Machine learning techniques for crop identification and classification

This chapter presents a comprehensive framework for enhancing deep learning-based crop classification by addressing spatial nonstationarity, limited training samples, and the challenge of model explainability. The proposed approach integrates reinforcement learning (RL)-based back projection, open set classification, graph-based strategies, denoising techniques, and ante hoc Explainability into an end-to-end trainable architecture. The framework employs a variational autoencoder for unsupervised denoising, improving the robustness of feature representations from noisy spectral and time-series data. A graph-based strategy is utilized to transform multimodal data into a graph structure, enabling efficient processing through techniques like K-means clustering and locality-sensitive hashing (LSH). Graph neural networks (GNNs) are employed for classification, incorporating dynamic time warping (DTW) for aligning temporal patterns and adaptive kernels for enhanced spatial feature extraction. The RL component iteratively refines classification decisions based on feedback, while a Shapley value-based regularization framework promotes model explainability by focusing on significant features. The overall loss function integrates multiple components to optimize performance, and Bayesian optimization is used for hyperparameter tuning. This integrated approach aims to improve model accuracy, sample efficiency, and interpretability, ultimately contributing to more reliable crop classification in variable agricultural environments. Experiments on various datasets indicate that the proposed approaches give better results than the state-of-the-art approaches in terms of classification accuracy, explainability, and computational performance.