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

IF 3.7 3区 地球科学 Q2 ENVIRONMENTAL SCIENCES Egyptian Journal of Remote Sensing and Space Sciences Pub Date : 2024-11-08 DOI:10.1016/j.ejrs.2024.10.002
Manju Tiwari , Prasun Kumar Gupta , Nitish Tiwari , Shrikant Chitale
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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.
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时空卫星数据分析在检测水稻作物杂草侵扰方面的潜力
杂草是与主要作物争夺光照、水分和养分等必要资源的无用植被,导致粮食作物大幅减产和经济损失。解决这一问题至关重要,尤其是在云层干扰遥感能力的 Kharif 耕种季节。本研究试图调查基于卫星的时间分析在农田杂草探测中的潜力。研究的重点是恰蒂斯加尔邦赖布尔英迪拉-甘地-克里希-维希瓦维亚学院研究与教学农场的水稻种植。该研究探索了卫星图像在评估作物健康方面的效用,展示了杂草侵扰如何影响植被指数。该研究利用 PlanetScope 和哨兵-2 的卫星图像,研究了两种处理中植被指数的时间变化:纯水稻和杂草丛生的水稻。NDVI分析表明,受杂草影响的处理植被指数明显下降(降幅高达41%),这表明时间序列卫星数据可作为水稻作物杂草侵染的早期指标。哨兵-1 数据集的反向散射值进一步验证了这些发现,该数据集显示,与无杂草水稻相比,受杂草影响的处理区生长条件较差,导致反向散射减少(最多减少 18%)。虽然该技术在初步阶段显示出了功效,但其在业务环境中的更广泛应用和可扩展性还有很大潜力。
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来源期刊
CiteScore
8.10
自引率
0.00%
发文量
85
审稿时长
48 weeks
期刊介绍: The Egyptian Journal of Remote Sensing and Space Sciences (EJRS) encompasses a comprehensive range of topics within Remote Sensing, Geographic Information Systems (GIS), planetary geology, and space technology development, including theories, applications, and modeling. EJRS aims to disseminate high-quality, peer-reviewed research focusing on the advancement of remote sensing and GIS technologies and their practical applications for effective planning, sustainable development, and environmental resource conservation. The journal particularly welcomes innovative papers with broad scientific appeal.
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