Rehearsal-based class-incremental learning approaches for plant disease classification

• Published in: Computers and electronics in agriculture Vol. 224; p. 109211
• Main Authors: Li, Dasen, Yin, Zhendong, Zhao, Yanlong, Li, Jiqing, Zhang, Hongjun
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2024
• Subjects: agriculture; algorithms; Class-incremental learning; crop quality; crop yield; data collection; Deep learning; early diagnosis; electronics; Image classification; leaves; mathematical models; Plant diagnosis; prediction; Rehearsal; Deep learning; Plant diagnosis; Rehearsal; Class-incremental learning; Image classification
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2024.109211

•Class-incremental learning frameworks mitigate the catastrophic forgetting problems of continual plant diagnosis.•Adversarial training significantly improves the generalization of class-incremental classification.•The advantage of adversarial training based class incremental learning is more pronounced with less exemplar storage.•Employing the adversarial training after the conventional training to finetune the parameters is efficient. Early diagnosis of plant disease is of utmost importance to improve the crop yield and the crop quality. In the early stage of disease infestation, characteristics of the plant leaves could be used to indicate the disease category. As a reliable tool for leaf-image classification, deep learning approaches have achieved a wide attention recently. These data-driven approaches could extract the characteristics by the mathematical model from a diseased leaf-image dataset automatically, then output the prediction result which belongs to the dataset label. However, if the model learns a new disease incrementally from future data, it might forget previously learned knowledge. In this paper, we investigate the rehearsal-based class-incremental learning approaches for plant disease classification, in order to mitigate the catastrophic forgetting while learning the knowledge of a new disease. Moreover, we explore the effectiveness of class-incremental learning approaches on images with significantly different distribution. To improve the prediction and generalization performance of these out-of-distribution leaf images captured in complex scenes of the real-world, we propose a new rehearsal-based class-incremental learning approach, which is termed as adversarial class-incremental learning. The approach is based on a proposed adversarial training algorithm, which finetunes the trained parameters by projected gradient descent instead of learning the weights directly by adversarial samples. In the experiment of a class-incremental learning task with 30 categories, the proposed approach achieved a predictive accuracy of 86.8% on the leaves of PlantVillage dataset, and an accuracy of 66.6% on the leaf-image dataset with cluttered backgrounds, outperforming the existing suboptimal method which achieved the accuracies of 83.3% and 57.6%. The satisfactory performance of our proposed method provides an opportunity of continual diagnosis for agricultural application.