  Crop failure owing to pests & diseases are inherent within Indian
agriculture, leading to annual losses of 15 to 25% of productivity, resulting
in a huge economic loss. This research analyzes the performance of various
optimizers for predictive analysis of plant diseases with deep learning
approach. The research uses Convolutional Neural Networks for classification of
farm or plant leaf samples of 3 crops into 15 classes. The various optimizers
used in this research include RMSprop, Adam and AMSgrad. Optimizers Performance
is visualised by plotting the Training and Validation Accuracy and Loss curves,
ROC curves and Confusion Matrix. The best performance is achieved using Adam
optimizer, with the maximum validation accuracy being 98%. This paper focuses
on the research analysis proving that plant diseases can be predicted and
pre-empted using deep learning methodology with the help of satellite, drone
based or mobile based images that result in reducing crop failure and
agricultural losses.
