Deep Convolution Neural Network-Based Analysis of Tomato Plant Leaves

Plant diseases that may seriously impact agriculture are often discovered with the naked eye, albeit this can take more time and increase the likelihood of a false positive. This issue may be resolved and the chance of decreased plant output is decreased with early discovery. The aim of this experimental research is to deploy intelligence, which can be effectively used for picture classification utilising numerous convolutional neural network (CNN) manners, to automatically identify tomato plant leaf illnesses more quickly. For better performance measurement, the Visual Geometry Group (VGG) model, which is based on CNN, is employed. To diagnose illnesses, this research concludes to categorise photos using VGG-19 transfer learning architectures with various optimizers. In the experimental comparative research, an accuracy of 97.67% and 87.67% was achieved as training and testing with nadam optimizer.