Elucidating Deep Transfer Learning Approach for Early Plant Disease Detection Through Spot and Lesion Analysis

The research field of detecting plant diseases through spot and lesion identification has become a crucial field because it helps minimize agricultural losses and enhances food security. The survey analyzes the latest progress in deep transfer learning methods which assist in detecting plant diseases during their early stages. A review of numerous research papers demonstrates how transfer learning techniques are used to achieve early detection of disease symptoms. The discussion includes significant development such as the application of CNN models ResNet, MobileNet, and EfficientNet alongside techniques like data augmentation and fine-tuning to overcome issues like limited labeled data and computational restrictions. The analysis focuses on commonly utilized data sets in plant disease detection research, which include the widely utilized PlantVillage dataset along with datasets from real-time field datasets. The paper highlights the dataset capabilities in managing data diversity, environmental variations, and scalability, but also represents dataset limitations when applied to real-world situations. A detailed overview discussed existing methodology in early plant disease detection through transfer learning and identifying major research gaps. It identifies critical obstacles, like unavailability of real-time based datasets, dataset imbalances, recommends future research based on development of lightweight models for devices with limited resources utilizing federated learning for the diagnostic process. This research presents essential insights to researchers and field experts to boost precision agriculture using novel AI methods.