Plant Disease Detection Using Self-Supervised Learning: A Systematic Review

Abstract

Agriculture has a crucial role in both the economy and food supply. However, the frequent occurrence of plant diseases can have a significant negative influence on the production of foods. Timely detection of a disease is important for its effective management, e.g., targeted use of pesticides. Conventional Plant Disease Detection (PDD) methods are manual, slow, tedious, and prone to errors, thereby increasing the risk of significant yield losses. Recently, various data-driven approaches, including Deep Learning and Computer Vision-based approaches, have been explored for PDD. However, the scarcity of large and annotated datasets and the limited scalability of these approaches have prompted researchers to turn to the Self-Supervised Learning (SSL) approach. In this review, we provide the very first conceptual grounding for the SSL approach in PDD. By reviewing a large body of recent related works in the literature, we thoroughly analyse and categorise them into generative, predictive, contrastive and hybrid SSL models. Moreover, this review analyses various recent datasets and performance metrics used in these models. Finally, we explain the research challenges and key research directions aimed at advancing PDD through the SSL approach.

Keywords

• Digital agriculture
• self-supervised learning
• plant disease detection
• machine learning
• deep learning
• image processing