Water Stress Index Detection Using a Low-Cost Infrared Sensor and Excess Green Image Processing.

Abstract

Precision Irrigation (PI) is a promising technique for monitoring and controlling water use that allows for meeting crop water requirements based on site-specific data. However, implementing the PI needs precise data on water evapotranspiration. The detection and monitoring of crop water stress can be achieved by several methods, one of the most interesting being the use of infra-red (IR) thermometry combined with the estimate of the Crop Water Stress Index (CWSI). However, conventional IR equipment is expensive, so the objective of this paper is to present the development of a new low-cost water stress detection system using TL indices obtained by crossing the responses of infrared sensors with image processing. The results demonstrated that it is possible to use low-cost IR sensors with a directional Field of Vision (FoV) to measure plant temperature, generate thermal maps, and identify water stress conditions. The Leaf Temperature Maps, generated by the IR sensor readings of the plant segmentation in the RGB image, were validated by thermal images. Furthermore, the estimated CWSI is consistent with the literature results.

Plant phenotyping relevance

低コスト赤外線センサーと画像処理による植物の水ストレス検出・葉温マップ生成法を開発し、熱画像で検証しているため、植物フェノタイピング手法が中心です。

Code and data availability

The paper describes a low-cost IR sensor + ExG image processing system for CWSI detection in arugula, but provides no public dataset, image, or code deposit. The Data Availability Statement reads 'Not applicable,' and no repository, URL, or supplement containing the authors' data or Python analysis code is mentioned. C

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