IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2023i1p29-d1306288.html
   My bibliography  Save this article

MSGV-YOLOv7: A Lightweight Pineapple Detection Method

Author

Listed:
  • Rihong Zhang

    (College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China)

  • Zejun Huang

    (College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China)

  • Yuling Zhang

    (Shantou Agricultural Product Quality and Safety Center, Shantou 515071, China)

  • Zhong Xue

    (South Subtropical Crops Research Institute, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang 524091, China)

  • Xiaomin Li

    (College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China)

Abstract

In order to optimize the efficiency of pineapple harvesting robots in recognition and target detection, this paper introduces a lightweight pineapple detection model, namely MSGV-YOLOv7. This model adopts MobileOne as the innovative backbone network and uses thin neck as the neck network. The enhancements in these architectures have significantly improved the ability of feature extraction and fusion, thereby speeding up the detection rate. Empirical results indicated that MSGV-YOLOv7 surpassed the original YOLOv7 with a 1.98% increase in precision, 1.35% increase in recall rate, and 3.03% increase in mAP , while the real-time detection speed reached 17.52 frames per second. Compared with Faster R-CNN and YOLOv5n, the mAP of this model increased by 14.89% and 5.22%, respectively, while the real-time detection speed increased by approximately 2.18 times and 1.58 times, respectively. The application of image visualization testing has verified the results, confirming that the MSGV-YOLOv7 model successfully and precisely identified the unique features of pineapples. The proposed pineapple detection method presents significant potential for broad-scale implementation. It is expected to notably reduce both the time and economic costs associated with pineapple harvesting operations.

Suggested Citation

  • Rihong Zhang & Zejun Huang & Yuling Zhang & Zhong Xue & Xiaomin Li, 2023. "MSGV-YOLOv7: A Lightweight Pineapple Detection Method," Agriculture, MDPI, vol. 14(1), pages 1-16, December.
    • Handle: RePEc:gam:jagris:v:14:y:2023:i:1:p:29-:d:1306288
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/1/29/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/14/1/29/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zuhui Huang & Qiao Liang, 2018. "Agricultural organizations and the role of farmer cooperatives in China since 1978: past and future," China Agricultural Economic Review, Emerald Group Publishing Limited, vol. 10(1), pages 48-64, February.
    2. Zuhui Huang & Qiao Liang, 2018. "Agricultural organizations and the role of farmer cooperatives in China since 1978: past and future," China Agricultural Economic Review, Emerald Group Publishing Limited, vol. 10(1), pages 48-64, February.
    3. Huawei Yang & Yinzeng Liu & Shaowei Wang & Huixing Qu & Ning Li & Jie Wu & Yinfa Yan & Hongjian Zhang & Jinxing Wang & Jianfeng Qiu, 2023. "Improved Apple Fruit Target Recognition Method Based on YOLOv7 Model," Agriculture, MDPI, vol. 13(7), pages 1-21, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xuelan Li & Rui Guan, 2023. "How Does Agricultural Mechanization Service Affect Agricultural Green Transformation in China?," IJERPH, MDPI, vol. 20(2), pages 1-23, January.
    2. Feifei Chen & Zhigang Xu & Yufeng Luo, 2023. "False prosperity: Rethinking government support for farmers’ cooperatives in China," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 94(3), pages 905-920, September.
    3. Jiang, Meishan & Li, Jingrong & Mi, Yunsheng, 2024. "Farmers’ cooperatives and smallholder farmers’ access to credit: Evidence from China," Journal of Asian Economics, Elsevier, vol. 92(C).
    4. Zheng, Linyi, 2024. "Big hands holding small hands: The role of new agricultural operating entities in farmland abandonment," Food Policy, Elsevier, vol. 123(C).
    5. Qiao Liang & Kangwei Ma & Wenhao Liu, 2023. "The role of farmer cooperatives in promoting environmentally sustainable agricultural development in China: A review," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 94(3), pages 741-759, September.
    6. Chen, Chen & Gan, Christopher & Li, Junpeng & Lu, Yao & Rahut, Dil, 2023. "Linking farmers to markets: Does cooperative membership facilitate e-commerce adoption and income growth in rural China?," Economic Analysis and Policy, Elsevier, vol. 80(C), pages 1155-1170.
    7. Zhiping Huang & Tianran Wang & Na Li, 2022. "Reciprocal and Symbiotic: Family Farms’ Operational Performance and Long-Term Cooperation of Entities in the Agricultural Industrial Chain—From the Evidence of Xinjiang in China," Sustainability, MDPI, vol. 15(1), pages 1-17, December.
    8. Hongyun Zheng & Puneet Vatsa & Wanglin Ma & Dil Bahadur Rahut, 2023. "Does agricultural cooperative membership influence off‐farm work decisions of farm couples?," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 94(3), pages 831-855, September.
    9. Jie Yu & Fei You & Jian Wang & Zishan Wang, 2023. "Evolution Modes of Chili Pepper Industry Clusters under the Perspective of Social Network—An Example from Xinfu District, Xinzhou, Shanxi Province," Sustainability, MDPI, vol. 15(6), pages 1-14, March.
    10. Qingzhi Sun & Guanyi Yin & Wei Wei & Zhan Zhang & Guanghao Li & Shenghao Zhu, 2024. "Social Network Analysis of Farmers after the Private Cooperatives’ “Intervention” in a Rural Area of China—A Case Study of the XiangX Cooperative in Shandong Province," Agriculture, MDPI, vol. 14(5), pages 1-22, April.
    11. Menglu Li & Shemei Zhang & Nawab Khan, 2024. "Do farmers' professional cooperatives improve agricultural technical efficiency? Evidence using a national‐level dataset of China," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 95(2), pages 363-383, June.
    12. Wen Xiang & Jianzhong Gao, 2023. "Do Not Be Anticlimactic: Farmers’ Behavior in the Sustainable Application of Green Agricultural Technology—A Perceived Value and Government Support Perspective," Agriculture, MDPI, vol. 13(2), pages 1-24, January.
    13. Lishi Mao & Junfeng Song & Siyuan Xu & Degui Yu, 2023. "Impact of Digital Platform Organization on Reducing Green Production Risk to Tackle COVID-19: Evidence from Farmers in Jiangsu China," Agriculture, MDPI, vol. 13(1), pages 1-16, January.
    14. Lei Wu & Chuanjian Li & Yang Gao, 2022. "Regional agricultural cooperatives and subjective wellbeing of rural households in China," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(S2), pages 138-158, November.
    15. Liyan Yu & Jerker Nilsson, 2019. "Social Capital and Financial Capital in Chinese Cooperatives," Sustainability, MDPI, vol. 11(8), pages 1-15, April.
    16. Yuying Liu & Ruiling Shi & Yiting Peng & Wei Wang & Xinhong Fu, 2022. "Impacts of Technology Training Provided by Agricultural Cooperatives on Farmers’ Adoption of Biopesticides in China," Agriculture, MDPI, vol. 12(3), pages 1-17, February.
    17. Yang Zou & Qingbin Wang, 2022. "Impacts of farmer cooperative membership on household income and inequality: Evidence from a household survey in China," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 10(1), pages 1-17, December.
    18. Yanan Huang & Xu Li & Guangsheng Zhang, 2021. "The Impact of Technology Perception and Government Support on E-Commerce Sales Behavior of Farmer Cooperatives: Evidence From Liaoning Province, China," SAGE Open, , vol. 11(2), pages 21582440211, June.
    19. Li, Xinyi & Ito, Junichi, 2021. "An empirical study of land rental development in rural Gansu, China: The role of agricultural cooperatives and transaction costs," Land Use Policy, Elsevier, vol. 109(C).
    20. Ping Dong & Kuo Li & Ming Wang & Feitao Li & Wei Guo & Haiping Si, 2023. "Maize Leaf Compound Disease Recognition Based on Attention Mechanism," Agriculture, MDPI, vol. 14(1), pages 1-22, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:14:y:2023:i:1:p:29-:d:1306288. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.