白粉病
高光谱成像
偏最小二乘回归
线性判别分析
数学
线性回归
模式识别(心理学)
统计
回归
人工智能
计算机科学
生物
农学
作者
Imran Khan,Haiyan Liu,Wei Li,Aizhong Cao,Xue Wang,Hongyan Liu,Tao Cheng,Yongchao Tian,Yan Zhu,Weixing Cao,Xia Yao
出处
期刊:Remote Sensing
[MDPI AG]
日期:2021-09-10
卷期号:13 (18): 3612-3612
被引量:26
摘要
Early detection of the crop disease using agricultural remote sensing is crucial as a precaution against its spread. However, the traditional method, relying on the disease symptoms, is lagging. Here, an early detection model using machine learning with hyperspectral images is presented. This study first extracted the normalized difference texture indices (NDTIs) and vegetation indices (VIs) to enhance the difference between healthy and powdery mildew wheat. Then, a partial least-squares linear discrimination analysis was applied to detect powdery mildew with the combined optimal features (i.e., VIs & NDTIs). Further, a regression model on the partial least-squares regression was developed to estimate disease severity (DS). The results show that the discriminant model with the combined VIs & NDTIs improved the ability for early identification of the infected leaves, with an overall accuracy value and Kappa coefficient over 82.35% and 0.56 respectively, and with inconspicuous symptoms which were difficult to identify as symptoms of the disease using the traditional method. Furthermore, the calibrated and validated DS estimation model reached good performance as the coefficient of determination (R2) was over 0.748 and 0.722, respectively. Therefore, this methodology for detection, as well as the quantification model, is promising for early disease detection in crops.
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