Few-Shot Learning for Efficient and Accurate Crop Disease Detection in Agriculture

Abstract

Crop diseases pose a significant threat to agricultural productivity, necessitating rapid and accurate detection methods. This project explores the application of N-shot/Few-shot learning techniques for crop disease classification and detection. The conventional challenge in agricultural datasets is the scarcity of labeled examples for various disease classes. Leveraging the principles of N-shot learning, the project aims to develop a model capable of accurately identifying and categorizing crop diseases with only a limited number of labeled instances per class.

The project involves the implementation of a machine learning model that learns to generalize from a small dataset, thus overcoming the limitations of insufficient labeled samples. By adopting N-shot learning, the system becomes adept at recognizing patterns and features associated with different crop diseases, enabling it to make reliable predictions even in scenarios with sparse labeled data. The proposed solution holds promise for resource-constrained environments where obtaining extensive labeled datasets is challenging.

Through the development and evaluation of the N-shot/Few-shot learning model, this project contributes to the advancement of efficient and cost-effective crop disease detection methods. The outcomes are expected to have implications for sustainable agriculture by providing farmers with a reliable tool for early disease identification, thereby aiding in timely and targeted interventions to safeguard crop yield and quality.

Index terms

Crop diseases, Agricultural productivity, N-shot learning, Few-shot learning, Crop disease classification, Disease detection methods, labeled datasets, Machine learning models, Generalization, Pattern recognition, Feature extraction, Sparse labeled data, Resource, constrained environments, Efficient detection methods, Cost-effective solutions, Early disease identification, Timely interventions, Sustainable agriculture, Crop yield, Crop quality.