Cotton leaf disease detection using Image processing

Cotton is the most important cash crop in our country. It is also known as king of fibers among all cash crops. Productivity of cotton plant plays a major role in economy. Farmers struggle a lot for proper production of cotton crops but there are multiple diseases that affect the quality and product

Project Title

Cotton leaf disease detection using Image processing

Project Area of Specialization

Artificial Intelligence

Project Summary

Cotton is the most important cash crop in our country. It is also known as king of fibers among all cash crops. Productivity of cotton plant plays a major role in economy. Farmers struggle a lot for proper production of cotton crops but there are multiple diseases that affect the quality and productivity of plants. If the disease is not diagnosed on time, the quality and productivity of cotton is immensely affected. Usage of pesticides is required that is harmful for plants and exhibit negative impacts on the human environment too. Therefore, disease detection and classification are crucial in preventing the losses in the yield and quantity of the product. In our project, we are going to make a system which will detect and classify diseases of cotton leaves. We are using image processing techniques for the disease detection of the cotton plant leaf by examining the spots of the leaf. Furthermore, we are going to use a neural network that allows us to differentiate between healthy and unhealthy cotton plant leaves and classify the disease of the plant.

Image of the leaf of cotton plants is captured with the help of a camera and is fed to the computer that acts as the central processor. The image is processed in different color space representations and is segmented by the processor. The decision regarding the health of the cotton plant can be made by looking at the spots present on its leaves. This can be done by examining those spots over the leaf and quantify the area affected by the disease. The classifier is developed for the further identification of the disease in the cotton plants.

Project Objectives

• Collection of datasets from different regions,
• To acquire the data from the cotton leaf
• Segment the acquired data to obtain the useful segments.
• To compute the texture features of the leaf
• To identify the spots over the leaf
• To differentiate between healthy and unhealthy cotton plant leaves
• To classify the disease of the cotton leaf

Project Implementation Method

Our project is divided into two main phases. The first phase is the detection of cotton leaf disease that is carried out using image processing and the second is the classification of the disease that is done by the development of a neural network.

For the cotton leaf disease detection, the image of the cotton leaf is captured from the environment with the help of a camera. Image processing techniques are applied for the detection of the disease. Image preprocessing is carried out in which the color space conversion takes place to change chromaticity and intensity of the of RGB images to make color space representation. Segmentation is done where the multiple segments of the image are created, allowing the images to be represented into some portion that is easier to analyze. Also, the unnecessary part of the image is removed by means of the segmentation and the spot on the cotton leaf can easily be detected. Feature extraction is further applied to extract the diseased part of the leaf.

For the cotton leaf disease classification, we capture multiple images of cotton leaf and create the dataset. Two sets of images are acquired, first set for the training purpose and the second for the testing purpose. Due to different climate conditions in different regions, we must capture the images from different areas. A multi-layer neural network is designed. The diseased part of the leaf is fed to the neural network as its input and the neural network classifier is used for further classification of the disease.

Benefits of the Project

An automatic plant disease detection system monitors large fields and detect large number of cotton diseases. Without proper identification of the disease, disease control measures can be a waste of time and money and can lead to further plant losses. Our project will provide farmers a chance to identify the disease of plant leaves at early stage, so that we can prevent or take a remedy to stop spreading of the disease and reduces the use of different pesticides. It is aimed to increase the productivity of cotton crop and prevent the losses in yield and quantity of crop. Our project differentiates between healthy and unhealthy leaves this is the approach that provides a chance of protecting healthy parts of crop.

Technical Details of Final Deliverable

The final deliverable will comprise of a software algorithm developed on Python, that involves a camera and a processor, mainly a computer to work. The camera used for the data acquisition is Logitech C130 to obtain the training and testing data for the developed algorithm. C310’s RightLightTM 2 feature adjusts to lighting conditions, producing brighter, contrasted images with a max resolution of 720p to acquire the data from different fields in different lightning conditions and environments. The PC is used as the central processor to run the algorithm, where the software-based inspection is done, allowing you to differentiate between a healthy and a diseased leaf and to classify the disease of the cotton plant. The software tools used for the development of the classifier and the algorithm includes Python and the modules used for this programming language includes OpenCV, TensorFlow and NumPy. The ending delivery will be a hardware and software integrated system working together in real-time.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Agriculture

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Decent Work and Economic Growth, Responsible Consumption and Production

Required Resources

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