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Agriculture
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Automation Strategy Using Machine Learning in Horticultural Crop Cultivation
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Automation Strategy Using Machine Learning in Horticultural Crop Cultivation

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 25 November 2025 | Viewed by 2099

Special Issue Editors

Dr. Dong Sub Kim

E-Mail Website
Guest Editor
Department of Horticulture, Kongju National University, Yesan 32588, Republic of Korea
Interests: smart farm; image analysis; artificial intelligence; hydroponics; IPM; disease detection
Special Issues, Collections and Topics in MDPI journals
Dr. Sunghyun Yoon

E-Mail Website
Guest Editor
Department of Artificial Intelligence, Kongju National University, Cheonan 31080, Republic of Korea
Interests: machine learning; time series modeling; acoustic modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Young-Yeol Cho

E-Mail Website
Guest Editor
Department of Horticultural Science, Jeju National University, Jeju 63243, Republic of Korea
Interests: vertical farm (plant factory); facility horticulture; hydroponics; growth modeling; smart farm (precision agriculture); artificial intelligence; Arduino

Special Issue Information

Dear Colleagues,

The use of artificial intelligence in agriculture is no longer unfamiliar. In particular, it is being used very actively in the horticulture industry, where smart farm-related studies are progressing very well. However, there is still room for artificial intelligence to play other important roles in many more areas of horticultural crop production.

This Special Issue focuses on building automated systems using machine learning for use throughout the entire crop production process, from sowing to harvest, and for cultivating crops within the system to improve crop productivity and quality. This Special Issue will include interdisciplinary studies embracing agriculture with disciplines of biology, computer science, data science, and engineering. Research articles will cover a broad range of crops from vegetables, ornamental plants, and trees. All types of articles, such as original research, opinions, and reviews are welcome.

Dr. Dong Sub Kim
Dr. Sunghyun Yoon
Prof. Dr. Young-Yeol Cho
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. Agriculture 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

  • smart farm
  • artificial intelligence
  • big data
  • crop cultivation
  • productivity
  • automation
  • sensor

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

Published Papers (3 papers)

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Research

23 pages, 2568 KiB  
Article
Reinforcement Learning-Driven Digital Twin for Zero-Delay Communication in Smart Greenhouse Robotics
by Cristian Bua, Luca Borgianni, Davide Adami and Stefano Giordano
Agriculture 2025, 15(12), 1290; https://doi.org/10.3390/agriculture15121290 - 15 Jun 2025
Viewed by 288
Abstract
This study presents a networked cyber-physical architecture that integrates a Reinforcement Learning-based Digital Twin (DT) to enable zero-delay interaction between physical and digital components in smart agriculture. The proposed system allows real-time remote control of a robotic arm inside a hydroponic greenhouse, using [...] Read more.
This study presents a networked cyber-physical architecture that integrates a Reinforcement Learning-based Digital Twin (DT) to enable zero-delay interaction between physical and digital components in smart agriculture. The proposed system allows real-time remote control of a robotic arm inside a hydroponic greenhouse, using a sensor-equipped Wearable Glove (SWG) for hand motion capture. The DT operates in three coordinated modes: Real2Digital, Digital2Real, and Digital2Digital, supporting bidirectional synchronization and predictive simulation. A core innovation lies in the use of a Reinforcement Learning model to anticipate hand motions, thereby compensating for network latency and enhancing the responsiveness of the virtual–physical interaction. The architecture was experimentally validated through a detailed communication delay analysis, covering sensing, data processing, network transmission, and 3D rendering. While results confirm the system’s effectiveness under typical conditions, performance may vary under unstable network scenarios. This work represents a promising step toward real-time adaptive DTs in complex smart greenhouse environments. Full article
(This article belongs to the Special Issue Automation Strategy Using Machine Learning in Horticultural Crop Cultivation)
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15 pages, 5185 KiB  
Article
Research on Recognition of Green Sichuan Pepper Clusters and Cutting-Point Localization in Complex Environments
by Qi Niu, Wenjun Ma, Rongxiang Diao, Wei Yu, Chunlei Wang, Hui Li, Lihong Wang, Chengsong Li and Pei Wang
Agriculture 2025, 15(10), 1079; https://doi.org/10.3390/agriculture15101079 - 16 May 2025
Viewed by 343
Abstract
The harvesting of green Sichuan pepper remains heavily reliant on manual field operations, but automation can enhance the efficiency, quality, and sustainability of the process. However, challenges such as intertwined branches, dense foliage, and overlapping pepper clusters hinder intelligent harvesting by causing inaccuracies [...] Read more.
The harvesting of green Sichuan pepper remains heavily reliant on manual field operations, but automation can enhance the efficiency, quality, and sustainability of the process. However, challenges such as intertwined branches, dense foliage, and overlapping pepper clusters hinder intelligent harvesting by causing inaccuracies in target recognition and localization. This study compared the performance of multiple You Only Look Once (YOLO) algorithms for recognition and proposed a cluster segmentation method based on K-means++ and a cutting-point localization strategy using geometry-based iterative optimization. A dataset containing 14,504 training images under diverse lighting and occlusion scenarios was constructed. Comparative experiments on YOLOv5s, YOLOv8s, and YOLOv11s models revealed that YOLOv11s achieved a recall of 0.91 in leaf-occluded environments, marking a 21.3% improvement over YOLOv5s, with a detection speed of 28 Frames Per Second(FPS). A K-means++-based cluster separation algorithm (K = 1~10, optimized via the elbow method) was developed and was combined with OpenCV to iteratively solve the minimum circumscribed triangle vertices. The longest median extension line of the triangle was dynamically determined to be the cutting point. The experimental results demonstrated an average cutting-point deviation of 20 mm and a valid cutting-point ratio of 69.23%. This research provides a robust visual solution for intelligent green Sichuan pepper harvesting equipment, offering both theoretical and engineering significance for advancing the automated harvesting of Sichuan pepper (Zanthoxylum schinifolium) as a specialty economic crop. Full article
(This article belongs to the Special Issue Automation Strategy Using Machine Learning in Horticultural Crop Cultivation)
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30 pages, 16384 KiB  
Article
Determination of Optimal Dataset Characteristics for Improving YOLO Performance in Agricultural Object Detection
by Jisu Song, Dongseok Kim, Eunji Jeong and Jaesung Park
Agriculture 2025, 15(7), 731; https://doi.org/10.3390/agriculture15070731 - 28 Mar 2025
Cited by 2 | Viewed by 1109
Abstract
Recent advances in artificial intelligence and computer vision have led to significant progress in the use of agricultural technologies for yield prediction, pest detection, and real-time monitoring of plant conditions. However, collecting large-scale, high-quality image datasets in the agriculture sector remains challenging, particularly [...] Read more.
Recent advances in artificial intelligence and computer vision have led to significant progress in the use of agricultural technologies for yield prediction, pest detection, and real-time monitoring of plant conditions. However, collecting large-scale, high-quality image datasets in the agriculture sector remains challenging, particularly for specialized datasets such as plant disease images. This study analyzed the effects of the image size (320–640+) and the number of labels on the performance of a YOLO-based object detection model using diverse agricultural datasets for strawberries, tomatoes, chilies, and peppers. Model performance was evaluated using the intersection over union and average precision (AP), where the AP curve was smoothed using the Savitzky–Golay filter and EEM. The results revealed that increasing the number of labels improved the model performance to a certain degree, after which the performance gradually diminished. Furthermore, while increasing the image size from 320 to 640 substantially enhanced the model performance, additional increases beyond 640 yielded only marginal improvements. However, the training time and graphics processing unit usage scaled linearly with increasing image sizes, as larger size images require greater computational resources. These findings underscore the importance of an optimal strategy for selecting the image size and label quantity under resource constraints in real-world model development. Full article
(This article belongs to the Special Issue Automation Strategy Using Machine Learning in Horticultural Crop Cultivation)
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