Karthik, ModuguriModuguriKarthikNandini, AnkenapalleAnkenapalleNandiniVedha, Pochamreddy VenkataPochamreddy VenkataVedhaLatha, A RaagaA RaagaLathaBalaji, K VK VBalajiSungheetha, AkeyAkeySungheetha2026-02-112026-02-1120259798331507244http://dx.doi.org/10.1109/ICAISS61471.2025.11041903https://www.scopus.com/pages/publications/105010768252https://gnanaganga.alliance.edu.in/handle/123456789/9545In this paper, we presented a crop recommendation system for precision agriculture that is based on the machine learning models providing crop recommendations based on the environmental and soil context. Introducing Explainable AI (XAI) in the designed system using LIME, we hope to enhance the level of trust to its recommendations through the understanding of the process which goes into each of them by the farmers. This has been made possible by the system's architecture that consists of backend in Flask and frontend in React. The system was subjected to a rigorous assessment of its reliability and efficiency once developed to obviate measurement errors and ensure its effectiveness and efficiency. Moreover, the feedback on the integration of XAI also reveals an enhancement of interpretability by demonstrating the explanations of such features as pH of the soil, temperature, and moisture of the soil to have a positive effect to the system. Besides improving the chances of accurate crop yields estimation, this approach enables an accurate assessment of farming inputs with regards to sustainability. © 2025 IEEE.enExplainable Artificial Intelligence (Xai)Lime (Local Interpretable Model-Agnostic Explanations)Conference paper