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Conference Papers Year : 2023

Pairwise Feature Learning for Unseen Plant Disease Recognition

Abstract

With the advent of Deep Learning, people have begun to use it with computer vision approaches to identify plant diseases on a large scale targeting multiple crops and diseases. However, this requires a large amount of plant disease data, which is often not readily available, and the cost of acquiring disease images is high. Thus, developing a generalized model for recognizing unseen classes is very important and remains a major challenge to date. Existing methods solve the problem with general supervised recognition tasks based on the seen composition of the crop and the disease. However, ignoring the composition of unseen classes during model training can lead to a reduction in model generalisation. Therefore, in this work, we propose a new approach that leverages the visual features of crop and disease from the seen composition, using them to learn the features of unseen crop-disease composition classes. We show that our proposed method can improve the classification performance of these unseen classes and outperform the state-of-the-art in the identification of multiple crop-diseases.

Dates and versions

hal-04214623 , version 1 (09-01-2024)

Identifiers

Cite

Abel Yu Hao Chai, Sue Han Lee, Fei Siang Tay, Yi Lung Then, Hervé Goëau, et al.. Pairwise Feature Learning for Unseen Plant Disease Recognition. ICIP 2023 - 30th IEEE International Conference on Image Processing, Thie Institute of Electrical and Electronics Engineers; Signal Processing Society, Oct 2023, Kuala Lumpur, Malaysia. pp.306-310, ⟨10.1109/ICIP49359.2023.10222401⟩. ⟨hal-04214623⟩
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