Development of Estimation System for the Grass Chemical Composition
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概要
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A system was developed to estimate grass chemical composition values at the field level. Six important grass chemical compositions were estimated using a sensing system with a hyperspectral imaging sensor mounted on a tractor. Calibration models were obtained by using multiple linear regression, and accuracy was evaluated with three statistical analysis methods. These methods were the standard error of cross validation (SECV), the coefficient of determination, and the Bias used by cross-validation. The calibration models were evaluated the evaluation index (EI). Results indicated good potential for the sensing system to estimate grass chemical composition.
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