Che Chang, Xue Nian, Li Zhen, Zhao Yilin, Huang Xin
Electronic and Information Engineering, School of Civil Engineering, Harbin University, Harbin, Heilongjiang, China.
Heilongjiang Urban Water Quality Monitoring Co, Ltd, Harbin, Heilongjiang, China.
PeerJ Comput Sci. 2025 Mar 18;11:e2721. doi: 10.7717/peerj-cs.2721. eCollection 2025.
Cassava is a vital crop for millions of farmers worldwide, but its cultivation is threatened by various destructive diseases. Current detection methods for cassava diseases are costly, time-consuming, and often limited to controlled environments, making them unsuitable for large-scale agricultural use. This study aims to develop a deep learning framework that enables early, accurate, and efficient detection of cassava diseases in real-world conditions. We propose a self-supervised object segmentation technique, combined with a progressive learning algorithm (PLA) that incorporates both triplet loss and classification loss to learn robust feature embeddings. Our approach achieves superior performance on the Cassava Leaf Disease Classification (CLDC) dataset from the Kaggle competition, with an accuracy of 91.43%, outperforming all other participants. The proposed method offers a practical and efficient solution for cassava disease detection, demonstrating the potential for large-scale, real-world application in agriculture.
木薯是全球数百万农民的重要作物，但其种植受到各种毁灭性病害的威胁。目前木薯病害的检测方法成本高昂、耗时且通常局限于受控环境，不适用于大规模农业应用。本研究旨在开发一种深度学习框架，能够在实际条件下早期、准确且高效地检测木薯病害。我们提出了一种自监督目标分割技术，并结合一种渐进学习算法（PLA），该算法同时纳入三元组损失和分类损失以学习鲁棒的特征嵌入。我们的方法在Kaggle竞赛的木薯叶病分类（CLDC）数据集上取得了卓越的性能，准确率达到91.43%，优于所有其他参与者。所提出的方法为木薯病害检测提供了一种实用且高效的解决方案，展示了在农业中大规模实际应用的潜力。
