ArticleScientific reports2025
Effectiveness of drone-based thermal sensors in optimizing controlled environment agriculture performance under arid conditions.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- UAV-Based Thermal Inversion for Canopy Temperature Retrieval and Precision Irrigation.Sensors (Basel, Switzerland) · 2026Article
- From detection to action: artificial intelligence in integrated pest and invasive plant management.Advanced biotechnology · 2026Review
- Drought influence on carbon assimilation and water use efficiency in Mediterranean ecosystems.Scientific reports · 2026Article
- A Uav-based multisensor framework for legal industrial Cannabis monitoring and open-access dataset development.Data in brief · 2026Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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