IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v15y2025i5p483-d1598248.html
   My bibliography  Save this article

Research on Apple Detection and Tracking Count in Complex Scenes Based on the Improved YOLOv7-Tiny-PDE

Author

Listed:
  • Dongxuan Cao

    (North China Institute of Aerospace Engineering, Langfang 065000, China
    These authors contributed equally to this work.)

  • Wei Luo

    (North China Institute of Aerospace Engineering, Langfang 065000, China
    Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China
    National Joint Engineering Research Center of Space Remote Sensing Information Application Technology, Langfang 065000, China
    These authors contributed equally to this work.)

  • Ruiyin Tang

    (North China Institute of Aerospace Engineering, Langfang 065000, China
    Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China
    National Joint Engineering Research Center of Space Remote Sensing Information Application Technology, Langfang 065000, China)

  • Yuyan Liu

    (North China Institute of Aerospace Engineering, Langfang 065000, China
    Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China
    National Joint Engineering Research Center of Space Remote Sensing Information Application Technology, Langfang 065000, China)

  • Jiasen Zhao

    (North China Institute of Aerospace Engineering, Langfang 065000, China)

  • Xuqing Li

    (North China Institute of Aerospace Engineering, Langfang 065000, China
    Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China
    National Joint Engineering Research Center of Space Remote Sensing Information Application Technology, Langfang 065000, China)

  • Lihua Yuan

    (North China Institute of Aerospace Engineering, Langfang 065000, China
    Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China
    National Joint Engineering Research Center of Space Remote Sensing Information Application Technology, Langfang 065000, China)

Abstract

Accurately detecting apple fruit can crucially assist in estimating the fruit yield in apple orchards in complex scenarios. In such environments, the factors of density, leaf occlusion, and fruit overlap can affect the detection and counting accuracy. This paper proposes an improved YOLOv7-Tiny-PDE network model based on the YOLOv7-Tiny model to detect and count apples from data collected by drones, considering various occlusion and lighting conditions. First, within the backbone network, we replaced the simplified efficient layer aggregation network (ELAN) with partial convolution (PConv), reducing the network parameters and computational redundancy while maintaining the detection accuracy. Second, in the neck network, we used a dynamic detection head to replace the original detection head, effectively suppressing the background interference and capturing the background information more comprehensively, thus enhancing the detection accuracy for occluded targets and improving the fruit feature extraction. To further optimize the model, we replaced the boundary box loss function from CIOU to EIOU. For fruit counting across video frames in complex occlusion scenes, we integrated the improved model with the DeepSort tracking algorithm based on Kalman filtering and motion trajectory prediction with a cascading matching algorithm. According to experimental results, compared with the baseline YOLOv7-Tiny, the improved model reduced the total parameters by 22.2% and computation complexity by 18.3%. Additionally, in data testing, the p -value improved by 0.5%; the R-value rose by 2.7%; the mAP and F1 scores rose by 4% and 1.7%, respectively; and the MOTA value improved by 2%. The improved model is more lightweight and can preserve a high detection accuracy well, and hence, it can be applied to detection and counting tasks in complex orchards and provides a new solution for fruit yield estimation using lightweight devices.

Suggested Citation

  • Dongxuan Cao & Wei Luo & Ruiyin Tang & Yuyan Liu & Jiasen Zhao & Xuqing Li & Lihua Yuan, 2025. "Research on Apple Detection and Tracking Count in Complex Scenes Based on the Improved YOLOv7-Tiny-PDE," Agriculture, MDPI, vol. 15(5), pages 1-26, February.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:5:p:483-:d:1598248
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/15/5/483/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/15/5/483/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. H. W. Kuhn, 1955. "The Hungarian method for the assignment problem," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 2(1‐2), pages 83-97, March.
    2. Wei Ji & Yu Pan & Bo Xu & Juncheng Wang, 2022. "A Real-Time Apple Targets Detection Method for Picking Robot Based on ShufflenetV2-YOLOX," Agriculture, MDPI, vol. 12(6), pages 1-18, 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. András Frank, 2005. "On Kuhn's Hungarian Method—A tribute from Hungary," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(1), pages 2-5, February.
    2. Amit Kumar & Anila Gupta, 2013. "Mehar’s methods for fuzzy assignment problems with restrictions," Fuzzy Information and Engineering, Springer, vol. 5(1), pages 27-44, March.
    3. Parvin Ahmadi & Iman Gholampour & Mahmoud Tabandeh, 2018. "Cluster-based sparse topical coding for topic mining and document clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 537-558, September.
    4. Chenchen Ma & Jing Ouyang & Gongjun Xu, 2023. "Learning Latent and Hierarchical Structures in Cognitive Diagnosis Models," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 175-207, March.
    5. Aidin Rezaeian & Hamidreza Koosha & Mohammad Ranjbar & Saeed Poormoaied, 2024. "The assignment of project managers to projects in an uncertain dynamic environment," Annals of Operations Research, Springer, vol. 341(2), pages 1107-1134, October.
    6. Tran Hoang Hai, 2020. "Estimation of volatility causality in structural autoregressions with heteroskedasticity using independent component analysis," Statistical Papers, Springer, vol. 61(1), pages 1-16, February.
    7. Caplin, Andrew & Leahy, John, 2020. "Comparative statics in markets for indivisible goods," Journal of Mathematical Economics, Elsevier, vol. 90(C), pages 80-94.
    8. Biró, Péter & Gudmundsson, Jens, 2021. "Complexity of finding Pareto-efficient allocations of highest welfare," European Journal of Operational Research, Elsevier, vol. 291(2), pages 614-628.
    9. Ana Viana & Xenia Klimentova & Péter Biró & Flip Klijn, 2021. "Shapley-Scarf Housing Markets: Respecting Improvement, Integer Programming, and Kidney Exchange," Working Papers 1235, Barcelona School of Economics.
    10. Michal Brylinski, 2014. "eMatchSite: Sequence Order-Independent Structure Alignments of Ligand Binding Pockets in Protein Models," PLOS Computational Biology, Public Library of Science, vol. 10(9), pages 1-15, September.
    11. Juan G. Carvajal-Patiño & Vincent Mallet & David Becerra & Luis Fernando Niño Vasquez & Carlos Oliver & Jérôme Waldispühl, 2025. "RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    12. Chiwei Yan & Helin Zhu & Nikita Korolko & Dawn Woodard, 2020. "Dynamic pricing and matching in ride‐hailing platforms," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(8), pages 705-724, December.
    13. Fanrong Xie & Anuj Sharma & Zuoan Li, 2022. "An alternate approach to solve two-level priority based assignment problem," Computational Optimization and Applications, Springer, vol. 81(2), pages 613-656, March.
    14. Yan, Pengyu & Lee, Chung-Yee & Chu, Chengbin & Chen, Cynthia & Luo, Zhiqin, 2021. "Matching and pricing in ride-sharing: Optimality, stability, and financial sustainability," Omega, Elsevier, vol. 102(C).
    15. Zhenwei Liang & Xingyue Xu & Deyong Yang & Yanbin Liu, 2025. "The Development of a Lightweight DE-YOLO Model for Detecting Impurities and Broken Rice Grains," Agriculture, MDPI, vol. 15(8), pages 1-17, April.
    16. Morrill, Thayer & Roth, Alvin E., 2024. "Top trading cycles," Journal of Mathematical Economics, Elsevier, vol. 112(C).
    17. Bo Cowgill & Jonathan M. V. Davis & B. Pablo Montagnes & Patryk Perkowski, 2025. "Stable Matching on the Job? Theory and Evidence on Internal Talent Markets," Management Science, INFORMS, vol. 71(3), pages 2508-2526, March.
    18. Guo, Yuhan & Zhang, Yu & Boulaksil, Youssef, 2021. "Real-time ride-sharing framework with dynamic timeframe and anticipation-based migration," European Journal of Operational Research, Elsevier, vol. 288(3), pages 810-828.
    19. Demetrescu, Camil & Lupia, Francesco & Mendicelli, Angelo & Ribichini, Andrea & Scarcello, Francesco & Schaerf, Marco, 2019. "On the Shapley value and its application to the Italian VQR research assessment exercise," Journal of Informetrics, Elsevier, vol. 13(1), pages 87-104.
    20. Christian Billing & Florian Jaehn & Thomas Wensing, 2020. "Fair task allocation problem," Annals of Operations Research, Springer, vol. 284(1), pages 131-146, January.

    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:15:y:2025:i:5:p:483-:d:1598248. 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.