外文摘要：BACKGROUNDCoffee farming constitutes a substantial economic resource, representing a source of income for several countries due to the high consumption of coffee worldwide. Precise management of coffee crops involves collecting crop attributes (characteristics of the soil and the plant), mapping, and applying inputs according to the plants' needs. This differentiated management is precision coffee growing and it stands out for its increased yield and sustainability.RESULTSThis research aimed to predict yield in coffee plantations by applying machine learning methodologies to soil and plant attributes. The data were obtained in a field of 54.6 ha during two consecutive seasons, applying varied fertilization rates in accordance with the recommendations of soil attribute maps. Leaf analysis maps also were monitored with the aim of establishing a correlation between input parameters and yield prediction. The machine-learning models obtained from these data predicted coffee yield efficiently. The best model demonstrated predictive fit results with a Pearson correlation of 0.86. Soil chemical attributes did not interfere with the prediction models, indicating that this analysis can be dispensed with when applying these models.CONCLUSIONThese findings have important implications for optimizing coffee management and cultivation, providing valuable insights for producers and researchers interested in maximizing yield using precision agriculture.
外文关键词：precision agriculture;digital farming;biennial yield;coffee growing
作者：Santana, Lucas Santos;de Oliveira Faria, Rafael;Filho, Aldir Carpes Marques;Martins, Murilo Battistuzzi;Sobrinho, Renato Lustosa;Zoz, Tiago;de Oliveira, Bruno Rodrigues;Alwasel, Yasmeen A;Okla, Mohammad K;Abdelgawad, Hamada
作者单位：King Saud Univ;Univ Fed Lavras;Fed Univ Vale Jequitinhonha & Mucuri UFVJM;Mato Grosso State Univ UEMS;Fed Univ Technol Parana UTFPR;Univ Antwerp;Pantanal Editora
期刊名称：JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE
期刊影响因子：0.0
出版年份：2024
出版刊次：104(9)
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台