1. 华中农业大学资源与环境学院，湖北 武汉 430070
2. 农业部长江中下游耕地保育重点实验室，湖北 武汉 430070
3. 安徽大学安徽省农业生态大数据工程实验室，安徽 合肥 230601
4. Texas A&M AgriLife Research and Extension Center, Beaumont, TX 77713, USA
5. 华中农业大学理学院，湖北 武汉 430070
Comparison between the Effects of Visible Light and Multispectral Sensor Based on Low-Altitude Remote Sensing Platform in the Evaluation of Rice Sheath Blight
1. College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
2. Key Laboratory of Farmland Conservation in the Middle and Lower Reaches of the Ministry of Agriculture and Rural Affairs, Wuhan 430070, China
3. Anhui Engineering Laboratory of Agro-Ecological Big Data, Anhui University, Hefei 230601, China
4. Texas A&M AgriLife Research and Extension Center, Beaumont, TX 77713, USA
5. College of Science, Huazhong Agricultural University, Wuhan 430070, China
Abstract：Efficient and non-destructive assessment of crop disease grade is of great significance to the practical agricultural production and research. In this study, the feasibility of low-altitude UAV (Unmanned Aerial Vehicle) remote sensing platform for the disease grade assessment of rice Sheath Blight (ShB) was discussed. Then the spectral response differences of visible light sensor and multispectral sensor and their effects on the spectral reflectance acquisition of rice with ShB were analyzed. And rice ShB monitoring effects of two kinds of sensors were compared quantitively. The study area consisted of 67 rice plots with different varieties, each of which was divided into inoculation zone and infection zone. The drone was Phantom 3 Advanced, a small consumer-grade UAV made by DJI-Innovations company, and the payloads were the self-contained visible light sensor and Micasense RedEdgeTM multispectral sensor to acquire remote sensing images respectively. At the same time, the rice ShB disease grades were investigated by manual expert recognition and measured NDVI was obtained with Trimble's GreenSeeker Handheld Crop Sensor. Remote sensing images were preprocessed by image mosaic, layer stacking and radiometric calibration. A total of 134 plots in inoculation and infection zones of visible light image were used to calculate seven kinds of visible light vegetation indices, namely NDI (Normalized Difference Index), ExG (Excess Green), ExR (Excess Red), ExG-ExR, B*, G* and R*. Besides the above seven kinds of visible light vegetation indices, multispectral image was calculated by three kinds of multispectral vegetation indices additionally, namely NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index) and NDWI (Normalized Difference Water Index). The correlation between the image-based vegetation index and ground-based NDVI was analyzed, and the optimal image-based vegetation indices of the visible light and multispectral sensor were selected to establish the disease grade inversion model of rice ShB. The results of correlation analysis showed that the fitting degree of image-based NDVI and ground-based NDVI based on multispectral sensor was the highest, and R2 was 0.914 and RMSE was 0.024 in the inoculation zone, while R2 and RMSE were 0.863 and 0.024 respectively in the infection zone. As for the visible light sensor, the correlation between image-based NDI and measured NDVI was best, and R2 was 0.875 and RMSE was 0.011 in the inoculation zone, while R2 was 0.703 and RMSE was 0.014 in the infection zone. The consistencies of the same image-based vegetation index and ground-based NDVI of two kinds of sensors and two kinds of zones were compared, which revealed that NDI, ExR, ExG-ExR, G*, ExG, R* except B* were mainly highly correlated with the measured NDVI. In the inoculation zones with severe disease, the two kinds of sensors had similar effects on the detection of rice ShB, but the monitoring effect of multispectral sensor was more precise and sensitive in infection zones with relatively lighter disease. The disease grade inversion model of rice ShB established by NDVI based on multispectral sensor was effective, whose R2 reached 0.624, and RMSE was 0.801 and prediction accuracy was 90.04%. The disease grade inversion model established by NDI based on visible light sensor was slightly worse, whose R2 was 0.580, and RMSE was 0.847 and prediction accuracy was 89.45%. The spectral response curves of visible light and multispectral sensor were compared and analyzed. The visible light sensor can obtain three bands of red, green, blue in the range of visible light, and wavelength range overlaps with each other, while the multispectral sensor including five imaging units can independently obtain five narrow-band spectral bands from visible light to near infrared providing subtler spectral information. Through comparing the average reflectance curves of rice in inoculation zone and infection zone, the multispectral sensor not only reflected bigger difference than visible light sensor in the visible light band, but also represented more obvious difference in the red and near infrared band, which demonstrated that the professional narrow-band sensor had an advantage over broad-band consumer-grade sensor in the rice ShB monitoring. In conclusion, it is feasible to evaluate the disease grade of rice ShB based on the low-altitude UAV remote sensing platform with visible light and multispectral sensor. The multispectral sensor is precise and sensitive which can be used for early detection of rice ShB, and the visible light sensor is less accurate but economical and easy to popularize. The results of this study are expected to provide decision support for diseases control and be beneficial to promoting precision agriculture and ensureing food security.
赵晓阳，张 建，张东彦，周新根，刘小辉，谢 静. 低空遥感平台下可见光与多光谱传感器在水稻纹枯病病害评估中的效果对比研究[J]. 光谱学与光谱分析, 2019, 39(04): 1192-1198.
ZHAO Xiao-yang, ZHANG Jian, ZHANG Dong-yan, ZHOU Xin-gen, LIU Xiao-hui, XIE Jing. Comparison between the Effects of Visible Light and Multispectral Sensor Based on Low-Altitude Remote Sensing Platform in the Evaluation of Rice Sheath Blight. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(04): 1192-1198.
