Abstract
Rice false smut (RFS), caused by Ustilaginoidea virens, is a significant grain disease in rice that can lead to reduced yield and quality. In order to obtain spatiotemporal change information, multitemporal hyperspectral UAV data were used in this study to determine the sensitive wavebands for RFS identification, 665–685 and 705–880 nm. Then, two methods were used for the extraction of rice false smut-infected areas, one based on spectral similarity analysis and one based on spectral and temporal characteristics. The final overall accuracy of the two methods was 74.23 and 85.19%, respectively, showing that the second method had better prediction accuracy. In addition, the classification results of the two methods show that the areas of rice false smut infection had an expanding trend over time, which is consistent with the natural development law of rice false smut, and also shows the scientific nature of the two methods.
Highlights
Pests and disease are the main causes of yield loss and reduced grain quality in global agriculture production
STF was applied to the hyperspectral imagery of each phase pixel by pixel to obtain the spatiotemporal distribution map of rice false smut
It can be seen from the figures that qualitatively, the spatial distribution of rice false smut in the two plots was relatively sparse on 14 August 2020 compared to the other dates; the occurrence area gradually became denser over time, and, with the growth of rice, the occurrence area of rice false smut gradually expanded
Summary
Pests and disease are the main causes of yield loss and reduced grain quality in global agriculture production. In addition to economic losses, pests and diseases can endanger global food security [1,2]. Abuse of pesticides will increase the economic cost for farmers and cause environmental pollution (such as water and soil pollution) [1,3]. An automated and nondestructive approach to monitoring crop pests is urgently needed to support sustainable agricultural production by reducing the application of pesticides and chemical fertilizers [4]. The development of remote sensing technology brings a promising solution for pest and disease monitoring that is favored by more researchers and farming communities [5,6,7,8]
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