Abstract:

With the growing population, there comes a great need to provide sufficient necessities for everyone. Here comes the question whether we have enough resources to provide necessities for everyone or not. It shows the importance of increasing agricultural production. There are a lot of reasons for the decrease in Agriculture production, one of the main factors is diseases/pests. Pests/diseases can damage the entire crop in a short time if not detected and diagnosed on time. Detection of crop diseases at an initial stage can help farmers diagnose the disease on time, hence increasing the productivity of the crop. This is possible with the implementation of advanced technologies like Deep Learning (DL) in the field of Agriculture. DL is being used in Agriculture for Crop Recommendation, Precision Agriculture, Disease detection, and Smart Irrigation etc. DL approach, precisely Convolution Neural Network (CNN) can be used to detect the leaf disease more precisely and accurately than humans. The proposed work uses various CNN architectures like AlexNet, MobileNet, ResNet50 and some CNN based models that are built from scratch for the detection and identification of leaf diseases of various crops. Once the classification is done, these architectures will then be compared based on their performance and accuracy. The best model will be chosen for deployment using Django framework to create a web application to make the model more readable and user friendly.

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