Tomato Plant Diseases Detection System Using Image Processing
Abstract- In the agriculture sector, one of the major problems in the plants is its diseases. The plant diseases can be caused by various factors such as viruses, bacteria, fungus etc. Most of the farmers are unaware of such diseases. That’s why the detection of various diseases of plants is very essential to prevent the damages that it can make to the plants itself as well as to the farmers and the whole agriculture ecosystem. Regarding this practical issues, this research aimed to classify and detect the plant’s diseases automatically especially for the tomato plant. As per the hardware requirement, Raspberry Pi is the major computing unit. Image processing is the key process of the project which includes image acquisition, adjusting image ROI, feature extraction and convolution neural network (CNN) based classification. Here, Python programming language, OPENCV library is used to manipulate raw input image. To train on CNN architecture and creating a machine learning model that can predict the type of diseases, image data is collected from the authenticated online source. As the result, few diseases that usually occurs in tomato plants such as Late blight (training 100, test 21), Gray spot (training 95, test 18) and bacterial canker (training 90, test 21) are detected.
Keywords: Convolution Neural Network (CNN), Image Processing, Raspberry-Pi, YOLO