Machine learning in nutrient management: A review

Abstract

In agriculture, precise fertilization and effective nutrient management are critical. Machine learning (ML) has recently been increasingly used to develop decision support tools for modern agricultural systems, including nutrient management, to improve yields while reducing expenses and environmental impact. ML based systems require huge amounts of data from different platforms to handle non-linear tasks and build predictive models that can improve agricultural productivity. This study reviews machine learning based techniques for estimating fertilizer and nutrient status that have been developed in the last decade. A thorough investigation of detection and classification approaches was conducted, which served as the basis for a detailed assessment of the key challenges that remain to be addressed. The research findings suggest that rapid improvements in machine learning and sensor technology can provide cost-effective and thorough nutrient assessment and decision-making solutions. Future research directions are also recommended to improve the practical application of this technology.