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Preliminary Study on Multispectral Imaging Techniques for Early Pest Detection in Rice Production
Fig. 3.　Heatmaps of normalized difference vegetation index (NDVI) deviation under different brown planthopper (BPH) infestation densities relative to the control and rice near border (BL) groups. (A) shows the ΔNDVI of each treatment group relative to the control (CK), and (B) shows the ΔNDVI relative to the rice near border group (BL). The x-axis represents treatment groups, the left y-axis indicates days after transplanting, and the right y-axis indicates days after BPH release. The gray dashed line marks the infestation day. The color scale represents the degree of deviation from the natural variation threshold (± 0.0135), with a symmetric diverging palette. These heatmaps visualize the temporal dynamics and severity of rice growth suppression under different infestation densities, providing a reference for damage assessment and early-warning decisions based on the natural variation threshold.
Fig. 3.　Heatmaps of normalized difference vegetation index (NDVI) deviation under different brown planthopper (BPH) infestation densities relative to the control and rice near border (BL) groups. (A) shows the ΔNDVI of each treatment group relative to the control (CK), and (B) shows the ΔNDVI relative to the rice near border group (BL). The x-axis represents treatment groups, the left y-axis indicates days after transplanting, and the right y-axis indicates days after BPH release. The gray dashed line marks the infestation day. The color scale represents the degree of deviation from the natural variation threshold (± 0.0135), with a symmetric diverging palette. These heatmaps visualize the temporal dynamics and severity of rice growth suppression under different infestation densities, providing a reference for damage assessment and early-warning decisions based on the natural variation threshold.

Author:Huai-Wen Tsao, Yu-Shun Huang, Shou-Horng Huang, Po-Yu Lai, and Ming-Hsin Lai*

Abstract:

This study focuses on the brown planthopper (Nilaparvata lugens (Stål)), a major pest in rice cultivation, and proposes an early-stage pest detection approach that integrates artificial infestation experiments with unmanned aerial vehicle-based multispectral imaging. Treatments with varying insect population densities were simulated, and rice spectral responses were monitored using multispectral imagery collected during the experiment. In this study, the term “early stage” refers to the period between the panicle initiation and heading stages, and corresponds to the typical outbreak period of brown planthopper (BPH) infestations in the second cropping season. Results showed that when the normalized difference vegetation index (NDVI) deviated from the control group by more than the natural variation range (± 1s), such deviation could serve as an early-warning signal with the potential to indicate the onset of BPH infestation. The feasibility of using rice plants near field borders as a substitute reference for formal control plots was also evaluated to improve the feasibility for field operation. The proposed approach provides a quantifiable basis for pest monitoring and serves as a potential alternative to traditional manual scouting in the context of smart agriculture.

Key words:Brown planthopper, Insect pest monitoring, Multispectral, Smart agriculture

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Journal of
Taiwan Agricultural Research
Vol. 74, No.3 (2025) ISSN: 2790-086X Vol. 74, No.3 (2025)
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