Abstract
Timely diagnosis of plant disease is important to get better crop yields.
Infected plants can cause significant financial losses to farmers by lowering crop
yields. It is extremely desirable to detect early signs and symptoms of plant diseases in
a nation like India, where agriculture supports the majority of the population. More
accurate and faster plant disease detection might assist in lowering the damage. With
tremendous improvements and advancements in deep learning, the effectiveness and
precision of plant disease detection and identification systems may be improved. The
goal of this study is to discover leaf illnesses found in tomato crops and reduce the
financial losses caused by the diseases. We have implemented transfer learning using a
pre-trained Squeeze Net Model to detect and classify tomato leaf diseases. Our model
can automatically detect 9 types of deadly diseases that are very common in tomato
crops. We have acquired 10 classes (one healthy leaf class and 9 diseased leaf classes)
consisting of 16,012 tomato leaf samples from a benchmarked Plant Village dataset to
train and validate the suggested method. On the public dataset, the class-wise
classification precision rate varies from 77.9% to 99.6%, and the overall classification
accuracy of the suggested model is observed as 93.1% which is a significant
enhancement in performance over previous tomato disease detection techniques.