Application of Smart Technology and Equipment in Horticulture
1
Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
2
School of Mechatronics Engineering and Automation, Foshan University, Foshan 528000, China
*
Author to whom correspondence should be addressed.
Horticulturae 2024, 10(7), 676; https://doi.org/10.3390/horticulturae10070676
Submission received: 19 June 2024
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Accepted: 20 June 2024
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Published: 26 June 2024
(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture)
Horticulture, as an important component of modern agriculture, plays a significant role in beautifying the environment but also in enriching human nutrition [1]. With the widespread application of intelligent devices in various aspects of agriculture, horticulture, which requires more refined management and operations, is also moving towards intelligence and intensification [2]. Therefore, developing intelligent technologies and equipment that can assist in horticulture, beautify the environment, and support plant cultivation and breeding has become a current research focus [3]. This Special Issue, “Application of Intelligent Technology and Equipment in Horticulture”, highlights the latest intelligent technologies and equipment in areas such as environmental beautification, agricultural intensification, and plant species cultivation.
Machine learning and deep learning technologies have vast potential in horticulture, and this Special Issue explores some specific application scenarios. By analyzing large amounts of data, machine learning algorithms can help farmers anticipate crop diseases and optimize prevention plans, thereby reducing the use of chemical agents and protecting the ecological environment [4]. Additionally, the application of machine learning in soil and underground structure detection can help improve planting schemes, enhance soil utilization efficiency, and increase crop yields. Through advanced image recognition technology, deep learning algorithms can achieve real-time monitoring and management of key stages such as crop growth, pest and disease control, and flowering periods.
The application of intelligent devices and sensor technology in horticulture allows for real-time monitoring and regulation of environmental parameters and crop growth conditions. These devices can provide precise data support, helping farmers optimize planting strategies, improve resource utilization efficiency, and reduce waste [5]. This Special Issue includes research on the application of sensors in horticulture. For example, wearable sensor technology can monitor the moisture and nutrient status of plants in real-time, allowing for timely adjustments to irrigation and fertilization plans, thereby ensuring the healthy growth of crops.
Plant growth simulation and management optimization technology are additional research focuses of this Special Issue. By establishing mathematical models and simulation algorithms, it is possible to accurately predict and manage various parameters during the crop growth process. This helps farmers better understand the growth patterns of crops and optimize planting density, fertilization amounts, and irrigation frequency, thereby improving crop yield and quality. Additionally, these simulation models can be used for new variety breeding and cultivation technique improvements, promoting the development of horticultural production towards more scientific and precise methods [6].
This Special Issue showcases the multifaceted applications of intelligent technology and equipment in horticulture, covering several cutting-edge fields such as artificial intelligence, deep learning, sensor technology, and plant growth simulation. Through these studies, various aspects of horticultural production are optimized, not only enhancing production efficiency and crop quality but also promoting the precision and intelligence of horticultural management. In the future, with the continuous advancement of intelligent technology, the horticulture sector will experience more innovations and transformations, providing robust technical support for the sustainable development of modern agriculture. The widespread adoption of intelligent devices and the increased success rate of breeding will further promote the intelligent and intensive development of horticulture [7].
Conflicts of Interest
The author declares no conflicts of interest.
List of Contributions
This Special Issue includes 11 research articles and 1 review article, comprehensively exploring the application of intelligent technology and equipment in horticulture. These contributions have delved into various aspects, such as the application of artificial intelligence in plant pathology, crop management technologies based on machine learning and deep learning, the application of intelligent devices and sensors in environmental monitoring and crop growth, and research on plant growth simulation and management optimization. The Guest Editor would like to thank each author for sharing their knowledge and providing their fascinating research findings to this Special Issue. In addition, they also appreciate the valuable assistance provided by the horticultural industry to make this Special Issue a reality.
- González-Rodríguez, V.E.; Izquierdo-Bueno, I.; Cantoral, J.M.; Carbú, M.; Garrido, C. Artificial Intelligence: A Promising Tool for Application in Phytopathology. Horticulturae 2024, 10, 197. https://doi.org/10.3390/horticulturae10030197.
- Qiu, Z.; Zeng, J.; Tang, W.; Yang, H.; Lu, J.; Zhao, Z. Research on Real-Time Automatic Picking of Ground-Penetrating Radar Image Features by Using Machine Learning. Horticulturae 2022, 8, 1116. https://doi.org/10.3390/horticulturae8121116.
- Meng, Y.; Zhai, X.; Zhang, J.; Wei, J.; Zhu, J.; Zhang, T. HeLoDL: Hedgerow Localization Based on Deep Learning. Horticulturae 2023, 9, 227. https://doi.org/10.3390/horticulturae9020227.
- Zhou, H.; Ou, J.; Meng, P.; Tong, J.; Ye, H.; Li, Z. Reasearch on Kiwi Fruit Flower Recognition for Efficient Pollination Based on an Improved YOLOv5 Algorithm. Horticulturae 2023, 9, 400. https://doi.org/10.3390/horticulturae9030400.
- Chen, J.; Ma, A.; Huang, L.; Su, Y.; Li, W.; Zhang, H.; Wang, Z. GA-YOLO: A Lightweight YOLO Model for Dense and Occluded Grape Target Detection. Horticulturae 2023, 9, 443. https://doi.org/10.3390/horticulturae9040443.
- Liang, H.; Zhu, J.; Ge, M.; Wang, D.; Liu, K.; Zhou, M.; Sun, Y.; Zhang, Q.; Jiang, K.; Shi, X. A Comparative Analysis of the Grafting Efficiency of Watermelon with a Grafting Machine. Horticulturae 2023, 9, 600. https://doi.org/10.3390/horticulturae9050600.
- Zhao, R.; Liao, C.; Yu, T.; Chen, J.; Li, Y.; Lin, G.; Huan, X.; Wang, Z. IMVTS: A Detection Model for Multi-Varieties of Famous Tea Sprouts Based on Deep Learning. Horticulturae 2023, 9, 819. https://doi.org/10.3390/horticulturae9070819.
- Guo, S.; Wu, L.; Cao, X.; Sun, X.; Cao, Y.; Li, Y.; Shi, H. Simulation Model Construction of Plant Height and Leaf Area Index Based on the Overground Weight of Greenhouse Tomato: Device Development and Application. Horticulturae 2024, 10, 270. https://doi.org/10.3390/horticulturae10030270.
- Huang, Z.; Ou, C.; Guo, Z.; Ye, L.; Li, J. Human-Following Strategy for Orchard Mobile Robot Based on the KCF-YOLO Algorithm. Horticulturae 2024, 10, 348. https://doi.org/10.3390/horticulturae10040348.
- Zhang, X.; Kong, L.; Lu, H.; Feng, Q.; Li, T.; Zhang, Q.; Jiang, K. An Original UV Adhesive Watermelon Grafting Method, the Grafting Device, and Experimental Verification. Horticulturae 2024, 10, 365. https://doi.org/10.3390/horticulturae10040365.
- Wang, B.; Yang, H.; Li, L.; Zhang, S. Non-Destructive Detection of Cerasus Humilis Fruit Quality by Hyperspectral Imaging Combined with Chemometric Method. Horticulturae 2024, 10, 519. https://doi.org/10.3390/horticulturae10050519.
- Zhang, R.; Chai, Y.; Liang, X.; Liu, X.; Wang, X.; Hu, Z. A New Plant-Wearable Sap Flow Sensor Reveals the Dynamic Water Distribution during Watermelon Fruit Development. Horticulturae 2024, 10, 649. https://doi.org/10.3390/horticulturae10060649.
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MDPI and ACS Style
Wang, C.; Luo, L. Application of Smart Technology and Equipment in Horticulture. Horticulturae 2024, 10, 676. https://doi.org/10.3390/horticulturae10070676
AMA Style
Wang C, Luo L. Application of Smart Technology and Equipment in Horticulture. Horticulturae. 2024; 10(7):676. https://doi.org/10.3390/horticulturae10070676
Chicago/Turabian StyleWang, Chenglin, and Lufeng Luo. 2024. "Application of Smart Technology and Equipment in Horticulture" Horticulturae 10, no. 7: 676. https://doi.org/10.3390/horticulturae10070676
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