Empowering Agriculture: A Soil Recommendation Model For Rice Cultivation Using Explainable AI

Abstract

Explainable artificial intelligence (XAI) is a set of techniques and procedures that makes it possible for individuals to comprehend and trust the results generated by machine learning algorithms. Explainable AI explains AI models, their effects, and possible biases to guarantee results in AI-powered decision making that are accurate, equitable, transparent, and fair. Explainable AI systems help with soil recommendation and foster trust between the cultivation and the system when distributors are aware of how Artificial Intelligence (AI) recommenders support farmer crops. Explainable AI is a collection of goods and services that integrates machine learning models to assist people understand the connections between soil and agriculture, make sense of forecasts, and improve model performance. The study presents a model for recommending soil for rice cultivation using Explainable Artificial Intelligence.