Potential of temporal satellite data analysis for detection of weed infestation in rice crop

Abstract

Weeds are unwanted vegetation that compete with main crops for essential resources like light, water, and nutrients, leading to significant reductions in food crop yield and economic losses. Addressing this issue is crucial, particularly during the Kharif cropping season when cloud cover interferes with remote sensing capabilities. This study is an attempt to investigate the potential of satellite-based temporal analysis in weed detection from agricultural fields. The research focused on rice cultivation at the Research cum Instructional farms of Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh. The study explored the utility of satellite imagery for assessing crop health, demonstrating how weed infestation influences vegetative indices. The study utilized satellite images from PlanetScope and Sentinel-2 to examine the temporal variation in vegetation indices across two treatments: pure rice and rice with weeds. NDVI analysis revealed a significant decline in treatments affected by weeds (upto 41% less), suggesting that time-series satellite data can serve as an early indicator of weed infestation in standing rice crops. These findings were further verified by backscatter values from the Sentinel-1 dataset, which indicated a reduction in backscatter (upto 18% less) due to the suboptimal growth conditions in weed-infested treatments compared to weed-free rice. While the technology has shown efficacy at a preliminary stage, there is significant potential for its broader application and scalability in operational contexts.