Performance Analysis of Different CNN Architecture with Different Optimisers for Plant Disease Classification

In the current scenario, the human population is getting increased exponentially still the cultivation land available for farming remains the same, compelling agriculturists to exhibit new methodologies that increase the yield of crops to feed the growing population. In this regard, maintaining the health of crops is very crucial. So, the early identification of disease in plants is very important to detect the disease and increase the yield. This work explores a computer vision techniques called CNN to identify black rot, measles and blight in grape plants. Three different CNN architectures are compared for this task. The performance of three different CNN architectures are measured with three different optimizers for disease classifying application.