Proper development and growth of crops had always been a major concern
and challenge in Agriculture. Proper crop development assures good quality of crops
and also bumper harvest. Humans may not always identity all plant diseases accurately
at all stages having an automated system for crop disease identification and detection
can be a great help for a tiller. This thought inspired me to perform the proposed
research work. VGG-16 based learning model achieved an accuracy of 98.74%,
ResNet-50 based transfer achieved an accuracy of 98.84%, and ResNet-50 v2 based
transfer learning model achieved an accuracy of 98.21%.
Keywords: CNN Achitecture, ResNet, ResNet50-v2, Transfer Learning, VGG.
