Explainable AI in agriculture: review of applications, methodologies, and future directions

Document Type

Article

Publication Title

Engineering Research Express

Abstract

Agriculture forms the backbone of the global economy, facing mounting pressure from population growth and resource constraints. The sector increasingly relies on data-driven technologies to enhance productivity while reducing environmental impact. Agriculture is being revolutionized by Artificial Intelligence (AI), which is enhancing pesticide application, weed control, and irrigation management. Deep Learning techniques that have demonstrated predictive power include Generative Adversarial Networks, Recurrent Neural Networks, and Convolutional Neural Networks. Their opacity and intricacy, however, make practical use difficult. In agricultural settings, Explainable AI (XAI) enables informed decisions by providing transparency without compromising performance. This comprehensive review analyzes peer- reviewed publications from 2020 onwards, categorizing XAI techniques and their applications in agriculture. The starting point of 2020 was deliberately chosen to capture the most recent advancements, as this period marks a phase of rapid growth and wider adoption of XAI within agricultural AI applications, making it particularly relevant for reflecting state-of-the-art developments. This review identifies significant challenges, current research trends, methodological approaches, and evaluate the efficacy of various explainability methods, including LIME, SHAP, Grad-CAM, and rule-based models. The analysis examines key domains including crop-weed discrimination, plant disease detection, precision farming techniques, yield forecasting, and soil quality assessment. The integration of XAI methodologies in precision agriculture presents promising opportunities to address pressing challenges related to resource optimization, climate adaptation, and global food security. This review also provides a structured framework for future research directions and practical implementation guidelines to enhance the interpretability, trustworthiness, and adoption of AI-powered agricultural systems among farmers, agronomists, and policymakers.

DOI

10.1088/2631-8695/ae086a

Publication Date

9-30-2025

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