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Crop diseases constitute a danger to worldwide food stability, diminishing harvest yield, grade, and total farming efficiency. Conventionally, the identification of plant ailments has depended on lengthy visual assessments by specialists, a method that frequently results in mistakes. Machine learning (ML) and artificial intelligence (AI), in particular Vision Transformers (ViTs) and Convolutional Neural Networks, deliver a swifter, automated substitute for recognizing plant illnesses through leaf image analysis. However, these frameworks are regularly faulted for their "black box" character, which curbs confidence in their outputs due to a lack of clarity. Our outcomes indicate that integrating Explainable AI (XAI) approaches like Grad-CAM, Integrated Gradients, and LIME notably boosts model explainability, rendering it simpler for users to detect the fundamental indicators of plant sicknesses. This investigation not only adds to the area of plant disease recognition but also supplies an original viewpoint on advancing AI openness in actual farming settings through the application of XAI methods. With training accuracy rates of 100.00% for ViT, 96.88% for EfficientNetB7, 93.75% for EfficientNetB0, and 87.50% for ResNet50, and equivalent validation accuracy rates of 96.39% for ViT, 86.98% for EfficientNetB7, and 82.00% for EfficientNetB0, our suggested architectures exceed earlier studies performed on the identical dataset. This illustrates a considerable leap in model capability while upholding clarity and dependability by way of explainable and trustworthy decision procedures.

Artificial intelligence is increasingly used in food safety systems to support contamination detection, shelf-life prediction, quality classification, inspection prioritisation, and risk scoring. These applications promise faster, more sensitive, and more scalable decision support across complex food supply chains. However, the same predictive complexity that makes AI useful can also make its outputs difficult for food safety professionals, regulators, and operators to interpret. The central problem addressed in this framework article is that AI-supported food safety decisions may become opaque, uncontestable, and weakly aligned with existing structures of regulatory responsibility. When risk scores or alerts cannot be understood, challenged, or traced to relevant evidence, they may undermine rather than strengthen food safety governance. This problem is especially serious because food safety decisions involve direct public health consequences, market consequences, and legal accountability. The objective of this article is to develop an original Explainable AI Framework tailored to food safety decision systems. The framework integrates four interdependent dimensions: risk interpretation, regulatory confidence, operational accountability, and human review. It treats explainability not only as a technical property of models but also as a governance condition for responsible food safety decision-making. The article is based on a conceptual synthesis of 27 peer-reviewed articles published between 2017 and 2025 across food safety, explainable artificial intelligence, algorithmic accountability, risk assessment, and digital food safety systems. It does not present new empirical data. Instead, it constructs a design and governance framework that can guide the development, validation, deployment, and oversight of AI-supported food safety tools. The proposed framework defines four pillars: interpretable risk communication, transparent regulatory evidence, accountable operational responsibility, and contestable human review. Together, these pillars show how food safety AI systems can become not only predictive but also auditable, trustworthy, and practically usable. The article argues that embedding explainability into food safety AI is a governance and trust-building imperative rather than a purely technical enhancement.

Agri-food systems increasingly rely on analytics, artificial intelligence, machine learning, environmental monitoring, and decision-support tools to evaluate sustainability performance. These systems can estimate resource use, model environmental impacts, detect risks, and inform actions across farms, supply chains, markets, and governance settings. As analytics become embedded in sustainability decisions, their outputs increasingly shape how environmental problems are recognised and acted upon. Yet sustainability analytics does not become useful or responsible simply because it produces indicators, predictions, rankings, or dashboards. Environmental metrics may be technically sophisticated while remaining difficult for farmers, firms, auditors, regulators, and communities to interpret or challenge. This creates an explainability problem in which users need to understand not only what an analytic system reports, but also how the result was produced, what assumptions it contains, and what consequences may follow from acting on it. This article develops a conceptual framework for explainable sustainability analytics in agri-food systems. The framework treats explainability as a system-level requirement for linking environmental impact interpretation, risk explanation, responsibility attribution, human review, and accountability safeguards. It argues that sustainability analytics should be evaluated not only by predictive performance or measurement coverage, but also by whether it supports transparent, contestable, and context-sensitive decision-making. The article contributes a structured framework for interpreting environmental impact, explaining risk, attributing responsibility, and supporting accountable review in agri-food decision contexts. It positions explainable sustainability analytics as an approach that connects data, models, indicators, explanation mechanisms, human judgement, and governance safeguards. In doing so, it provides a foundation for sustainability analytics that is more interpretable, trustworthy, and actionable.