Towards Sustainable Farming: A Conceptual Machine Learning-Based Crop Recommendation Model

Abstract

Global food security is largely dependent on agriculture, and enhancing agricultural productivity is essential to meet the growing food demands. One effective way to achieve this is by enabling farmers to select crops that are appropriate to their specific farming conditions. Crop recommendation systems powered by machine learning (ML) are increasingly being developed to support this goal. These systems utilise data-driven approaches to try to give accurate recommendations to farmers regarding crop selection and potential yield. A conceptual model for an ML-based crop recommendation system design for sustainable farming practice is one that incorporates multiple critical parameters such as soil properties (including pH value, soil type, moisture level, and nutrient content) and weather data (such as humidity, temperature, and rainfall). By analysing these factors, various supervised ML algorithms can be applied to predict crops that would increase yield in a given field. This conceptual study focuses on the sustainability in agriculture and also focuses on the advantages of supervised ML techniques in improving crop recommendations, providing farmers with scientific support for decision making. The model also lays a strong foundation for upcoming technological advancement and practical implementations in intelligent agriculture.

Keywords: Agriculture, Crop recommendation system, Machine learning, Sustainable farming.

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