Multi-Domain Few-Shot Learning and Dataset for Agricultural Applications

TL;DR

This paper introduces a novel Few-Shot Learning approach using Transformers and Mahalanobis distance for classifying pests and plants in agriculture, demonstrating significant performance improvements and providing a new dataset for real-world applications.

Contribution

The paper presents a new FSL architecture combining feature extraction, Transformers, and Mahalanobis distance, along with a new agricultural dataset for few-shot plant and pest classification.

Findings

Achieves up to 14% and 24% performance gains on benchmarks.

Outperforms existing FSL architectures in agricultural classification.

Provides a new real-world agricultural dataset for FSL research.

Abstract

Automatic classification of pests and plants (both healthy and diseased) is of paramount importance in agriculture to improve yield. Conventional deep learning models based on convolutional neural networks require thousands of labeled examples per category. In this work we propose a method to learn from a few samples to automatically classify different pests, plants, and their diseases, using Few-Shot Learning (FSL). We learn a feature extractor to generate embeddings and then update the embeddings using Transformers. Using Mahalanobis distance, a class-covariance-based metric, we then calculate the similarity of the transformed embeddings with the embedding of the image to be classified. Using our proposed architecture, we conduct extensive experiments on multiple datasets showing the effectiveness of our proposed model. We conduct 42 experiments in total to comprehensively analyze the model and it achieves up to 14% and 24% performance gains on few-shot image classification benchmarks on two datasets. We also compile a new FSL dataset containing images of healthy and diseased plants taken in real-world settings. Using our proposed architecture which has been shown to outperform several existing FSL architectures in agriculture, we provide strong baselines on our newly proposed dataset.