计算机科学
人工神经网络
杂草
人工智能
模式识别(心理学)
杂草防治
深度学习
多光谱图像
作者
Bryan Scherrer,John W. Sheppard,Prashant Jha,Joseph A. Shaw
标识
DOI:10.1117/1.jrs.13.044516
摘要
A segment of the field of precision agriculture is being developed to accurately and quickly map the location of herbicide-resistant and herbicide-susceptible weeds using advanced optics and computer algorithms. In our previous paper, we classified herbicide-susceptible and herbicide-resistant kochia [ Bassia scoparia (L.) Schrad.] using ground-based hyperspectral imaging and a support vector machine learning algorithm, achieving classification accuracies of up to 80%. In our current work, we imaged kochia along with marestail (also called horseweed) [ Conyza canadensis (L.) Cronquist] and common lambsquarters ( Chenopodium album L.) and the crops barley, corn, dry pea, garbanzo, lentils, pinto bean, safflower, and sugar beet, all of which were grown at the Southern Agricultural Research Center in Huntley, Montana. These plants were imaged using both ground-based and drone-based hyperspectral imagers and were classified using a neural network machine learning algorithm. Depending on what plants were imaged, the age of the plants, and lighting conditions, the classification accuracies ranged from 77% to 99% for spectra acquired on our ground-based imaging platform and from 25% to 79% on our drone-based platform. These accuracies were generally highest when imaging younger plants.
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