Abstract
Crop pests and diseases are key factors that damage crop production and threaten food security. Remote sensing techniques may provide an objective and effective alternative for automatic detection of crop pests and diseases. However, ground-based spectroscopic or imaging sensors may be limited in practically guiding the precision application and reduction of pesticide. Therefore, this study developed an unmanned aerial vehicle (UAV)-based remote sensing system to detect leaf folder (Cnaphalocrocis medinalis). Rice canopy reflectance spectra were obtained in the booting growth stage by using the UAV-based hyperspectral remote sensor. Newly developed and published multivariate spectral indices were initially calculated to estimate leaf-roll rates. The newly developed two-band spectral index (R490−R470), three-band spectral index (R400−R470)/(R400−R490), and published spectral index photochemical reflectance index (R550−R531)/(R550+R531) showed good applicability for estimating leaf-roll rates. The newly developed UAV-based micro hyperspectral system had potential in detecting rice stress induced by leaf folder. The newly developed spectral index (R490−R470) and (R400−R470)/(R400−R490) might be recommended as an indicator for estimating leaf-roll rates in the study area, and (R550−R531)/(R550+R531) might serve as a universal spectral index for monitoring leaf folder.
Highlights
Crop pests and diseases, such as insects, plant pathogens, and weeds, are key factors that damage crop production and threaten food security [1]
Three scales of proximal- or remotesensed observations were used for pest and disease detection: (i) the leaf scale, mainly performed in the field or laboratory to test the methodology; (ii) the canopy scale, usually performed in situ and took into account the canopy structural characteristics of the plants; and (iii) the farm scale for precision management applications, which encompassed an entire farm by acquiring airborne data; for example, Zhang et al [48] detected late blight disease in tomatoes by using airborne visible infrared imaging spectrometer (AVIRIS) image and Hillnhütter et al [49] adopted hyperspectral mapper (Hymap) data to detect plant stress caused by heterodera schachtii and rhizoctonia solani in sugar beet
The results indicated that it was feasible to detect rice stress induced by rice leaf folder (Cnaphalocrocis medinalis), and that the maps of leaf-roll rates could guide precision usage of pesticide
Summary
Crop pests and diseases, such as insects, plant pathogens, and weeds, are key factors that damage crop production and threaten food security [1]. It is estimated by the Food and Agriculture Organization (FAO) that crop pests and diseases result in approximately 24% of grain losses globally every year and 40 billion kg of grain reduction in China [2]. The accurate and timely monitoring of the location, range, and harm degree of crop pests and diseases is essential to reduce expensive pesticide use in crop protection and to render agriculture more eco-friendly and sustainable. New innovative techniques are required to monitor crop pests and diseases over a vast area cheaply and quickly
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