Description:FIELD OF THE INVENTION
This invention relates to computer vision and ai based finger millet system disease detection.
BACKGROUND OF THE INVENTION
Finger millet, a vital staple crop, is extremely receptive to various ailments that can curtail output and its quality. Traditionally, diagnosing diseases in the crop has been conducted manually which takes time and is often inaccurate due to variations in disease characteristics. This has resulted in delayed response leading to substantial losses of crops as well as economic hardships for farmers. Therefore, there is an urgent need for a better solution that can be used to detect diseases and pathogens in finger millet crops efficiently and automatically.
With developments in computer vision and machine learning algorithms, it is now possible to make robust models that can accurately recognize diseases occurring on plants. The objective of this project therefore aims at utilizing these technologies by developing a model with ability to differentiate between seven different classes of finger millet diseases such as bacterial leaf infections among other forms of blast conditions. We also plan on implementing this model on Raspberry Pi which is a cheap platform that farmers can easily access; thus, allowing them have portable real-time disease detection tool.
The suggested innovations will implement a high-definition camera that will take photos of finger millet leaves, while these will be processed through the Raspberry Pi disease detection model. Besides, with the Intel Neural Compute Stick 2 aiding in this process, the system can undertake a faster and precise disease classification. As such, it not only promises to reduce time and labor spent on crop monitoring but also aims at improving plant disease management strategies thus enhancing crop productivity and sustainability.
US11564357B2 In embodiments, acquiring sensor data associated with a plant growing in a field, and analyzing the sensor data to extract one or more phenotypic traits associated with the plant from the sensor data. Indexing the one or more phenotypic traits to one or both of an identifier of the plant or a virtual representation of a part of the plant, and determining one or more plant insights based on the one or more phenotypic traits, wherein the one or more plant insights includes information about one or more of a health, a yield, a planting, a growth, a harvest, a management, a performance, and a state of the plant. One or more of the health, yield, planting, growth, harvest, management, performance, and the state of the plant are included in a plant insights report that is generated.
RESEARCH GAP:
1. Environmental Sustainability Minimizes the need for widespread chemical applications by using target treatments. Supports sustainable agricultural practices through minimizing environmental impacts of disease control measures.
2. Data-Driven Insights Carries out data collection and analysis on diseases’ spread patterns and prevalence.
3. Helps in making informed choices for managing the condition better
US20230292647A1 Disclosed is a method of automated crop monitoring based on the processing and analysis of a large number of high-resolution aerial images that map an area of interest using computer vision and machine learning techniques. The method comprises receiving 120 or retrieving image data containing a plurality of high-resolution images of crops in an area of interest for monitoring, identifying 130 one or more crop features of each crop in each image, determining 140, for each identified crop feature, one or more crop feature attributes, and generating or determining 160 one or more crop monitoring outputs based, at least in part, on the crop features and crop feature attributes. Also disclosed is a method generating field camera specific training data for the machine learning model used to analyse the received image data.
RESEARCH GAP:
1. Improved Crop Yield and Quality: Timely identification and treatment of diseases make crops healthier, increasing yields.
2. Better overall quality of products adds value to farmers as well as buyers.
3. Decreased Labor and Time: Automates the detection of diseases thereby saving time and labor required by farmers. Enhancing crop management systems that are more streamlined and effective.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. This invention relates to computer vision and ai based finger millet disease detection.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
A detailed architecture diagram shows how to create a complete schema for raspberry pi-based finger millet disease detection system. The Raspberry Pi is at the core of this system as a central processing unit which connects all connected peripherals and modules necessary for its seamless operation. In addition, there is an FHD (Full HD) camera (1280x1080p) connected to the Raspberry Pi that captures high-quality images of the finger millet leaves. This camera provides detailed visual data necessary for treatment by neural network models that detect and classify diseases. The connectivity of the neural stick to Raspberry Pi is also responsible for improving the processing power by accelerating deep learning models to ensure that system is able to handle complex computations with high efficiency. In addition, A chart contains a WiFi module which allows Raspberry Pi to access the internet for various activities such as remote monitoring and data storage. With remote monitoring one can get real-time updates on plant health from any location via accessing output devices of control systems. Also, there is a cloud storage integration which guarantees secure storage and accessibility of collected data and analysis results for further scrutiny or assessment. The microcontroller is connected to an LCD screen that presents information in real time about crop status so that users can have direct interactions with the system. It acts as a go-between between other components and the Raspberry Pi handling data flow and peripheral control for smooth running.
Even when in the field, being continuously operational is one of the most important aspects that this system can have and hence power supply management is critical. Portable and reliable energy source for this system is provided by a 12V 3amp lithium polymer battery. A charger is connected to the battery to recharge it when it gets drained which should be plugged into an AC outlet. The power supply management setup includes voltage changing module that regulates power supplied to different components thus ensuring that each device receives appropriate voltage for optimal performance. Moreover, heat sinks are attached to Raspberry Pi and other vital parts for heat dissipation and maintaining optimal operating temperatures which in turn prevents overheating and extends the life of the system.
A keyboard and mouse are also included as peripheral devices through which connection can be established between them and Raspberry Pi thereby making it easy for setup, configuration, and interaction with the system. Such peripherals are especially helpful during the initial set up process as well as in case any problem pops up during operation. The combination of these devices makes a robust versatile unit that detects accurately finger millet diseases so as to enable diagnosis efficiently. The Raspberry Pi and neural stick’s processing power, together with the integration of advanced hardware components, makes this system an applicable and efficient answer to real-time disease detection in agriculture. The accuracy and efficiency of disease detection are improved by this setup while offering flexibility and portability hence making it suitable for deployment in various agricultural settings.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
Figure 1: General Structure of the system
Figure 2: Detailed architecture of the device with power supply
Figure 3: Algorithmic view of the system
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Present invention discloses a Computer vision and AI based Finger millet disease detection system comprises a Raspberry Pi (9) as a central processing unit; an FHD camera (6) connected to the Raspberry Pi for capturing images of finger millet leaves; a neural stick (8) connected to the Raspberry Pi for accelerating deep learning models; a WiFi module (7) connected to the Raspberry Pi for enabling remote monitoring; a microcontroller (10) connected to the Raspberry Pi for managing peripheral control; a power supply management system including a battery (15), charger (16), and voltage-changing module (17); heat sinks (13) attached to the Raspberry Pi and other critical components; and peripheral devices such as a keyboard (11) and mouse(12);
Wherein system also comprising cloud storage for storing and accessing collected data and analysis results; and the neural stick is used to detect and classify finger millet diseases based on the captured images.
In another embodiment, the neural network models are trained on a dataset of finger millet leaf images with corresponding disease labels. The neural network models are capable of detecting and classifying multiple finger millet diseases.
In another embodiment, the neural network models are implemented using a deep learning architecture, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs).
In another embodiment, the WiFi module is configured to transmit real-time disease detection results to a remote monitoring device.
In another embodiment, the cloud storage is configured to store historical data on disease detection results, plant health metrics, and environmental conditions.
In another embodiment, microcontroller is configured to control the operation of additional peripherals, such as sensors for measuring environmental parameters or actuators for implementing disease control measures; and the microcontroller is connected to the LCD screen that presents information in real time about crop status so that users can have direct interactions with the system
In another embodiment, the power supply management system includes a battery management system for monitoring battery charge level and preventing overcharging or deep discharge. The heat sinks are configured to dissipate heat efficiently and maintain the Raspberry Pi and other components within an optimal operating temperature range.
In another embodiment, keyboard and mouse are used for inputting user-defined parameters or configuring the system's settings; and the WiFi model allows microprocessor to access the internet for various activities such as remote monitoring and data storage. The present system utilizes a Raspberry Pi as the central processing unit, coordinating the operation of various connected peripherals. An FHD camera captures high-quality images of finger millet leaves, providing visual data for disease detection. A neural stick accelerates deep learning models, enhancing the system's ability to handle complex computations efficiently.
Connectivity to a WiFi module allows for remote monitoring, enabling users to access real-time updates on plant health from any location. Cloud storage integration ensures secure storage and accessibility of collected data and analysis results. A microcontroller facilitates communication between the Raspberry Pi and other components, managing data flow and peripheral control.
To ensure continuous operation in field conditions, a power supply management system is incorporated. A 12V 3A lithium-polymer battery provides a portable and reliable energy source, while a charger allows for recharging. A voltage-changing module regulates power distribution to different components, ensuring optimal performance. Heat sinks are attached to the Raspberry Pi and other critical parts to dissipate heat and prevent overheating.
Peripheral devices such as a keyboard and mouse enable easy setup, configuration, and interaction with the system. This combination of hardware and software components provides a robust and versatile solution for accurate finger millet disease detection.
In another embodiment the microprocessor is at the core of this system as a central processing unit which connects all connected peripherals and modules necessary for its seamless operation.
In another embodiment The connectivity of the neural stick to microprocessor is also responsible for improving the processing power by accelerating deep learning models to ensure that system is able to handle complex computations with high efficiency.
In another embodiment the WiFi model allows microprocessor to access the internet for various activities such as remote monitoring and data storage.
In another embodiment the microcontroller is connected to the LCD screen that presents information in real time about crop status so that users can have direct interactions with the system.
In another embodiment the cloud storage integration which guarantees secure storage and accessibility of collected data and analysis results for further scrutiny or assessment.
In another embodiment a charger is connected to the battery to recharge it when it gets drained which should be plugged into an AC outlet.
, Claims:1. A Computer vision and AI based Finger millet disease detection system comprises a Raspberry Pi (9) as a central processing unit; an FHD camera (6) connected to the Raspberry Pi for capturing images of finger millet leaves; a neural stick (8) connected to the Raspberry Pi for accelerating deep learning models; a WiFi module (7) connected to the Raspberry Pi for enabling remote monitoring; a microcontroller (10) connected to the Raspberry Pi for managing peripheral control; a power supply management system including a battery (15), charger (16), and voltage-changing module (17); heat sinks (13) attached to the Raspberry Pi and other critical components; and peripheral devices such as a keyboard (11) and mouse(12);
Wherein system also comprising cloud storage for storing and accessing collected data and analysis results; and the neural stick is used to detect and classify finger millet diseases based on the captured images.
2. The system as claimed in claim 1, wherein the neural network models are trained on a dataset of finger millet leaf images with corresponding disease labels.
3. The system as claimed in claim 1, wherein the neural network models are capable of detecting and classifying multiple finger millet diseases.
4. The system as claimed in claim 1, wherein the neural network models are implemented using a deep learning architecture, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs).
5. The system as claimed in claim 1, wherein the WiFi module is configured to transmit real-time disease detection results to a remote monitoring device.
6. The system as claimed in claim 1, wherein the cloud storage is configured to store historical data on disease detection results, plant health metrics, and environmental conditions.
7. The system as claimed in claim 1, wherein the microcontroller is configured to control the operation of additional peripherals, such as sensors for measuring environmental parameters or actuators for implementing disease control measures; and the microcontroller is connected to the LCD screen that presents information in real time about crop status so that users can have direct interactions with the system
8. The system as claimed in claim 1, wherein the power supply management system includes a battery management system for monitoring battery charge level and preventing overcharging or deep discharge; and a charger is connected to the battery to recharge it when it gets drained which should be plugged into an AC outlet
9. The system as claimed in claim 1, wherein the heat sinks are configured to dissipate heat efficiently and maintain the Raspberry Pi and other components within an optimal operating temperature range.
10. The system as claimed in claim 1, wherein the keyboard and mouse are used for inputting user-defined parameters or configuring the system's settings; and the WiFi model allows microprocessor to access the internet for various activities such as remote monitoring and data storage.