Early Monitoring of Maize Northern Leaf Blight Using Vegetation Indices and Plant Traits from Multiangle Hyperspectral Data

Anting Guo, Wenjiang Huang, Kun Wang (), Binxiang Qian and Xiangzhe Cheng
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Anting Guo: Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Wenjiang Huang: Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Kun Wang: Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Binxiang Qian: Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Xiangzhe Cheng: Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

Agriculture, 2024, vol. 14, issue 8, 1-18

Abstract: Maize northern leaf blight (MNLB), characterized by a bottom-up progression, is a prevalent and damaging disease affecting maize growth. Early monitoring is crucial for timely interventions, thus mitigating yield losses. Hyperspectral remote sensing technology is an effective means of early crop disease monitoring. However, traditional single-angle vertical hyperspectral remote sensing methods face challenges in monitoring early MNLB in the lower part of maize canopy due to obstruction by upper canopy leaves. Therefore, we propose a multiangle hyperspectral remote sensing method for early MNLB monitoring. From multiangle hyperspectral data (−60° to 60°), we extracted and selected vegetation indices (VIs) and plant traits (PTs) that show significant differences between healthy and diseased maize samples. Our findings indicate that besides structural PTs (LAI and FIDF), other PTs like Cab, Car, Anth, Cw, Cp, and CBC show strong disease discrimination capabilities. Using these selected features, we developed a disease monitoring model with the random forest (RF) algorithm, integrating VIs and PTs (PTVI-RF). The results showed that PTVI-RF outperformed models based solely on VIs or PTs. For instance, the overall accuracy (OA) of the PTVI-RF model at 0° was 80%, which was 4% and 6% higher than models relying solely on VIs and PTs, respectively. Additionally, we explored the impact of viewing angles on model accuracy. The results show that compared to the accuracy at the nadir angle (0°), higher accuracy is obtained at smaller off-nadir angles (±10° to ±30°), while lower accuracy is obtained at larger angles (±40° to ±60°). Specifically, the OA of the PTVI-RF model ranges from 80% to 88% and the Kappa ranges from 0.6 to 0.76 at ±10° to ±30°, with the highest accuracy at −10° (OA = 88%, Kappa = 0.76). In contrast, the OA ranges from 72% to 80% and the Kappa ranges from 0.44 to 0.6 at ±40° to ±60°. In conclusion, this research demonstrates that PTVI-RF, constructed by fusing VIs and PTs extracted from multiangle hyperspectral data, can effectively monitor early MNLB. This provides a basis for the early prevention and control of MNLB and offers a valuable reference for early monitoring crop diseases with similar bottom-up progression.

Keywords: maize northern leaf blight; multiangle hyperspectral; early disease monitoring; vegetation indices; plant traits (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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