基于移动传感的Rabi 2021-22马铃薯病虫害可扩展预测

Swagatam Bose Choudhury, Vidit Patil, Abhishek Kumar, R. Kulat, Sanat Sarangi, Hemavathy B, S. Pappula
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引用次数: 0

摘要

农业中大规模采用数字技术正在扩大精准农业的范围。印度北部的印度河-恒河地区是一个主要的马铃薯种植区,其中西孟加拉邦是早疫病、晚疫病、Septoria叶斑病和蓟马病的主要病虫害之一。预测整个地区的压力需要了解作物,其压力行为动态和整个感兴趣区域环境条件变化的模型。对于快速变化的地面条件,可以将预测压力的可能性与通过图像检测压力事件相结合,以便在季节中及时纠正航向,从而降低风险。我们介绍了在西孟加拉邦2021-22年Rabi季节报告条件自动检测支持下的区域水平马铃薯应力预测工作。提出了各种建议方法的结果,并对其进行了对比,以解决相关的场景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scalable Prediction of Potato Pests and Diseases with Insights from Mobile Sensing for Rabi 2021–22
Large scale adoption of digital technologies in farming is expanding the reach of precision agriculture. The Indo-Gangetic region in northern India is a major potato growing belt where West Bengal is one of the prominent states with Early Blight, Late Blight, Septoria Leaf Spot, and Thrips being some key disease and pest conditions. Forecasting stress across the region requires models that understand the crop, its stress behaviour dynamics and variation of ambient conditions across the zone of interest. For rapidly changing conditions on the ground, predicting the likelihood of stress could be combined with detection of stress incidents through images to course-correct timely during the season thereby mitigating risks. We present the work on region-level stress forecast in potato supported by automated detection of reported conditions for the Rabi season of 2021–22 in West Bengal. The results with various proposed approaches are presented and contrasted to address the related scenarios.
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