Deep - Transfer Learning for Multi-Crop Leaf Disease prediction using ResNet and ConvNet

Plant diseases can be categorized based on the nature of their primary causal agents, either infectious or noninfectious. The detection and identification of such plant diseases play a critical role in sustaining agricultural productivity and preventing crop losses. Manual disease tracking is a labor-intensive, knowledge-dependent, and time-consuming task. To address these challenges, Deep-TL (Deep Transfer Learning) techniques are used to capture plant leaf images and compare them with extensive datasets representing various plants. This paper suggests a model that makes use of the capabilities of deep learning algorithms, particularly those trained on large datasets, such as ResNet (Residual Networks) and ConvNet (Convolutional Neural Networks). Through the use of transfer learning strategies, the Deep-TL model gains better accuracy and performance by utilising the knowledge gained and achieved the accuracy of 98.2%.