A Review on Image-Based Methods for Plant Disease Identification in Diverse Data Conditions

Abstract

Image-based plant disease identification methods have demonstrated potential in enhancing crop protection through early detection. However, the development of this field faces several challenges, such as the scarcity of high-quality annotated data, significant intra-class variation and high inter-class similarity among plant diseases, and the limited generalization ability of current models under diverse domain conditions. We extensively investigated 110+ latest papers on plant disease identification, aiming to present a timely and comprehensive overview of the most recent advances in the field, along with impartial comparisons of strengths and weaknesses of the existing works. Specifically, we begin by reviewing traditional machine learning and deep learning methods, which form the foundation for many current models. We then introduce a taxonomy of transfer learning methods, including instance-based, mapping-based, and network-based methods, and analyze their effectiveness in enhancing classification performance by leveraging prior knowledge under data-constrained scenarios. Subsequently, we examine recent advances in few-shot learning methods for plant disease identification, categorizing them into model-based, metric-based, and optimization-based methods, and evaluate their capabilities in addressing data scarcity and improving identification accuracy. Finally, we summarize the current limitations and outline promising future research directions, with the aim of guiding continued development in this area.