首页|Data from Federal University Broaden Understanding of Machine Learning (Models for predicting coffee yield by chemical characteristics of soil and leaves using Machine Learning)
Data from Federal University Broaden Understanding of Machine Learning (Models for predicting coffee yield by chemical characteristics of soil and leaves using Machine Learning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Wiley
Data detailed on artificial intelligence have been presented. According to news originating from Federal University by NewsRx editors, the research stated, “Coffee farming constitutes a substantial economic resource. This crop represents a source of income for several countries due to the high consumption of coffee drinks worldwide.” The news journalists obtained a quote from the research from Federal University: “Precise management of coffee crops involves collecting crop attributes (soil, plant), mapping, and applying inputs according to the plant’s needs. This differentiated management is Precision Coffee Growing stands out for its increased yield and sustainability. Thus, this research aimed to predict yield in coffee plantations by applying machine learning methodologies to soil and plant attributes. The data was obtained in a field of 54.6ha during two consecutive seasons, applying varied fertilization rates according to recommendations of soil attributes maps. Furthermore, monitoring leaf analysis maps seeks to establish a correlation between input parameters and yield prediction. The machine learning models obtained from this data efficiently predicted coffee yield. The best model demonstrated predictive fit results of 0.86 Pearson correlation. Soil chemical attributes did not interfere with the prediction models, indicating that this analysis can be dispensed with when applying these models.”
Federal UniversityCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Wiley
