Deep Learning Convolution Neural Network to Detect and Classify Tomato Plant Leaf Diseases

The tomato crop is an important staple in the ？market and it is one of the most common crops daily ？consumed. Plant or crop diseases cause reduction of quality and quantity of the production; therefore detection and classification of these diseases are very necessary. There are many types of diseases that infect ？tomato plant like (bacterial spot, late blight, sartorial leaf ？spot, tomato mosaic and yellow curved). Early detection of plant diseases increases production and improves its quality. Currently, intelligent ？approaches have been widely used to detect and classify these ？diseases. This approach helps the farmers to identify the types？ of diseases that infect crop. The main object of the current work is ？to apply a modern technique to identify and classify the ？disease. Intelligent technique is based on using convolution ？neural network (CNN) which is a part of machine learning to ？obtain an early detection about the situation of plants. CNN ？method depends on feature extraction (such as color, leaves ？edge, etc.) from input image and on this basis the decision of ？classification is done. A Matlab m-file has been used to build ？the CNN structure. A dataset obtained from plant village has ？been used for training the network (CNN). The suggested ？neural network has been applied to classify six types of tomato ？leaves situation (one healthy and five types of leave plant ？diseases). The results show that the convolution neural ？network (CNN) has achieved a classification accuracy ？？of 96.43%. Real images are used to validate the ability of ？suggested CNN technique for detection and classification, and obtained using a 5-megapixel camera from a real ？farm because most common diseases which infect the planet are similar.