Automatic cassava disease recognition using object segmentation and progressive learning

Chang Che

doi:10.7717/peerj-cs.2721

Automatic cassava disease recognition using object segmentation and progressive learning

2025-03-18

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DOI：10.7717/peerj-cs.2721

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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.

Keywords:

Cassava disease recognition

Progressive learning

Object segmentation

Deep learning

Computation efficiency

Automatic cassava disease recognition using object segmentation and progressive learning

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