Effectiveness of drone-based thermal sensors in optimizing controlled environment agriculture performance under arid conditions.

Abstract

Controlled environmental agriculture (CEA), integrated with internet of things and wireless sensor network (WSN) technologies, offers advanced tools for real-time monitoring and assessment of microclimate and plant health/stress. Drone applications have emerged as transformative technology with significant potential for CEA. However, adoption and practical implementation of such technologies remain limited, particularly in arid regions. Despite their advantages in agriculture, drones have yet to gain widespread utilization in CEA systems. This study investigates the effectiveness of drone-based thermal imaging (DBTI) in optimizing CEA performance and monitoring plant health under arid conditions. Several WSN sensors were deployed to track microclimatic variations within the CEA environment. A novel method was developed for assessing canopy temperature (Tc) using thermocouples and DBTI. The crop water stress index (CWSI) was computed based on Tc extracted from DBTI. Findings revealed that DBTI effectively distinguished between all treatments, with Tc detection exhibiting a strong correlation (R 2 = 0.959) with sensor-based measurements. Results confirmed a direct relationship between CWSI and Tc, as well as a significant association between soil moisture content and CWSI. This research demonstrates that DBTI can enhance irrigation scheduling accuracy and provide precise evapotranspiration (ETc) estimates at specific spatiotemporal scales, contributing to improved water and food security.

Plant phenotyping relevance

ドローン熱画像から作物の樹冠温度と水ストレス指数を抽出する手法を開発し、センサー測定との相関で検証しており、植物状態の取得法が中心である。

Code and data availability

The paper's phenotype data (thermal images, thermocouple measurements, CWSI/Ig values) are not publicly deposited; the authors state data are available only upon reasonable request from the corresponding author. No public code, models, or datasets are mentioned.

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