In the face of climate change, population growth, and resource scarcity, sustainable agriculture requires intelligent, adaptive, and transparent technologies. Permaculture offers a resilient model rooted in ecological principles, but its integration with digital systems remains limited. To support decision-making in sustainable agriculture, this paper proposes a Cyber-Physical System (CPS) architecture that integrates machine learning with local explainability for intelligent crop recommendation in permaculture contexts. The system processes environmental features such as soil nutrients, temperature, pH, and rainfall to predict suitable crops, while ensuring transparency through local explainability methods. We evaluated three classification models: Decision Tree, Random Forest, and Multi-Layer Perceptron, using a curated dataset of 2,200 labeled crop instances. The Random Forest model achieves the highest accuracy (99.55%) and is selected for further interpretability analysis. Local post-hoc explanations address explainability by highlighting feature-level contributions for each prediction, allowing human users to understand and validate AI-driven suggestions. The system architecture and experimental results do not include real-world CPS deployment; however, they illustrate how explainable AI can support decisions based on ecological principles in smart permaculture. This work contributes to the design of transparent, trustworthy, and domain-aware AI systems for sustainable agriculture.

The emergence of Industry 4.0 has led a significant shift towards the widespread integration of Cyber Physical Systems(CPSs) across diverse industrial domains. Yet, the intricate design and implementation of these systems necessitate adept knowledge and expertise, posing challenges for researchers and engineers. In response, this paper introduces IoT-AID, a Cyber Physical Recommendation System aimed at alleviating these challenges. Leveraging the capabilities of large language models (LLMs) as decision support systems, IoT-AID relies on state-of-the-art techniques such as BERT and Sentence Transformers for semantic understanding and context-aware recommendations. Through a comprehensive exploration and evaluation, this study sheds light on the efficacy and potential of LLM-driven recommendation systems within the realm of CPSs, offering insights crucial for navigating the complexities of Industry 4.0 integration.