Application and Innovation of Digital Technologies in Controlled Environment Agriculture

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 25 August 2026 | Viewed by 849

Editors


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Guest Editor
Department of Horticulture, University of Georgia, 120 Carlton Street, 1111 Plant Sciences Building, Athens, GA 30602, USA
Interests: controlled environment agriculture (CEA); plant phenotyping; indoor remote sensing; hyperspectral imaging; interpretable machine learning; imagery data mining; agricultural robotics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Biological and Agricultural Engineering, College of Agriculture and Life Sciences, Texas A&M University, Dallas, TX, USA
Interests: precision agriculture; controlled environment agriculture; robotics; sensing and automation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Controlled environment agriculture (CEA) is rapidly transforming modern food production by enabling precise control of crop growth conditions, improving resource efficiency, and ensuring year-round productivity. As CEA systems expand in scale and complexity, digital technologies, ranging from imaging and sensing platforms to robotics, artificial intelligence, and data-driven decision tools, have become indispensable for optimizing crop performance and operational sustainability. These innovations enhance growers’ ability to monitor plant health, optimize inputs, automate labor-intensive tasks, facilitate crop protection strategies, and improve forecasting accuracy. Based on these advancements, we are pleased to announce a Special Issue of Agronomy, titled “Application and Innovation of Digital Technologies in Controlled Environment Agriculture,” which will focus on the following topics:

  • The current landscape of digital technologies supporting CEA production, management, and crop protection;
  • Novel sensing, imaging, and data analytics approaches for real-time crop and environmental assessment and prediction;
  • Integration of robotics, IoT systems, and AI-driven decision tools to enhance CEA productivity and sustainability;
  • Challenges and opportunities in scaling digital innovations across various commercial CEA production systems.

Dr. Zhihang Song
Dr. Azlan Zahid
Guest Editors

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Keywords

  • controlled environment agriculture (CEA)
  • agricultural robotics
  • plant phenotyping
  • real-time crop monitoring
  • precision agriculture
  • AI in agriculture
  • Internet of Things (IoT)

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Published Papers (1 paper)

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Research

19 pages, 16647 KB  
Article
Automated High-Frequency RGB Imaging for Biomass Estimation in Hydroponics
by Andrius Grigas, Tomas Krilavičius, Eimantas Zaranka, Danylo Abramov, Sarwan Shafeeq, Dainius Savickas, Indrė Bručienė, Veronika Bryskina, Deividas Valiuška and Rūta Juozaitienė
Agronomy 2026, 16(10), 963; https://doi.org/10.3390/agronomy16100963 - 12 May 2026
Viewed by 328
Abstract
Accurate, non-destructive estimation of crop biomass is essential for automated high-frequency monitoring and optimization in controlled-environment agriculture, yet standardized approaches remain limited for short-cycle hydroponic systems. This study introduces a reproducible and fully automated method for estimating the biomass of hydroponically grown wheat [...] Read more.
Accurate, non-destructive estimation of crop biomass is essential for automated high-frequency monitoring and optimization in controlled-environment agriculture, yet standardized approaches remain limited for short-cycle hydroponic systems. This study introduces a reproducible and fully automated method for estimating the biomass of hydroponically grown wheat sprouts (HWSs) using high-frequency RGB imaging. The workflow integrates image preprocessing, tray segmentation, and canopy feature extraction with synchronized load-cell measurements to enable continuous, non-invasive growth tracking. To account for irrigation events and associated weight fluctuations, raw mass signals were processed using a second-order low-pass Bessel filter, preserving underlying biomass trends while removing short-term oscillations. Across 3024 paired image–mass observations collected under commercial cultivation conditions, several canopy coverage, color-based indices (AGI, Proxy NDVI), and texture features exhibited strong predictive relationships with biomass. Features reflecting greenness, canopy density, and color uniformity were positively associated with plant mass, whereas brightness- and red-channel features showed consistent negative relationships. Feature selection using an elastic-net approach identified a compact subset of informative predictors, improving model stability and interpretability. Under a nested cross-validation framework based on contiguous interval splits within sprout-growth cohorts, support vector regression (SVR) achieved the best predictive performance, with an sMAPE of 3.64% and an RMSE of 0.16 kg. Additional experiments under altered illumination conditions showed that including light intensity as an explicit covariate improved model robustness across lighting regimes. These results demonstrate that combining elastic-net feature selection with environmental covariates provides a robust and transferable framework for visual biomass estimation in hydroponic HWS. More broadly, the proposed pipeline enables non-destructive crop monitoring and supports the development of intelligent, feedback-driven control strategies for hydroponic production systems. Full article
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