Monitoring brown planthopper invasions in rice fields using UAV-based multispectral imaging
JOURNAL OF KING SAUD UNIVERSITY SCIENCE, cilt.38, sa.6, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 38 Sayı: 6
- Basım Tarihi: 2026
- Doi Numarası: 10.25259/jksus_101_2025
- Dergi Adı: JOURNAL OF KING SAUD UNIVERSITY SCIENCE
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, zbMATH, Academic Search Ultimate (EBSCO)
- Recep Tayyip Erdoğan Üniversitesi Adresli: Evet
Özet
Rice, a vital staple for billions worldwide, confronts a significant challenge from brown planthoppers (Nilaparvata lugens (St & aring;l)), which can severely diminish crop yields. This research introduces an innovative method for monitoring brown planthopper infestations in rice (Oryza sativa L.) through unmanned aerial vehicle (UAV)-based multispectral imaging. Over the course of 64 days, a DJI Phantom 4 drone was employed to capture images that underwent preprocessing, including radiometric calibration, image alignment, and distortion correction. Various vegetation indices, such as normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI), were calculated to evaluate plant health. Findings revealed a significant reduction in NDVI decreasing by approximately 45%, and SAVI by 38% near the infestation center, compared to relatively stable values in the control group. This decrease became noticeable, approximately one-month post-introduction of the brown planthoppers. The data indicates that the damage inflicted by brown planthoppers radiates outward from the center of infestation, consistent with the pest's tendency to prefer densely populated rice regions. Experimental graphs illustrated a pronounced decline in NDVI values over time as the distance from the infestation center increased, whereas the control group demonstrated minimal fluctuations, maintaining high NDVI levels throughout the study. These results substantiate the detrimental impact of brown planthoppers on susceptible rice varieties and their preference for densely clustered areas. Moreover, NDVI and SAVI have proven to be effective tools for monitoring growth alterations induced by brown planthopper infestations, highlighting the potential of drone-based imaging technology in precision agriculture for pest management applications.