An In-Depth Exploration of ResNet-50 and Transfer Learning in Plant Disease Diagnosis

Plant diseases represent serious risks to the world’s agricultural sector, resulting in large crop losses and affecting the availability of food. An accurate and timely diagnosis is essential to the successful management of any disease. This study addresses this challenge through the development of a plant disease detection system. The proposed model is built upon the ResNet-50 architecture, a deep learning framework known for its process in image classification tasks. Benefits of ResNet-50 include accurate diagnosis of plant diseases from pictures, even when diseases have similar visual traits. The skip links in the architecture reduce the problems caused by disappearing gradients, making it easier to train on deeper networks and improving accuracy. The dataset employed in this study encompasses color, grayscale, and segmented directories, each housing instances of 38 different plant disease classes Studies using the ResNet-50 model use transfer learning using initially pre-trained weights from a large dataset of generic images. Later refinement of a particular plant disease dataset will improve the model and its performance. This approach accelerates convergence, improves accuracy, and reduces training time, contributing to the efficiency of disease diagnosis. The application of the ResNet-50 model, along with the various directories, provides the model’s adaptability and accuracy across a wide range of plant species. The suggested model accurately and efficiently diagnoses plant diseases by utilizing the advantages of the ResNet-50 algorithm architecture with the deliberate application of transfer learning.