Assessment for crop water stress with infrared thermal imagery in precision agriculture: A review and future prospects for deep learning applications

Abstract

With the increasing global water scarcity, efficient assessment methods for crop water stress have become a prerequisite to perform precision irrigation scheduling. The 1accessibility of infrared thermal sensor provides a powerful tool to detect and quantify crop water stress. This paper reviews the current practices of infrared thermal imagery utilized to assess crop water stress. Overall, three technological aspects of infrared thermal sensing applications for crop water stress assessment are reviewed along with the challenges and recommendations: (i) introduction of uncooled thermal camera and platforms, including ground-based platform and unmanned aerial vehicles (UAVs) platforms, for thermal imaging acquisition, (ii) strategies of canopy segmentation in thermal imaging used to obtain average canopy temperature for CWSI calculation, (iii) correlation between three forms of crop water stress index (CWSI) i.e. theoretical CWSI (CWSIt), empirical CWSI (CWSIe), and statistic CWSI (CWSIs) and physiological indicators. The emphasis is on imaging process techniques for canopy segmentation in thermal imaging. As a future perspective, the potential use of deep learning approaches to assess crop water stress has been elaborated highlighting the future trends.

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