Applied Artificial Intelligence in Digital Horticulture: Practices and Innovations

A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Postharvest Biology, Quality, Safety, and Technology".

Deadline for manuscript submissions: 5 December 2024 | Viewed by 1592

Special Issue Editors


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Guest Editor
Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, Tarim University, Alaer 843300, China
Interests: sustainable agriculture; fruit quality; non-destructive detection; machine learning

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Guest Editor
College of Mechanical Electrification Engineering, Tarim University, Alaer 843300, China
Interests: intelligent agriculture; post-harvest; horticulture; nut

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Guest Editor
College of Mechanical Electrification Engineering, Tarim University, Alaer 843300, China
Interests: sustainable agriculture; post-harvest; fruit ripening; non-destructive detection

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Guest Editor
Department of Soil and Agri-Food Engineering, Universite Laval, Québec, QC G1V 0A6, Canada
Interests: water erosion; sediment transport; hydrology; environmental modeling; numer-ical methods; water resources
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Special Issue Information

Dear Colleagues,

In the development of modern agriculture, artificial intelligence has achieved initial results in the field of horticulture, and the progress of its research and application continues to promote the development of precision agriculture. The integrated application of artificial intelligence has involved many links such as monitoring, forecasting, decision-making and execution, forming an intelligent monitoring and management system. Although the application of artificial intelligence in digital gardening is promising, the challenges cannot be ignored.

This Special Issue titled "Applied Artificial Intelligence in Digital Horticulture: Practices and Innovations" focuses on exploring the application and innovation of artificial intelligence technology in the field of digital horticulture, aiming to promote the progress and sustainable development of agricultural science and technology. This Special Issue introduces the technological innovations of artificial intelligence in plant growth monitoring, automatic management, precision irrigation, non-destructive testing and evaluation of fruit and vegetable quality, intelligent perception control systems and cloud platform construction. The journal encourages original research across disciplines, with a particular emphasis on research methods that combine theory and practice. Scholars from all walks of life are welcome to contribute to promote the development of artificial intelligence in digital horticulture, in order to achieve an efficient and intelligent agricultural production system.

Dr. Yang Liu
Prof. Hong Zhang
Prof. Dr. Haipeng Lan
Dr. Silvio José Gumiere
Guest Editors

Manuscript Submission Information

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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. Horticulturae is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • artificial intelligence
  • digital gardening
  • automated management
  • non-destructive testing
  • intelligent perception

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Published Papers (1 paper)

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Research

21 pages, 45821 KiB  
Article
OMC-YOLO: A Lightweight Grading Detection Method for Oyster Mushrooms
by Lei Shi, Zhanchen Wei, Haohai You, Jiali Wang, Zhuo Bai, Helong Yu, Ruiqing Ji and Chunguang Bi
Horticulturae 2024, 10(7), 742; https://doi.org/10.3390/horticulturae10070742 - 14 Jul 2024
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Abstract
In this paper, a lightweight model—OMC-YOLO, improved based on YOLOv8n—is proposed for the automated detection and grading of oyster mushrooms. Aiming at the problems of low efficiency, high costs, and the difficult quality assurance of manual operations in traditional oyster mushroom cultivation, OMC-YOLO [...] Read more.
In this paper, a lightweight model—OMC-YOLO, improved based on YOLOv8n—is proposed for the automated detection and grading of oyster mushrooms. Aiming at the problems of low efficiency, high costs, and the difficult quality assurance of manual operations in traditional oyster mushroom cultivation, OMC-YOLO was improved based on the YOLOv8n model. Specifically, the model introduces deeply separable convolution (DWConv) into the backbone network, integrates the large separated convolution kernel attention mechanism (LSKA) and Slim-Neck structure into the Neck part, and adopts the DIoU loss function for optimization. The experimental results show that on the oyster mushroom dataset, the OMC-YOLO model had a higher detection effect compared to mainstream target detection models such as Faster R-CNN, SSD, YOLOv3-tiny, YOLOv5n, YOLOv6, YOLOv7-tiny, YOLOv8n, YOLOv9-gelan, YOLOv10n, etc., and that the mAP50 value reached 94.95%, which is an improvement of 2.62%. The number of parameters and the computational amount were also reduced by 26%. The model provides technical support for the automatic detection of oyster mushroom grades, which helps in realizing quality control and reducing labor costs and has positive significance for the construction of smart agriculture. Full article
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