首页|Federal University of Lavras Reports Findings in Machine Learning (Models for pr edicting coffee yield from chemical characteristics of soil and leaves using mac hine learning)
Federal University of Lavras Reports Findings in Machine Learning (Models for pr edicting coffee yield from chemical characteristics of soil and leaves using mac hine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news originating from Lavras,Brazil,by New sRx correspondents,research stated,"Coffee farming constitutes a substantial e conomic resource,representing a source of income for several countries due to t he high consumption of coffee worldwide. Precise management of coffee crops invo lves collecting crop attributes (characteristics of the soil and the plant),map ping,and applying inputs according to the plants' needs." Our news journalists obtained a quote from the research from the Federal Univers ity of Lavras,"This differentiated management is precision coffee growing and i t stands out for its increased yield and sustainability. This research aimed to predict yield in coffee plantations by applying machine learning methodologies t o 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 monito red with the aim of establishing a correlation between input parameters and yiel d prediction. The machine-learning models obtained from these data predicted cof fee yield efficiently. The best model demonstrated predictive fit results with a Pearson correlation of 0.86. Soil chemical attributes did not interfere with th e prediction models,indicating that this analysis can be dispensed with when ap plying these models." According to the news editors,the research concluded: "These findings have impo rtant implications for optimizing coffee management and cultivation,providing v aluable insights for producers and researchers interested in maximizing yield us ing precision agriculture."
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