Determination of leaf breaking strength in some of clover genotypes collected from in Antalya natural flora

Öten, Mehmet; Kabaş, Önder; Kiremitci, Semiha

doi:10.16882/derim.2018.339204

Cited by 1 publication

(1 citation statement)

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“…Shinners et al (1987) [9] found that longitudinal shearing of alfalfa stems required less than 1/10 of the energy required to shear alfalfa transversely. Öten et al (2018) [10] carried out studies on the determination of both the green and dry leaf breaking resistance of some clover genotypes collected from the natural flora of Antalya province. It was reported that the highest leaf break force value was 1.0419 N and the lowest was 1.0022 N in the dry samples.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Leaf Break Resistance of Green and Dry Alfalfa Leaves by Machine Learning Methods

Ercan,

Kabas,

Moiceanu

2024

Applied Sciences

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Alfalfa holds an extremely significant place in animal nutrition when it comes to providing essential nutrients. 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. The obtained metric results and the graphs obtained from the prediction values of the models revealed that the machine learning methods made successful predictions. The best R2 (0.9853), RMSE (0.0171), MAE (0.0099) and MAPE (0.0969) values for the dry alfalfa plant were obtained from the model established with the ET method, while the best RMSE (0.0616) and R2 (0.96) values for the green alfalfa plant were obtained from the model established with the RF method and the best MAE (0.0340) value was obtained from the model established with the ET method. Additionally, the best MAPE (0.1447) value was obtained from the model established with the GB method.

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Section: Introductionmentioning

confidence: 99%