Foods and Raw Materials Foods and Raw Materials Foods and Raw Materials 2308-4057 2310-9599 89720 10.21603/2308-4057-2025-2-650 Research Article Research Article Research Article UAV imagery, advanced deep learning, and YOLOv7 object detection model in enhancing citrus yield estimation UAV imagery, advanced deep learning, and YOLOv7 object detection model in enhancing citrus yield estimation https://orcid.org/0009-0006-5525-7956 Daiaeddine Mohamed Jibril Daiaeddine Mohamed Jibril mohamedjibril.daiaeddine@usms.ma https://orcid.org/0009-0000-2675-4810 Badrouss Sara Badrouss Sara https://orcid.org/0000-0003-3976-4588 El Harti Abderrazak El Harti Abderrazak https://orcid.org/0000-0003-4163-6307 Bachaoui El Mostafa Bachaoui El Mostafa https://orcid.org/0000-0002-9448-6165 Biniz Mohamed Biniz Mohamed https://orcid.org/0000-0003-3312-8230 Mouncif Hicham Mouncif Hicham Sultan Moulay Slimane University Beni Mellal Марокко Sultan Moulay Slimane University Beni Mellal Morocco Sultan Moulay Slimane University Beni Mellal Марокко Sultan Moulay Slimane University Beni Mellal Morocco Sultan Moulay Slimane University Beni Mellal Марокко Sultan Moulay Slimane University Beni Mellal Morocco Sultan Moulay Slimane University Beni Mellal Марокко Sultan Moulay Slimane University Beni Mellal Morocco Sultan Moulay Slimane University Beni Mellal Марокко Sultan Moulay Slimane University Beni Mellal Morocco Sultan Moulay Slimane University Beni Mellal Марокко Sultan Moulay Slimane University Beni Mellal Morocco 01 01 2025 01 01 2025 13 2 242 253 04 01 2024 07 05 2024 https://jfrm.ru/en/issues/22898/22915/

Accurate citrus fruit yield and estimation is of utmost importance for precise agricultural management. Unmanned aerial vehicle (UAV) remote-sensing systems present a compelling solution to this problem. These systems capture remote-sensing imagery with both high temporal and spatial resolution, thus empowering farmers with valuable insights for better decisionmaking. This research assessed the potential application of UAV imagery combined with the YOLOv7 object detection model for the precise estimation of citrus yield. Images of citrus trees were captured in their natural field setting using a quadcopter-mounted UAV camera. Data augmentation techniques were applied to enhance the dataset diversity; the original YOLOv7 architecture and training parameters were modified to improve the model’s accuracy in detecting citrus fruits. The test results demonstrated commendable performance, with a precision of 96%, a recall of 100%, and an F1-score of 97.95%. The correlation between the fruit numbers recognized by the algorithm and the actual fruit numbers from 20 sample trees provided the coefficient R2 of 0.98. The strong positive correlation confirmed both the accuracy of the algorithm and the validity of the approach in identifying and quantifying citrus fruits on sample trees.

Accurate citrus fruit yield and estimation is of utmost importance for precise agricultural management. Unmanned aerial vehicle (UAV) remote-sensing systems present a compelling solution to this problem. These systems capture remote-sensing imagery with both high temporal and spatial resolution, thus empowering farmers with valuable insights for better decisionmaking. This research assessed the potential application of UAV imagery combined with the YOLOv7 object detection model for the precise estimation of citrus yield. Images of citrus trees were captured in their natural field setting using a quadcopter-mounted UAV camera. Data augmentation techniques were applied to enhance the dataset diversity; the original YOLOv7 architecture and training parameters were modified to improve the model’s accuracy in detecting citrus fruits. The test results demonstrated commendable performance, with a precision of 96%, a recall of 100%, and an F1-score of 97.95%. The correlation between the fruit numbers recognized by the algorithm and the actual fruit numbers from 20 sample trees provided the coefficient R2 of 0.98. The strong positive correlation confirmed both the accuracy of the algorithm and the validity of the approach in identifying and quantifying citrus fruits on sample trees.

 Agricultural management unmanned aerial vehicle (UAV) remote-sensing systems YOLOv7 object detection model crop yield estimation Agricultural management unmanned aerial vehicle (UAV) remote-sensing systems YOLOv7 object detection model crop yield estimation

Marani R, Milella A, Petitti A, Reina G. Deep neural networks for grape bunch segmentation in natural images from a consumer-grade camera. Precision Agriculture. 2021;22:387–413. https://doi.org/10.1007/s11119-020-09736-0 Marani R, Milella A, Petitti A, Reina G. Deep neural networks for grape bunch segmentation in natural images from a consumer-grade camera. Precision Agriculture. 2021;22:387–413. https://doi.org/10.1007/s11119-020-09736-0 Gongal A, Amatya S, Karkee M, Zhang Q, Lewis K. Sensors and systems for fruit detection and localization: A review. Computers and Electronics in Agriculture. 2015;116:8–19. https://doi.org/10.1016/j.compag.2015.05.021 Gongal A, Amatya S, Karkee M, Zhang Q, Lewis K. Sensors and systems for fruit detection and localization: A review. Computers and Electronics in Agriculture. 2015;116:8–19. https://doi.org/10.1016/j.compag.2015.05.021 Sengupta S, Lee WS. Identification and determination of the number of immature green citrus fruit in a canopy under different ambient light conditions. Biosystems Engineering. 2014;117:51–61. https://doi.org/10.1016/j.biosystemseng.2013.07.007 Sengupta S, Lee WS. Identification and determination of the number of immature green citrus fruit in a canopy under different ambient light conditions. Biosystems Engineering. 2014;117:51–61. https://doi.org/10.1016/j.biosystemseng.2013.07.007 Maldonado Jr W, Barbosa JC. Automatic green fruit counting in orange trees using digital images. Computers and Electronics in Agriculture. 2016;127:572–581. https://doi.org/10.1016/j.compag.2016.07.023 Maldonado Jr W, Barbosa JC. Automatic green fruit counting in orange trees using digital images. Computers and Electronics in Agriculture. 2016;127:572–581. https://doi.org/10.1016/j.compag.2016.07.023 Zhao C, Lee WS, He D. Immature green citrus detection based on colour feature and sum of absolute transformed difference (SATD) using colour images in the citrus grove. Computers and Electronics in Agriculture. 2016;124:243–253. https://doi.org/10.1016/j.compag.2016.04.009 Zhao C, Lee WS, He D. Immature green citrus detection based on colour feature and sum of absolute transformed difference (SATD) using colour images in the citrus grove. Computers and Electronics in Agriculture. 2016;124:243–253. https://doi.org/10.1016/j.compag.2016.04.009 Dorj U-O, Lee M, Yun S. An yield estimation in citrus orchards via fruit detection and counting using image processing. Computers and Electronics in Agriculture. 2017;140:103–112. https://doi.org/10.1016/j.compag.2017.05.019 Dorj U-O, Lee M, Yun S. An yield estimation in citrus orchards via fruit detection and counting using image processing. Computers and Electronics in Agriculture. 2017;140:103–112. https://doi.org/10.1016/j.compag.2017.05.019 Liu T-H, Ehsani R, Toudeshki A, Zou X-J, Wang H-J. Detection of citrus fruit and tree trunks in natural environments using a multi-elliptical boundary model. Computers in Industry. 2018;99:9–16. https://doi.org/10.1016/j.compind.2018.03.007 Liu T-H, Ehsani R, Toudeshki A, Zou X-J, Wang H-J. Detection of citrus fruit and tree trunks in natural environments using a multi-elliptical boundary model. Computers in Industry. 2018;99:9–16. https://doi.org/10.1016/j.compind.2018.03.007 Liu S, Yang C, Hu Y, Huang L, Xiong L. A method for segmentation and recognition of mature citrus and branches-leaves based on regional features. In: Wang Y, Jiang Z, Peng Y, editors. Image and graphics technologies and applications. Singapore: Springer; 2018. pp. 292–301. https://doi.org/10.1007/978-981-13-1702-6_29 Liu S, Yang C, Hu Y, Huang L, Xiong L. A method for segmentation and recognition of mature citrus and branches-leaves based on regional features. In: Wang Y, Jiang Z, Peng Y, editors. Image and graphics technologies and applications. Singapore: Springer; 2018. pp. 292–301. https://doi.org/10.1007/978-981-13-1702-6_29 Xu L, Zhu S, Chen X, Wang Y, Kang Z, Huang P, et al. Citrus recognition in real scenarios based on machine vision. DYNA. Ingenier´ıa e Industria. 2020;95(1):87–93. https://doi.org/10.6036/9363 Xu L, Zhu S, Chen X, Wang Y, Kang Z, Huang P, et al. Citrus recognition in real scenarios based on machine vision. DYNA. Ingenier´ıa e Industria. 2020;95(1):87–93. https://doi.org/10.6036/9363 Zhang X, Toudeshki A, Ehsani R, Li H, Zhang W, Ma R. Yield estimation of citrus fruit using rapid image processing in natural background. Smart Agricultural Technology. 2022;2:100027. https://doi.org/10.1016/j.atech.2021.100027 Zhang X, Toudeshki A, Ehsani R, Li H, Zhang W, Ma R. Yield estimation of citrus fruit using rapid image processing in natural background. Smart Agricultural Technology. 2022;2:100027. https://doi.org/10.1016/j.atech.2021.100027 Maheswari P, Raja P, Apolo-Apolo OE, Perez-Ruiz M. Intelligent fruit yield estimation for orchards using deep learning based semantic segmentation techniques – A review. Frontiers in Plant Science. 2021;12:684328. https://doi.org/10.3389/fpls.2021.684328 Maheswari P, Raja P, Apolo-Apolo OE, Perez-Ruiz M. Intelligent fruit yield estimation for orchards using deep learning based semantic segmentation techniques – A review. Frontiers in Plant Science. 2021;12:684328. https://doi.org/10.3389/fpls.2021.684328 Yamamoto K, Guo W, Yoshioka Y, Ninomiya S. On plant detection of intact tomato fruits using image analysis and machine learning methods. Sensors. 2014;14(7):12191–12206. https://doi.org/10.3390/s140712191 Yamamoto K, Guo W, Yoshioka Y, Ninomiya S. On plant detection of intact tomato fruits using image analysis and machine learning methods. Sensors. 2014;14(7):12191–12206. https://doi.org/10.3390/s140712191 Loddo A, Loddo M, Di Ruberto C. A novel deep learning based approach for seed image classification and retrieval. Computers and Electronics in Agriculture. 2021;187:106269. https://doi.org/10.1016/j.compag.2021.106269 Loddo A, Loddo M, Di Ruberto C. A novel deep learning based approach for seed image classification and retrieval. Computers and Electronics in Agriculture. 2021;187:106269. https://doi.org/10.1016/j.compag.2021.106269 Han B-G, Lee J-G, Lim K-T, Choi D-H. Design of a scalable and fast YOLO for edge-computing devices. Sensors. 2020;20(23):6779. https://doi.org/10.3390/s20236779 Han B-G, Lee J-G, Lim K-T, Choi D-H. Design of a scalable and fast YOLO for edge-computing devices. Sensors. 2020;20(23):6779. https://doi.org/10.3390/s20236779 Sivakumar ANV, Li J, Scott S, Psota E, Jhala AJ, Luck JD, et al. Comparison of object detection and patch-based classification deep learning models on mid-to late-season weed detection in UAV imagery. Remote Sensing. 2020;12(13):2136. https://doi.org/10.3390/rs12132136 Sivakumar ANV, Li J, Scott S, Psota E, Jhala AJ, Luck JD, et al. Comparison of object detection and patch-based classification deep learning models on mid-to late-season weed detection in UAV imagery. Remote Sensing. 2020;12(13):2136. https://doi.org/10.3390/rs12132136 Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, et al. SSD: Single shot multibox detector. In: Leibe B, Matas J, Sebe N, Welling M, editors. Computer Vision – ECCV 2016. Cham: Springer; 2016. pp. 21–37. https://doi.org/10.1007/978-3-319-46448-0_2 Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, et al. SSD: Single shot multibox detector. In: Leibe B, Matas J, Sebe N, Welling M, editors. Computer Vision – ECCV 2016. Cham: Springer; 2016. pp. 21–37. https://doi.org/10.1007/978-3-319-46448-0_2 Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: Unified, real-time object detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition; 2016; Vegas. IEEE; 2016. pp. 779– 788. https://doi.org/10.1109/CVPR.2016.91 Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: Unified, real-time object detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition; 2016; Vegas. IEEE; 2016. pp. 779– 788. https://doi.org/10.1109/CVPR.2016.91 Redmon J, Farhadi A. YOLO9000: better, faster, stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition; 2017; Honolulu. IEEE; 2017. pp. 7263–7271. https://doi.org/10.1109/CVPR.2017.690 Redmon J, Farhadi A. YOLO9000: better, faster, stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition; 2017; Honolulu. IEEE; 2017. pp. 7263–7271. https://doi.org/10.1109/CVPR.2017.690 Redmon J, Farhadi A. YOLOv3: An incremental improvement. 2018. https://doi.org/10.48550/arXiv.1804.02767 Redmon J, Farhadi A. YOLOv3: An incremental improvement. 2018. https://doi.org/10.48550/arXiv.1804.02767 Bochkovskiy A, Wang C-Y, Liao H-YM. YOLOv4: Optimal speed and accuracy of object detection. 2020. https://doi.org/10.48550/arXiv.2004.10934 Bochkovskiy A, Wang C-Y, Liao H-YM. YOLOv4: Optimal speed and accuracy of object detection. 2020. https://doi.org/10.48550/arXiv.2004.10934 Jocher G, Chaurasia A, Stoken A, Borovec J, Kwon Y, Fang J, et al. ultralytics/yolov5: v6.1 – TensorRT, TensorFlow edge TPU and OpenVINO export and inference. Zenodo. 2022. https://doi.org/10.5281/zenodo.6222936 Jocher G, Chaurasia A, Stoken A, Borovec J, Kwon Y, Fang J, et al. ultralytics/yolov5: v6.1 – TensorRT, TensorFlow edge TPU and OpenVINO export and inference. Zenodo. 2022. https://doi.org/10.5281/zenodo.6222936 Ge Z, Liu S, Wang F, Li Z, Sun J. YOLOX: Exceeding YOLO series in 2021. 2021. https://doi.org/10.48550/arXiv.2107.08430 Ge Z, Liu S, Wang F, Li Z, Sun J. YOLOX: Exceeding YOLO series in 2021. 2021. https://doi.org/10.48550/arXiv.2107.08430 Wang C-Y, Bochkovskiy A, Liao H-YM. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2023; Vancouver. IEEE; 2023. pp. 7464–7475. https://doi.org/10.1109/CVPR52729.2023.00721 Wang C-Y, Bochkovskiy A, Liao H-YM. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2023; Vancouver. IEEE; 2023. pp. 7464–7475. https://doi.org/10.1109/CVPR52729.2023.00721 Girshick R. Fast r-cnn. Proceedings of the 2015 IEEE International Conference on Computer Vision; 2015; Santiago. IEEE; 2015. pp. 1440–1448. https://doi.org/10.1109/ICCV.2015.169 Girshick R. Fast r-cnn. Proceedings of the 2015 IEEE International Conference on Computer Vision; 2015; Santiago. IEEE; 2015. pp. 1440–1448. https://doi.org/10.1109/ICCV.2015.169 Ren S, He K, Girshick R, Sun J. Faster R-CNN: Towards real-time object detection with region proposal networks. In: Cortes C, Lawrence N, Lee D, Sugiyama M, Garnett R, editors. Advances in neural information processing systems. Purchase Printed Proceeding; 2015. Ren S, He K, Girshick R, Sun J. Faster R-CNN: Towards real-time object detection with region proposal networks. In: Cortes C, Lawrence N, Lee D, Sugiyama M, Garnett R, editors. Advances in neural information processing systems. Purchase Printed Proceeding; 2015. Lucena F, Breunig FM, Kux H. The combined use of UAV-based RGB and DEM images for the detection and delineation of orange tree crowns with mask R-CNN: An approach of labeling and unified framework. Future Internet. 2022;14(10):275. https://doi.org/10.3390/fi14100275 Lucena F, Breunig FM, Kux H. The combined use of UAV-based RGB and DEM images for the detection and delineation of orange tree crowns with mask R-CNN: An approach of labeling and unified framework. Future Internet. 2022;14(10):275. https://doi.org/10.3390/fi14100275 Xu L, Wang Y, Shi X, Tang Z, Chen X, Wang Y, et al. Real-time and accurate detection of citrus in complex scenes based on HPL-YOLOv4. Computers and Electronics in Agriculture. 2023;205:107590. https://doi.org/10.1016/j.compag.2022.107590 Xu L, Wang Y, Shi X, Tang Z, Chen X, Wang Y, et al. Real-time and accurate detection of citrus in complex scenes based on HPL-YOLOv4. Computers and Electronics in Agriculture. 2023;205:107590. https://doi.org/10.1016/j.compag.2022.107590 Yang R, Hu Y, Yao Y, Gao M, Liu R. Fruit target detection based on BCo-YOLOv5 model. Mobile Information Systems. 2022;2022:8457173. https://doi.org/10.1155/2022/8457173 Yang R, Hu Y, Yao Y, Gao M, Liu R. Fruit target detection based on BCo-YOLOv5 model. Mobile Information Systems. 2022;2022:8457173. https://doi.org/10.1155/2022/8457173 Lai Y, Ma R, Chen Y, Wan T, Jiao R, He H. A pineapple target detection method in a field environment based on improved YOLOv7. Applied Sciences. 2023;13(4):2691. https://doi.org/10.3390/app13042691 Lai Y, Ma R, Chen Y, Wan T, Jiao R, He H. A pineapple target detection method in a field environment based on improved YOLOv7. Applied Sciences. 2023;13(4):2691. https://doi.org/10.3390/app13042691 Chen J, Liu H, Zhang Y, Zhang D, Ouyang H, Chen X. A multiscale lightweight and efficient model based on YOLOv7: Applied to citrus orchard. Plants. 2022;11(23):3260. https://doi.org/10.3390/plants11233260 Chen J, Liu H, Zhang Y, Zhang D, Ouyang H, Chen X. A multiscale lightweight and efficient model based on YOLOv7: Applied to citrus orchard. Plants. 2022;11(23):3260. https://doi.org/10.3390/plants11233260 Yang H, Liu Y, Wang S, Qu H, Li N, Wu J, et al. Improved apple fruit target recognition method based on YOLOv7 model. Agriculture. 2023;13(7):1278. https://doi.org/10.3390/agriculture13071278 Yang H, Liu Y, Wang S, Qu H, Li N, Wu J, et al. Improved apple fruit target recognition method based on YOLOv7 model. Agriculture. 2023;13(7):1278. https://doi.org/10.3390/agriculture13071278 Ministry of Agriculture. https://www.agriculture.gov.ma Ministry of Agriculture. https://www.agriculture.gov.ma Zheng Z, Wang P, Liu W, Li J, Ye R, Ren D. Distance-IoU loss: Faster and better learning for bounding box regression. AAAI-20 Technical Tracks 7. 2020;34(7):12993–13000. https://doi.org/10.1609/aaai.v34i07.6999 Zheng Z, Wang P, Liu W, Li J, Ye R, Ren D. Distance-IoU loss: Faster and better learning for bounding box regression. AAAI-20 Technical Tracks 7. 2020;34(7):12993–13000. https://doi.org/10.1609/aaai.v34i07.6999