Crop canopy temperature measurement is necessary for monitoring water stress indicators such as the Crop Water Stress Index (CWSI). Water stress indicators are very useful for irrigation strategies management in the precision agriculture context. For this purpose, one of the techniques used is thermography, which allows remote temperature measurement. However, the applicability of these techniques depends on being affordable, allowing continuous monitoring over multiple field measurement. In this article, the development of a sensor capable of automatically measuring the crop canopy temperature by means of a low-cost thermal camera and the implementation of artificial intelligence-based image segmentation models is presented. In addition, we provide results on almond trees comparing our system with a commercial thermal camera, in which an R-squared of 0.75 is obtained.

This research was funded by the Agencia Estatal de Investigación (AEI) under project numbers: AGL2016-77282-C3-3-R, and PID2019-106226-C22 AEI/https://doi.org//10.13039/501100011033. FPU17/05155, FPU19/00020 have been granted by Ministerio de Educación y Formación Profesional. The authors would like to acknowledge the support of Miriam Montoya Gómez in language assistance.