AgriDoc: Classification and Prediction of plant leaf diseases

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Kushal H, K Swathi, Konanki Nithyusha, Lavanya A, Kalneedi srihari, Ravindranath R C

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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