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Agronomy
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Advancement in Controlled Environment Agriculture (CEA) Automation and Crop Management
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Advancement in Controlled Environment Agriculture (CEA) Automation and Crop Management

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 9876

Special Issue Editors

Dr. Peter P. Ling

E-Mail Website
Guest Editor
Department of Food, Agricultural and Biological Engineering, The Ohio State University, Wooster, OH 44691, USA
Interests: controlled environment plant production
Dr. Milon Chowdhury

E-Mail Website
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
Dr. Teng Yang

E-Mail Website
Guest Editor
School of Applied and Interdisciplinary Studies, Kansas State University, Olathe, KS 66061, USA
Interests: sustainable horticultural crop production; controlled environment agriculture; aquaponics; hydroponics; nutrient management; plant nutrient; plant physiology

Special Issue Information

Dear Colleagues,

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, innovative research, reviews and methods manuscripts that fall within one or more of the keywords listed below, that could address existing challenges and enhance 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 100 words) can be sent to the Editorial Office for announcement on this website.

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 monthly 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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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

20 pages, 5504 KiB  
Article
Influence of Light Intensity and Nutrient Concentration on Soybean (Glycine max (L.) Merr.) Seedling Growth
by Kiet Anh Huynh, Márton Jolánkai, Mária Katalin Kassai, Gergő Péter Kovács, Csaba Gyuricza and László Balázs
Agronomy 2025, 15(5), 1037; https://doi.org/10.3390/agronomy15051037 - 25 Apr 2025
Viewed by 94
Abstract
Light and nutrient availability are critical factors of plant growth and development, particularly at the early stages, where they significantly influence the establishment and survival of young seedlings. The morphological parameters and the biomass accumulation of soybean were measured in a hydroponic vertical [...] Read more.
Light and nutrient availability are critical factors of plant growth and development, particularly at the early stages, where they significantly influence the establishment and survival of young seedlings. The morphological parameters and the biomass accumulation of soybean were measured in a hydroponic vertical farm in the first 14 days of seedling growth in two successive experiments under two types of lighting environments and at three nutrient concentration levels. The lighting conditions were set by two parallel variable-spectrum linear luminaires positioned above the lower and upper edges of the cultivation trays. In the first lighting environment, seedlings were exposed to a constant photosynthetic photon flux density (PPFD) with red and blue photon irradiance ratio (R/B) varying in broad range from the lower to the upper end of the cultivation trays. In the second environment, the spatial R/B distribution was uniform, and the PPFD varied between two maxima at the edges and a minimum in the middle of the trays. The R/B ratio within the 0.6–6 interval had little or no effect on plant development. We report the dependence of growth traits as a function of PPFD in the range of 30–290 µmol m−2 s−1 in full-strength, half-strength, and blank nutrient solutions. The light response for shoot height and the first internode length was mainly influenced by blue light. We observed a rapid decline in growth between 6–20 µmol m−2 s−1 blue photon irradiance. The shoot height and first internode length did not change significantly at higher blue light intensities. The lengths of the first internode and the root dry mass did depend on the nutrient solution strength. All other growth traits, including stem diameter, leaf size, shoot mass, root mass, and SPAD readings, showed a linear correlation with PPFD and electrical conductivity. The leaf mass and root mass ratios indicated that soybeans adopt a nutrient search strategy by giving preference for root growth while increasing shoot height at the expense of the shoot diameter in conditions of low nutrient availability and low light intensity. The functional relationships determined in the experiments provide valuable inputs to plant growth models. The methodology we employed could also be used to study other plant species and to investigate the interactive effects of specific nutrients and lighting conditions. Full article
(This article belongs to the Special Issue Advancement in Controlled Environment Agriculture (CEA) Automation and Crop Management)
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21 pages, 5715 KiB  
Article
Evaluation of Machine Learning Models for Stress Symptom Classification of Cucumber Seedlings Grown in a Controlled Environment
by Kyu-Ho Lee, Samsuzzaman, Md Nasim Reza, Sumaiya Islam, Shahriar Ahmed, Yeon Jin Cho, Dong Hee Noh and Sun-Ok Chung
Agronomy 2025, 15(1), 90; https://doi.org/10.3390/agronomy15010090 - 31 Dec 2024
Cited by 1 | Viewed by 964
Abstract
Stress by unfavorable environmental conditions, including temperature, light intensity, and photoperiod, significantly impact early-stage growth in crops, such as cucumber seedlings, often resulting in yield reduction and quality degradation. Advanced machine learning (ML) models combined with image-based analysis offer promising solutions for precise, [...] Read more.
Stress by unfavorable environmental conditions, including temperature, light intensity, and photoperiod, significantly impact early-stage growth in crops, such as cucumber seedlings, often resulting in yield reduction and quality degradation. Advanced machine learning (ML) models combined with image-based analysis offer promising solutions for precise, non-invasive stress monitoring. This study aims to classify environmental stress symptom levels in cucumber seedlings using ML models by extracting critical color, texture, and morphological features from RGB images. In a controlled plant factory setup, two-week-old cucumber seedlings were subjected to varied environmental conditions across five chambers with differing temperatures (15, 20, 25, and 30 °C), light intensities (50, 250, and 450 µmol m−2 s−1), and day-night cycles (8/16, 10/14, and 16/8 h). A cost-effective RGB camera, integrated with a microcontroller, captured images from the top of the seedlings over a two-week period, from which sequential forward floating selection (SFFS) and correlation matrices were used to streamline feature extraction. Four ML classifiers: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF), were trained to detect stress symptoms based on selected features, highlighting that stress symptoms were detectable after day 4. KNN achieved the highest accuracy at 0.94 (94%), followed closely by SVM and RF, both at 93%, while NB reached 88%. Findings suggested that color and texture features were critical indicators of stress, and that the KNN model, with optimized hyperparameters, provided a reliable classification for stress symptom monitoring for seedlings under controlled environments. This study highlights the potential of ML-driven stress symptom detection models for controlled seedling production, enabling real-time decision-making to optimize crop health and productivity. Full article
(This article belongs to the Special Issue Advancement in Controlled Environment Agriculture (CEA) Automation and Crop Management)
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32 pages, 22123 KiB  
Article
Automated Seedling Contour Determination and Segmentation Using Support Vector Machine and Image Features
by Samsuzzaman, Md Nasim Reza, Sumaiya Islam, Kyu-Ho Lee, Md Asrakul Haque, Md Razob Ali, Yeon Jin Cho, Dong Hee Noh and Sun-Ok Chung
Agronomy 2024, 14(12), 2940; https://doi.org/10.3390/agronomy14122940 - 10 Dec 2024
Cited by 1 | Viewed by 1029
Abstract
Boundary contour determination during seedling image segmentation is critical for accurate object detection and morphological characterization in agricultural machine vision systems. The traditional manual annotation for segmentation is labor-intensive, time-consuming, and prone to errors, especially in controlled environments with complex backgrounds. These errors [...] Read more.
Boundary contour determination during seedling image segmentation is critical for accurate object detection and morphological characterization in agricultural machine vision systems. The traditional manual annotation for segmentation is labor-intensive, time-consuming, and prone to errors, especially in controlled environments with complex backgrounds. These errors can affect the accuracy of detecting phenotypic traits, like shape, size, and width. To address these issues, this study introduced a method that integrated image features and a support vector machine (SVM) to improve boundary contour determination during segmentation, enabling real-time detection and monitoring. Seedling images (pepper, tomato, cucumber, and watermelon) were captured under various lighting conditions to enhance object–background differentiation. Histogram equalization and noise reduction filters (median and Gaussian) were applied to minimize the illumination effects. The peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) were used to select the clip limit for histogram equalization. The images were analyzed across 18 different color spaces to extract the color features, and six texture features were derived using the gray-level co-occurrence matrix (GLCM) method. To reduce feature overlap, sequential feature selection (SFS) was applied, and the SVM was used for object segmentation. The SVM model achieved 73% segmentation accuracy without SFS and 98% with SFS. Segmentation accuracy for the different seedlings ranged from 81% to 98%, with a low boundary misclassification rate between 0.011 and 0.019. The correlation between the actual and segmented contour areas was strong, with an R2 up to 0.9887. The segmented boundary contour files were converted into annotation files to train a YOLOv8 model, which achieved a precision ranging from 96% to 98.5% and a recall ranging from 96% to 98%. This approach enhanced the segmentation accuracy, reduced manual annotation, and improved the agricultural monitoring systems for plant health management. The future direction involves integrating this system with advanced methods to address overlapping image segmentation challenges, further enhancing the real-time seedling monitoring and optimizing crop management and productivity. Full article
(This article belongs to the Special Issue Advancement in Controlled Environment Agriculture (CEA) Automation and Crop Management)
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20 pages, 4034 KiB  
Article
Influence of Electrical Conductivity on Plant Growth, Nutritional Quality, and Phytochemical Properties of Kale (Brassica napus) and Collard (Brassica oleracea) Grown Using Hydroponics
by Teng Yang, Uttara Samarakoon, James Altland and Peter Ling
Agronomy 2024, 14(11), 2704; https://doi.org/10.3390/agronomy14112704 - 16 Nov 2024
Viewed by 1842
Abstract
Kale (Brassica napus) and collard (Brassica oleracea) are two leafy greens in the family Brassicaceae. The leaves are rich sources of numerous health-beneficial compounds and are commonly used either fresh or cooked. This study aimed to optimize the nutrient [...] Read more.
Kale (Brassica napus) and collard (Brassica oleracea) are two leafy greens in the family Brassicaceae. The leaves are rich sources of numerous health-beneficial compounds and are commonly used either fresh or cooked. This study aimed to optimize the nutrient management of kale and collard in hydroponic production for greater yield and crop quality. ‘Red Russian’ kale and ‘Flash F1’ collard were grown for 4 weeks after transplanting in a double polyethylene-plastic-covered greenhouse using a nutrient film technique (NFT) system with 18 channels. Kale and collard were alternately grown in each channel at four different electrical conductivity (EC) levels (1.2, 1.5, 1.8, and 2.1 mS·cm−1). Fresh and dry yields of kale increased linearly with increasing EC levels, while those of collard did not increase when EC was higher than 1.8 mS·cm−1. Kale leaves had significantly higher P, K, Mn, Zn, Cu, and B than the collard at all EC levels. Additionally, mineral nutrients (except N and Zn) in leaf tissue were highest at EC 1.5 and EC 1.8 in both the kale and collard. However, the changing trend of the total N and NO3- of the leaves showed a linear trend; these levels were highest under EC 2.1, followed by EC 1.8 and EC 1.5. EC levels also affected phytochemical accumulation in leaf tissue. In general, the kale leaves had significantly higher total anthocyanin, vitamin C, phenolic compounds, and glucosinolates but lower total chlorophylls and carotenoids than the collard. In addition, although EC levels affected neither the total chlorophyll or carotenoid content in kale nor glucosinolate content in either kale or collard, other important health-beneficial compounds (especially vitamin C, anthocyanin, and phenolic compounds) in kale and collard leaves reduced with the increasing EC levels. In conclusion, the kale leaf had more nutritional and phytochemical compounds than the collard. An EC level of 1.8 mS·cm−1 was the optimum EC level for the collard, while the kale yielded more at 2.1 mS·cm−1. Further investigations are needed to optimize nitrogen nutrition for hydroponically grown kale. Full article
(This article belongs to the Special Issue Advancement in Controlled Environment Agriculture (CEA) Automation and Crop Management)
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14 pages, 5419 KiB  
Article
Strategic Light Use Efficiency Optimization of Hydroponic Lettuce Exposed to Different Photosynthetic Photon Flux Densities
by Peyton Lou Palsha, Marc W. van Iersel, Ryan William Dickson, Lynne Seymour, Melanie Yelton, Kuan Qin and Rhuanito Soranz Ferrarezi
Agronomy 2024, 14(10), 2281; https://doi.org/10.3390/agronomy14102281 - 4 Oct 2024
Cited by 2 | Viewed by 2228
Abstract
Light use efficiency characterizes the ability of a crop to convert radiation into biomass. Determining optimum cultivar-specific photosynthetic photon flux density (PPFD) values from sole-source lighting can be used to optimize leaf expansion, maximize biomass, and shorten the production period. This study evaluated [...] Read more.
Light use efficiency characterizes the ability of a crop to convert radiation into biomass. Determining optimum cultivar-specific photosynthetic photon flux density (PPFD) values from sole-source lighting can be used to optimize leaf expansion, maximize biomass, and shorten the production period. This study evaluated the growth of hydroponic lettuce (Lactuca sativa) ‘Rex’ cultivated under different PPFD levels using sole-source lighting. At lower PPFD levels of 201 to 292 µmol·m−2·s−1, the plant projected canopy size (PCS) and specific leaf area increased to enhance light capture by 36.2% as compared to higher PPFD levels (333 and 413 µmol·m−2·s−1), while plants exhibited 10.3% lower canopy overlap ratio and 27.8% lower shoot dry weights. Both low and high PPFD conditions lead to a similar trend in PCS among plants. Light use efficiency was not a major factor in influencing lettuce growth. Instead, the critical factor was the total incident light the plants received. This study showcased the importance of incident light and PPFD on the growth, morphology, and biomass accumulation in lettuce. Full article
(This article belongs to the Special Issue Advancement in Controlled Environment Agriculture (CEA) Automation and Crop Management)
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11 pages, 2030 KiB  
Article
Sufficient Light Intensity Is Required for the Drought Responses in Sweet Basil (Ocimum basilicum L.)
by Gyeongmin Lee and Jongyun Kim
Agronomy 2024, 14(9), 2101; https://doi.org/10.3390/agronomy14092101 - 15 Sep 2024
Cited by 1 | Viewed by 1331
Abstract
Various environmental factors not only affect plant growth and physiological responses individually but also interact with each other. To examine the impact of light intensity on the drought responses of sweet basil, plants were subjected to maintenance of two substrate volumetric water contents [...] Read more.
Various environmental factors not only affect plant growth and physiological responses individually but also interact with each other. To examine the impact of light intensity on the drought responses of sweet basil, plants were subjected to maintenance of two substrate volumetric water contents (VWC) using a sensor-based automated irrigation system under two distinct light intensities. The VWC threshold was set to either a dry (0.2 m3·m−3) or sufficiently wet condition (0.6 m3·m−3) under low (170 μmol·m−2·s−1) or high light intensities (500 μmol·m−2·s−1). The growth and physiological responses of sweet basil (Ocimum basilicum L.) were observed over 21 days in the four treatment groups, where the combination of two environmental factors was analyzed. Under high light intensity, sweet basil showed lower Fv/Fm and quantum yield of PSII, compared to that under low light intensity, regardless of drought treatment. Fourteen days after drought treatment under high light intensity, stomatal conductance and the photosynthetic rate significantly reduced. Whereas plants under low light intensity showed similar stomatal conductance and photosynthetic rates regardless of drought treatment. Assessment of shoot and root dry weights revealed that plant growth decline caused by drought was more pronounced under high light intensity than under low light intensity. Thus, sweet basil showed significant declines in growth and physiological responses owing to drought only under high light intensity; no significant changes were observed under low light intensity. Full article
(This article belongs to the Special Issue Advancement in Controlled Environment Agriculture (CEA) Automation and Crop Management)
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