Integrating Feature Selection and Machine Learning for Nitrogen Assessment in Grapevine Leaves using In-Field Hyperspectral Imaging
整合特征选择与机器学习用于葡萄叶氮含量评估的田间高光谱成像
机构 * organization= Center for Precision ; Automated Agricultural Systems , addressline= Washington State University , city= Prosser , postcode= 99350 , state= WA , country= USA ; organization= Biological \& Environmental Engineering Department , addressline= Cornell University , city= Ithaca , postcode= 14853 , state= NY , country= USA ; organization= Fruit Research ; Extension Center , addressline= The Penn State University , city= Biglerville , postcode= 17307 , state= PA , country= USA ; organization= School of Business ; Technology , addressline= Curry College , city= Milton , postcode= 02186 , state= MA , country= USA ; organization= Department of Viticulture ; Enology , addressline= Washington State University , city= Prosser , postcode= 99350 , state= WA , country= USA ; Genetic Improvement Research Unit (HCPGIRU) , city= Corvallis , postcode= 973300 , state= OR , country= USA ; organization= Mid-Columbia Agricultural Research ; Extension Center , addressline = Oregon State University , city= Corvallis , postcode= 97031 , state= OR , country= USA ; organization= Department of Plant Sciences , addressline = University of Tennessee, Institute of Agriculture , city= Knoxville , postcode= 37996 , state= TN , country= USA
AI总结 本文通过田间高光谱成像与机器学习整合,开发了特征选择框架以提高葡萄叶氮含量预测精度,验证了在不同生长阶段和品种间的方法有效性。
Comments Major Revision