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The review aims to explore the integration of explainable AI in sustainable agriculture to improve decision-making and transparency.

Leveraging explainable AI for sustainable agriculture: a comprehensive review of recent advances

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The review aims to explore the integration of explainable AI in sustainable agriculture to improve decision-making and transparency.
Reviewed articles from various databases including Scopus and IEEE Xplore published between 2017 and 2024.
Examined the application of XAI methods in agriculture, particularly for identifying plant diseases.
Analyzed advancements in machine learning and deep learning relevant to agricultural practices.
Identified the primary uses of XAI in agriculture, such as improving the transparency of AI models.
Outlined challenges faced by researchers in implementing XAI methodologies.
Proposed a modern explainable model specifically for identifying plant diseases.

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This review demonstrates recent advancements in explainable AI for enhancing agricultural productivity, suggesting its critical necessity.

Rajbongshi et al. (2026) studied this question.

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This review demonstrates recent advancements in explainable AI for enhancing agricultural productivity, suggesting its critical necessity.