首页|Studies from Akdeniz University Further Understanding of Machine Learning (Prediction of Leaf Break Resistance of Green and Dry Alfalfa Leaves by Machine Learning Methods)
Studies from Akdeniz University Further Understanding of Machine Learning (Prediction of Leaf Break Resistance of Green and Dry Alfalfa Leaves by Machine Learning Methods)
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New study results on artificial intelligence have been published. According to news reporting originating from Akdeniz University by NewsRx correspondents, research stated, "Alfalfa holds an extremely significant place in animal nutrition when it comes to providing essential nutrients." Financial supporters for this research include National University of Science And Technology Politehnica Bucharest. The news journalists obtained a quote from the research from Akdeniz University: "The leaves of alfalfa specifically boast the highest nutritional value, containing a remarkable 70% of crude protein and an impressive 90% of essential vitamins. Due to this incredible nutritional profile, it becomes exceedingly important to ensure that the harvesting and threshing processes are executed with utmost care to minimize any potential loss of these invaluable nutrients present in the leaves. To minimize losses, it is essential to accurately determine the resistance of the leaves in both their green and dried forms. This study aimed to estimate the breaking resistance of green and dried alfalfa plants using machine learning methods. During the modeling phase, five different popular machine learning methods, Extra Trees (ET), Random Forest (RF), Gradient Boost (GB), Extreme Gradient Boosting (XGB), and CatBoost (CB), were used. The correlation coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) metrics were used to evaluate the models."
Akdeniz UniversityCyborgsEmerging TechnologiesMachine Learning
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