Abstract
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.
Highlights
Wheat (Triticum aestivum) is an important food crop in China
This study demonstrates the capability of hyperspectral imaging to determine occurrence and severity of Powdery mildew (PM) infection at the early stage
The spectral reflectance of diseased leaves was higher in the visible light band (VIS) region but lower in the nearinfrared band (NIR) region, which is consistent with previous studies [17]
Summary
Wheat (Triticum aestivum) is an important food crop in China. The sustainable production of wheat is important for the stability of China’s society and economy. Wheat is often attacked by various diseases due to unfavorable environments and management conditions. Tritici) is one of the major diseases that seriously affect wheat yield and quality [1]. People use disease-resistant varieties [2,3] and spray pesticides [4] to control PM, but these are not optimal solutions, and will produce an increased cost and environmental pollution. The essential approach to combating PM is the early identification and quantitative assessment of disease severity (DS), thereby helping farmers to ensure timely use of fungicides [5]
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