Remote detection of water stress in cotton using a center pivot irrigation system-mounted sensor package.

Abstract

Much research has been invested in infrared temperature (IRT)-based methods for cotton (Gossypium hirsutism L.) water stress detection using in-field sensors, but adoption of these is low, perhaps due to logistical challenges. Alternatively, the Water Deficit Index (WDI) was developed for crop water stress assessment using remote sensors not embedded in the canopy. The objective of this research was to evaluate the performance of a sensor package-including modern IRT and normalized difference vegetation index (NDVI) sensors facing downward at 45˚, and a mini weather station-attached unintrusively to a center pivot irrigation system for detecting cotton water stress using WDI. Sensor packages were evaluated in a two-year field study that included four irrigation treatments (0, 30, 60, and 90% ET replacement) and in two production cotton fields. Overall, the tested system was effective at distinguishing crop water stress among irrigation rates. Comparison of the results to a ground-based station and simulations indicated that WDI overestimated water stress at the highest irrigation rate, but performed well otherwise. Accuracy of the system could be improved by measuring canopy coverage (Fc) from the same vantage point as the IRT and NDVI sensors (from the pivot, downward at a 45˚ angle).

Plant phenotyping relevance

綿花の水ストレスという植物状態を、センターピボット搭載センサーパッケージとWDIで検出・評価する方法が研究の中心であり、技術性能の比較検証も行っている。

Code and data availability

The paper reports pivot-mounted IRT/NDVI sensor and UAS-derived phenotyping measurements (NDVI, Fc, WDI, weather data) and DSSAT simulations, but no public repository deposit of these datasets or analysis code is stated. The Data availability statement says the supporting data can be obtained from the corresponding作者on

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