Machine learning in nutrient management:A review

作     者：Oumnia Ennaji Leonardus Vergütz Achraf El Allali 

作者机构：Chair of Soil ScienceUniversity Mohammed VI PolytechnicLot 660Hay Moulay RachidBenguerir 43150Morocco African Genome CenterUniversity Mohammed VI PolytechnicLot 660Hay Moulay RachidBenguerir 43150Morocco 

出 版 物：《Artificial Intelligence in Agriculture》 (农业人工智能（英文）)

年 卷 期：2023年第9卷第3期

页      面：1-11页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Fertilization Nutrient management Machine learning 

摘      要：In agriculture,precise fertilization and effective nutrient management are *** 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 *** based systems require huge amounts of data from different platforms to handle non-linear tasks and build predictive models that can improve agricultural *** 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 *** research findings suggest that rapid improvements in machine learning and sensor technology can provide cost-effective and thorough nutrient assessment and decision-making *** research directions are also recommended to improve the practical application of this technology.