image. The output of the convolutional and pooling layer is input into a fully connected layer, followed by the SoftMax layer. This continues to the last layer. The SoftMax layer has the function of converting the outputs to probabilistic values. That is, objects in the image are classified as per the chances of their presence within the image.
Figure 1 depicts the deep learning convolutional neural network (DL-CNN) architecture utilized in the proposed methodology for content-based image retrieval (CBIR) systems. This technique is utilized in an attempt to get images from the internet. Through improving the word picture feature representation, this methodology makes it possible to retrieve information with greater accuracy and performance as opposed to what is attainable through classical methods. This is because the feature representation of word images is strengthened. With the use of the capability of deep learning convolutional neural network in learning and representing delicate features, the method that has been proposed can enhance image retrieval performance.
Because of this, DL-CNN has a better performance with respect to detection and word image retrieval tasks. With the application of this technology, the conventional methods of retrieval have been enhanced significantly, which has helped in achieving a better solution for image-based search. Figure 1 indicates the convolution layer, which is the central layer responsible for preserving pixel connections and feature extraction from a source image.
This layer is illustrated in the figure. Learning image features from very short blocks of reference material is how this objective is achieved. For this mathematical function to work well, it is required to have two independent things: a source picture and geographical coordinates, which are indicated by rows and columns. It can be expressed that the dimensions of an image (RGB source image) and a filter or kernel with the same dimensions as the input image have the formula. The output dimensions which are produced as a result of the convolution process and are attained by the addition of the input image and the filter are referred to as a feature map. Shown in figure 3.2 is the concept of convolution. Input should be an image of size and a filter of size should be taken. As shown in Figure 1, one can create the feature map by multiplying the values of input image with the values of filter. Horticulturists, either amateur or professional, can now regulate diseases by using vibrational and visual effects due to this electronic hardware. There are improvements in the specification of precision agricultural computer vision and plant security surveillance.
Color analysis and segmentation were both employed in the process of determining the nature of plant diseases. Artificial neural networks handle most of the word infestations of criminal investigations. They use many different picture pre-processing techniques in a bid to have a greater number of attributes. The cerebrum has an enormous number of neurons whose close relationships exist among themselves, and therefore they are capable of handling problems. Human beings maintain the upkeep of the generation of nerve cells, which have more than one input but only a single cell output. The nerve cells are irrevocably engaged in bearing the loads of general tendency with each piece of data. The design which had to be designed was made up of modules that could be stacked.
It is the Input Layer that is responsible for passing the raw image input in width, height, and depth dimensions of 32 pixels. The Convolution Layer is responsible for detecting a small object that is between every set of channels. This is subsequently corrected so that volume of the result can be calculated. Consider, for instance, a sheet that has twelve channels and a yield volume of 32 channels by 32 channels by 12.
Does component-wise convolution at the beginning of the Actuation Function Layer when the layer has been initialized. Tanh, RELU activation of max (0, x), Sigmoid: 1/(1+e^-x), and the Leaky RELU activation are all located very close to one another. The pool layer is in charge of reducing a picture to certain aspects, either 5x5 or 2x2. The lattice of aspects can be one of the two sizes. Each layer is connected to the ones preceding it. It is important to design a platform that is not only user-friendly but also supports many languages and can be widely adopted by farmers who have a variety of technological skills. This will maximize the usage of the platform and put a lot of pressure. Encouraging sustainable agriculture through making factual recommendations, reducing the application of chemical inputs, and minimizing the impact on the environment is an imperative step which should be guaranteed. Improving the Precision in Disease Diagnosis: Deep learning models should be continually improved by adding a huge amount of plant species and an extensive list of environmental parameters.
This will allow for increased diagnostic accuracy and a reduction in the number of false positives and false negatives. In order to facilitate the monitoring of diseases in crops, provide for the following: Create disease detection algorithms that are tailored to the characteristics of particular types of crops. This would allow farmers to identify diseases infecting various plant species with even greater precision and equip them with disease control concepts tailored to their individual requirements. Foster Joint Work and Information Sharing: Within the system context, it also becomes essential to establish community-based platforms that will facilitate farmers, specialists, and scientists to communicate with one another, share information regarding diseases, and derive lessons from peers' experiences. It is critical to ensure the platform is scalable and adaptable for a wide variety of farm sizes, ranging from smallholder to large-scale commercial farming estates, in an attempt to increase scalability and access in rural regions. This needs to be done in an attempt to improve accessibility.
Brief description of Drawing
In the figure which are illustrate exemplary embodiments of the invention.
Figure 1. Plant diseases yields and productivity
Figure 2 Similar results are found in plant disease detection , Claims:The scope of the invention is defined by the following claims:

Claim:
1. A system/method for accurate plant disease detection and precision crop management through a mobile or web-based application, enabling real-time disease identification, outbreak forecasting, and optimized resource utilization, said system/method comprising the steps of:
a. The system starts up, and plant images are captured using a mobile device or imaging hardware (1). The application interface (2) allows users to submit images for analysis by artificial intelligence and computer vision algorithms (3), which identify plant diseases with an accuracy of up to 86.32% (4).
b. Upon disease detection, the system sends real-time alerts (5) and recommends specific treatment actions. The system further integrates meteorological data (6) and AI-based models (7) to forecast disease outbreaks up to two weeks in advance (8), delivering early warnings to enable timely preventive measures.
c. The system applies real-time data analytics (9) to recommend optimal usage of pesticides and fertilizers (10), helping farmers reduce input costs by up to 18.35% (11). The application includes a user-friendly interface (12) that requires no technical expertise (13), allowing easy use by farmers of all skill levels.
2. As mentioned in Claim 1, the system utilizes artificial intelligence and computer vision technology to detect plant diseases with an accuracy of up to 86.32%, thereby enhancing diagnostic reliability and reducing human error.

3. According to Claim 1, the system provides early warnings of disease outbreaks by analyzing meteorological data and predictive AI models, allowing farmers to act preventively up to two weeks in advance.
4. According to Claim 1, the system offers real-time alerts and actionable recommendations upon detecting disease or pest threats, improving response time and reducing crop loss.
5. According to Claim 1, the platform facilitates precision agriculture through optimized pesticide and fertilizer usage, reducing input costs by up to 18.35% without compromising crop health.