DExNet: Combining Observations of Domain Adapted Critics for Leaf Disease Classification with Limited Data

• URL: http://arxiv.org/abs/2506.18173v1
• Date: Sun, 22 Jun 2025 21:15:54 GMT
• Title: DExNet: Combining Observations of Domain Adapted Critics for Leaf Disease Classification with Limited Data
• Authors: Sabbir Ahmed, Md. Bakhtiar Hasan, Tasnim Ahmed, Md. Hasanul Kabir,
• Abstract summary: This work proposes a few-shot learning framework, Domain-adapted Expert Network (DExNet), for plant disease classification.<n>It starts with extracting the feature embeddings as 'observations' from nine 'critics' that are state-of-the-art pre-trained CNN-based architectures.<n>The proposed pipeline is evaluated on the 10 classes of tomato leaf images from the PlantVillage dataset.
• Score: 1.124958340749622
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While deep learning-based architectures have been widely used for correctly detecting and classifying plant diseases, they require large-scale datasets to learn generalized features and achieve state-of-the-art performance. This poses a challenge for such models to obtain satisfactory performance in classifying leaf diseases with limited samples. This work proposes a few-shot learning framework, Domain-adapted Expert Network (DExNet), for plant disease classification that compensates for the lack of sufficient training data by combining observations of a number of expert critics. It starts with extracting the feature embeddings as 'observations' from nine 'critics' that are state-of-the-art pre-trained CNN-based architectures. These critics are 'domain adapted' using a publicly available leaf disease dataset having no overlapping classes with the specific downstream task of interest. The observations are then passed to the 'Feature Fusion Block' and finally to a classifier network consisting of Bi-LSTM layers. The proposed pipeline is evaluated on the 10 classes of tomato leaf images from the PlantVillage dataset, achieving promising accuracies of 89.06%, 92.46%, and 94.07%, respectively, for 5-shot, 10-shot, and 15-shot classification. Furthermore, an accuracy of 98.09+-0.7% has been achieved in 80-shot classification, which is only 1.2% less than state-of-the-art, allowing a 94.5% reduction in the training data requirement. The proposed pipeline also outperforms existing works on leaf disease classification with limited data in both laboratory and real-life conditions in single-domain, mixed-domain, and cross-domain scenarios.

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