Advancement in Controlled Environment Agriculture (CEA) Automation and Crop Management—2nd Edition

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Horticultural and Floricultural Crops".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1127

Special Issue Editors


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Guest Editor
Department of Food, Agricultural and Biological Engineering, The Ohio State University, Wooster, OH 44691, USA
Interests: controlled environment plant production
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Guest Editor
College of Business, Engineering, and Technology, Kentucky State University, Frankfort, KY 40601, USA
Interests: smart farming; hydroponics; nutrient dynamics; sensor fusion; irrigation; artificial intelligence; agricultural machinery; precision agriculture
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Department of Agronomy, Horticulture, & Plant Science, South Dakota State University, Brookings, SD 57007, USA
Interests: sustainable horticultural crop production; controlled environment agriculture; aquaponics; hydroponics; nutrient management; plant nutrient; plant physiology
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Special Issue Information

Dear Colleagues,

Following the success of the first edition of the Special Issue “Advancement in Controlled Environment Agriculture (CEA) Automation and Crop Management” (https://www.mdpi.com/journal/agronomy/special_issues/BX8C4AF967), we are pleased to launch its second edition.

Rapid climate change, urbanization, decreasing soil fertility, resource wastage, and environmental degradation pose significant global challenges. Controlled environment agriculture (CEA) has the potential to minimize these problems by enhancing nutrition security, reducing the carbon footprint and increasing resource use efficiency. Automation technologies have evolved to encompass a wide spectrum of tasks, ranging from environmental control to precise resource allocation. Through the integration of sensors, robotics, and smart control systems, CEA facilities can optimize conditions such as temperature, humidity, CO2 levels, light, and nutrients, thereby creating an ideal environment for plant growth, which not only enhances crop quality and quantity but also contributes to environmental sustainability by minimizing waste.

In tandem with automation, there has been a significant transformation in crop management through the integration of data-driven insights. Advanced analytics and machine learning algorithms analyze a multitude of factors, including plant health, growth patterns, and historical data, thereby empowering farmers with actionable intelligence. This allows for preemptive actions, an early identification of possible problems, and the fine-tuning of farming methods. As a result, farmers can achieve higher yields, reduce environmental impact, and improve resource use efficiency.

The integration of CEA automation and crop management heralds a new era in agriculture, fostering resilience and adaptability in the face of evolving environmental challenges. With ongoing advancements, the future promises even more sophisticated and interconnected systems, further elevating the accuracy, efficiency, and eco-friendliness of CEA practices.

For this Special Issue, we welcome submissions of cutting-edge and innovative research articles, reviews, and methods manuscripts that fall within one or more of the keywords listed below, addressing existing challenges and enhancing the efficiency and sustainability of CEA production.

Dr. Peter P. Ling
Dr. Milon Chowdhury
Dr. Teng Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • advanced environmental control techniques
  • canopy architecture optimization
  • root zone management
  • energy use efficiency
  • AI-based plant health and growth prediction
  • decision support systems
  • climate-smart advanced technology
  • sustainability of CEA production

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Published Papers (1 paper)

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Research

26 pages, 5629 KB  
Article
Effect of Red–Blue Light Ratios on Leaf Development and Steviol Glycoside Production at Different Growth Stages in Hydroponic Stevia
by Cheng Tai Chou, Vivian Christabel, Mai Anh Le, Min-Lang Tsai and Shang-Ta Wang
Agronomy 2026, 16(8), 770; https://doi.org/10.3390/agronomy16080770 - 8 Apr 2026
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Abstract
Stevia is a natural source of high-intensity sweeteners, collectively known as steviol glycosides (SG), which are approximately 300 times sweeter than sucrose and widely used as sugar substitutes. This study examines the impact of five different red-to-blue (R:B) light ratios on SG content [...] Read more.
Stevia is a natural source of high-intensity sweeteners, collectively known as steviol glycosides (SG), which are approximately 300 times sweeter than sucrose and widely used as sugar substitutes. This study examines the impact of five different red-to-blue (R:B) light ratios on SG content and yield in hydroponic Stevia across four growth stages. Results indicate that the highest and lowest leaf dry weights were recorded in the R1B0 (R:B = 1:0) and R0B1 (R:B = 0:1) groups, at 2.88 and 1.98 g/plant, respectively, reflecting a 45.45% difference. The total SG content in dried leaves was highest in R0B1 (196.32 mg/g) and lowest in R1B0 (115.16 mg/g), with a 70.48% variation. The highest and lowest total SG yields (YSG) per square meter were observed in R0B1 (46.56 g/m2) and R50B37 (35.70 g/m2), differing by 30.42%. Stage-specific optimal YSG values were identified, with designated growth stages P1 (early vegetative growth phase), P2 (early leaf development phase), and P3 (late leaf development phase) favoring R4B1 and P4 (leaf senescence phase) favoring R0B1. These findings suggest an optimized lighting strategy for the four growth stages of hydroponic Stevia, sequentially applying R4B1, R4B1, R4B1 and R0B1 to enhance biomass accumulation and SG production at different developmental stages. Full article
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