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

arXiv: https://arxiv.org/abs/2506.18173 · HTML: https://mlsanity.com/paper/2506.18173/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 55 references that are in the map.
- First posted on arXiv 22 Jun 2025.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Big Transfer (BiT): General Visual Representation Learning](https://mlsanity.com/paper/1912.11370/) (2019)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?](https://mlsanity.com/paper/2003.11539/) (2020)
- [Task-Agnostic Meta-Learning for Few-shot Learning](https://mlsanity.com/paper/1805.07722/) (2018)
- [An open access repository of images on plant health to enable the development of mobile disease diagnostics](https://mlsanity.com/paper/1511.08060/) (2015)
- [Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification](https://mlsanity.com/paper/2109.02394/) (2021)
- [Two Decades of Bengali Handwritten Digit Recognition: A Survey](https://mlsanity.com/paper/2206.02234/) (2022)
- [Fruit Quality Assessment with Densely Connected Convolutional Neural Network](https://mlsanity.com/paper/2212.04255/) (2022)
- [Rethinking Cooking State Recognition with Vision Transformers](https://mlsanity.com/paper/2212.08586/) (2022)
- [HaSPeR: An Image Repository for Hand Shadow Puppet Recognition](https://mlsanity.com/paper/2408.10360/) (2024)
- [An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification](https://mlsanity.com/paper/2304.06520/) (2023)
- [Embrace Limited and Imperfect Training Datasets: Opportunities and Challenges in Plant Disease Recognition Using Deep Learning](https://mlsanity.com/paper/2305.11533/) (2023)
- [MangoLeafViT: Leveraging Lightweight Vision Transformer with Runtime Augmentation for Efficient Mango Leaf Disease Classification](https://mlsanity.com/paper/2505.23961/) (2025)
- [Performance Analysis of Few-Shot Learning Approaches for Bangla Handwritten Character and Digit Recognition](https://mlsanity.com/paper/2506.00447/) (2025)
- [Multi-Domain Few-Shot Learning and Dataset for Agricultural Applications](https://mlsanity.com/paper/2109.09952/) (2021)
- [Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-Case](https://mlsanity.com/paper/2102.12295/) (2021)

## Built on by (most cited first, 4 of 4)

- [A Domain-Adapted Lightweight Ensemble for Resource-Efficient Few-Shot Plant Disease Classification](https://mlsanity.com/paper/2512.13428/) (2025)
- [LeafLife: An Explainable Deep Learning Framework with Robustness for Grape Leaf Disease Recognition](https://mlsanity.com/paper/2601.03124/) (2026)
- [SpectroFusion-ViT: A Lightweight Transformer for Speech Emotion Recognition Using Harmonic Mel-Chroma Fusion](https://mlsanity.com/paper/2603.00746/) (2026)
- [Few-Shot Learning Pipeline for Monkeypox Skin Disease Classification Using CNN Feature Extractors](https://mlsanity.com/paper/2605.05034/) (2026)

## Abstract (arXiv)

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.