Crop Disease Detection and Recommendation of Pesticides and Secondary Crops using Deep Learning

In India, so many people belong to an agricultural background. Nowadays crop disease detection is a very crucial topic in analysis. Crop diseases are one of the major issues for the depletion of the quality and quantity of the crop. So, detecting and giving suitable solutions for crop disease is very important to increase the quality and quantity of the crops. In this research work, we use Deep Learning Algorithms such as Convolutional Neural Networks to train the crop disease dataset and we use image pre-processing techniques to pre-process the image and to detect the crop disease. In this project, we selected the convolution neural network to get better accuracy for the system. Here, crop leaf images that are affected by diseases are given as input to the system, and after pre-processing that image, it will detect whether there is a disease or not for the crop. If a disease is detected then it will give the name of the disease. After, it will show the suitable pesticides and chemicals that reduce the crop diseases for good yield of the crop. In this project, we also suggest to the farmers some alternative crops that provide various benefits such as improve yield, soil fertility, pest management, etc. These alternative crops often have unique characteristics that complement the yield and growth of main crops