https://doi.org/10.4081/jae.2024.1641
YOLO deep learning algorithm for object detection in agriculture: a review
Published: 13 December 2024
Abstract Views: 351
PDF: 171
HTML: 21
HTML: 21
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
YOLO represents the one-stage object detection also called regression-based object detection. Object in the given input is directly classified and located instead of using the candidate region. The accuracy from two-stage detection is higher than one-stage detection where one-stage object detection speed is higher than two-stage object detection. YOLO has become popular because of its Detection accuracy, good generalization, open-source, and speed. YOLO boasts exceptional speed due to its approach of using regression problems for frame detection, eliminating the need for a complex pipeline. In agriculture, using remote sensing and drone technologies YOLO classifies and detects crops, diseases, and pests, and is also used for land use mapping, environmental monitoring, urban planning, and wildlife. Recent research highlights YOLO's impressive performance in various agricultural applications. For instance, YOLOv4 demonstrated high accuracy in counting and locating small objects in UAV-captured images of bean plants, achieving an AP of 84.8% and a recall of 89%. Similarly, YOLOv5 showed significant precision in identifying rice leaf diseases, with a precision rate of 90%. In this review, we discuss the basic principles behind YOLO, different versions of YOLO, limitations, and YOLO application in agriculture and farming.
Abhijit, Akhil, S., Kumar, V.A., Jose, B.K., Abubeker, K. 2023. Computer vision assisted bird–eye chilli classification framework using YOLO V5 object detection model. In: Shrivastava, V., Bansal, J.C., Panigrahi, B.K. (eds.), Power Engineering and Intelligent Systems. PEIS 2023. Lecture Notes in Electrical Engineering vol 1097. Singapore, Springer.
DOI: https://doi.org/10.1007/978-981-99-7216-6_18
Ajayi, O G., Ashi, J., Guda, B. 2023. Performance evaluation of YOLO v5 model for automatic crop and weed classification on UAV images. Smart Agr. Technol. 5:100231.
DOI: https://doi.org/10.1016/j.atech.2023.100231
Ajikaran, R., Hewarathna, A.I., Palanisamy, V., Joseph, C., Thuseethan, S. 2023. An image analysis-based automated method using deep learning for grain counting. IEEE 17th Int. Conf. on Industrial and Information Systems (ICIIS), Peradeniya. pp. 25-30
DOI: https://doi.org/10.1109/ICIIS58898.2023.10253539
Amara, S.J., Yamini, S., Sumathi, D. 2023. Pest detection using YOLO V7 model. In: Namasudra, S., Trivedi, M.C., Crespo, R.G., Lorenz, P. (eds.), Data Science and Network Engineering. ICDSNE 2023. Lecture Notes in Networks and Systems,vol 791. Singapore, Springer.
DOI: https://doi.org/10.1007/978-981-99-6755-1_17
Amarasingam, N., Gonzalez, F., Salgadoe, A.S.A., Sandino, J., Powell, K. 2022. Detection of white leaf disease in sugarcane crops using UAV-derived RGB imagery with existing deep learning models. Remote Sens. (Basel) 14:6137.
DOI: https://doi.org/10.3390/rs14236137
An, Q., Wang, K., Li, Z., Song, C., Tang, X., Song, J. 2022. Real-time monitoring method of strawberry fruit growth state based on YOLO improved model. IEEE Access 10:124363-124372.
DOI: https://doi.org/10.1109/ACCESS.2022.3220234
Appe, S.N., Arulselvi, G., Balaji, G. 2023. CAM-YOLO: tomato detection and classification based on improved YOLOv5 using combining attention mechanism. PeerJ Comp. Sci. 9: e1463.
DOI: https://doi.org/10.7717/peerj-cs.1463
Ariyadi, M.R.N., Pribadi, M.R., Widiyanto, E.P. 2023. Unmanned aerial vehicle for remote sensing detection of oil palm trees using you only look once and convolutional neural network. 10th Int. Conf. on Electrical Engineering, Computer Science and Informatics (EECSI), Palembang. pp. 226-230
DOI: https://doi.org/10.1109/EECSI59885.2023.10295670
Bachhal, P., Kukreja, V., Ahuja, S. 2023. Real-time disease detection system for maize plants using deep convolutional neural networks. Int. J. Comput. Dig. Syst. 14:10263-10275.
DOI: https://doi.org/10.12785/ijcds/140199
Bandi, R., Swamy, S., Arvind, C. 2023. Leaf disease severity classification with explainable artificial intelligence using transformer networks. Int. J. Adv. Technol. Eng. Explor. 10:278.
DOI: https://doi.org/10.19101/IJATEE.2022.10100136
Bazame, H.C., Molin, J.P., Althoff, D., Martello, M. 2021. Detection, classification, and mapping of coffee fruits during harvest with computer vision. Comput. Electron. Agr. 183:106066.
DOI: https://doi.org/10.1016/j.compag.2021.106066
Bazame, H.C., Molin, J.P., Althoff, D., Martello, M. 2022. Detection of coffee fruits on tree branches using computer vision. Sci. Agric. 80:e20220064.
DOI: https://doi.org/10.1590/1678-992x-2022-0064
Benayad, M., Houran, N., Aamir, Z., Maanan, M., Rhinane, H. 2023. Geomembrane basins detection based on satellite high-resolution imagery using DEEP learning algorithms. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 48:75-79.
DOI: https://doi.org/10.5194/isprs-archives-XLVIII-4-W6-2022-75-2023
Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M. 2020. Yolov4: Optimal speed and accuracy of object detection. a arXiv:2004.10934.
Breuers, S., Yang, S., Mathias, M., Leibe, B. 2016. Exploring bounding box context for multi-object tracker fusion. IEEE Winter Conf. Applications of Computer Vision (WACV), Lake Placid. pp. 18-8.
DOI: https://doi.org/10.1109/WACV.2016.7477564
Buzzy, M., Thesma, V., Davoodi, M., Mohammadpour Velni, J. 2020. Real-time plant leaf counting using deep object detection networks. Sensors (basel) 20:6896.
DOI: https://doi.org/10.3390/s20236896
Camacho, J.C., Morocho-Cayamcela, M.E. 2023. Mask R-CNN and YOLOv8 comparison to perform tomato maturity recognition task. In: Maldonado-Mahauad, J., Herrera-Tapia, J., Zambrano-Martínez, J.L., Berrezueta, S. (eds.), Information and Communication Technologies. TICEC 2023. Communications in Computer and Information Science vol 1885. Cham, Springer.
DOI: https://doi.org/10.1007/978-3-031-45438-7_26
Chen, C., Lu, J., Zhou, M., Yi, J., Liao, M., Gao, Z. 2022. A YOLOv3-based computer vision system for identification of tea buds and the picking point. Comput. Electron. Agr. 198:107116.
DOI: https://doi.org/10.1016/j.compag.2022.107116
Chen, J., Ma, A., Huang, L., Su, Y., Li, W., Zhang, H., Wang, Z. 2023. GA-YOLO: a lightweight YOLO model for dense and occluded grape target detection. Horticulturae 9:443.
DOI: https://doi.org/10.3390/horticulturae9040443
Chen, L.-P. 2021. Practical statistics for data scientists: 50+ essential concepts using R and Python. Technometrics 63:272-273.
DOI: https://doi.org/10.1080/00401706.2021.1904738
Chen, M.-C., Cheng, Y.-T., Liu, C.-Y. 2022. Implementation of a fruit quality classification application using an artificial intelligence algorithm. Sensors Mater. 34:151-162.
DOI: https://doi.org/10.18494/SAM3553
Chen, Z., Su, R., Wang, Y., Chen, G., Wang, Z., Yin, P., Wang, J. 2022. Automatic estimation of apple orchard blooming levels using the improved YOLOv5. Agronomy (Basel) 12:2483.
DOI: https://doi.org/10.3390/agronomy12102483
Cheng, L., Li, J., Duan, P., Wang, M. 2021. A small attentional YOLO model for landslide detection from satellite remote sensing images. Landslides 18:2751-2765.
DOI: https://doi.org/10.1007/s10346-021-01694-6
Cowton, J., Kyriazakis, I., and Bacardit, J. 2019. Automated individual pig localisation, tracking and behaviour metric extraction using deep learning. IEEE Access 7:108049-108060.
DOI: https://doi.org/10.1109/ACCESS.2019.2933060
da Silva, J.C., Silva, M.C., Luz, E.J., Delabrida, S., Oliveira, R.A. 2023. Using mobile edge AI to detect and map diseases in citrus orchards. Sensors (Basel) 23:2165.
DOI: https://doi.org/10.3390/s23042165
Dai, G., Fan, J. 2022. An industrial-grade solution for crop disease image detection tasks. Front. Plant Sci. 13:921057.
DOI: https://doi.org/10.3389/fpls.2022.921057
Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., Sun, J. 202). Repvgg: Making vgg-style convnets great again. IEEE/CVF Conf. on Computer Vision and Pattern Recognition, Nashville. pp. 13728-13737.
DOI: https://doi.org/10.1109/CVPR46437.2021.01352
Diwan, T., Anirudh, G., Tembhurne, J.V. 2023. Object detection using YOLO: Challenges, architectural successors, datasets and applications. Multimed. Tools Appl. 82:9243-9275.
DOI: https://doi.org/10.1007/s11042-022-13644-y
Dollár, P., Appel, R., Belongie, S., Perona, P. 2014. Fast feature pyramids for object detection. IEEE T. Pattern Anal. 36:1532-1545.
DOI: https://doi.org/10.1109/TPAMI.2014.2300479
Espinoza-Hernández, J., de Jesús López-Canteñs, G., López-Cruz, I.L., Romantchik-Kriuchkova, E. 2023. Agave plant density using convolutional neural networks on aerial imagery. Agrociencia 57. Online Ahead of Print.
DOI: https://doi.org/10.47163/agrociencia.v57i7.2970
Etienne, A., Ahmad, A., Aggarwal, V., Saraswat, D. 2021. Deep learning-based object detection system for identifying weeds using uas imagery. Remote Sens. (Basel) 13:5182.
DOI: https://doi.org/10.3390/rs13245182
Feng, C., Zhong, Y., Gao, Y., Scott, M.R., Huang, W. 2021. Tood: Task-aligned one-stage object detection. IEEE/CVF Int. Conf. on Computer Vision (ICCV), Montreal. pp. 3490-3499.
DOI: https://doi.org/10.1109/ICCV48922.2021.00349
Fukada, K., Hara, K., Cai, J., Teruya, D., Shimizu, I., Kuriyama, T., et al. 2023. An automatic tomato growth analysis system using YOLO transfer learning. Appl. Sci. 13: 6880.
DOI: https://doi.org/10.3390/app13126880
Gai, R., Chen, N., Yuan, H. 2023. A detection algorithm for cherry fruits based on the improved YOLO-v4 model. Neural Comput. Appl. 35:13895-13906.
DOI: https://doi.org/10.1007/s00521-021-06029-z
Gallo, I., Rehman, A.U., Dehkordi, R. H., Landro, N., La Grassa, R., Boschetti, M. 2023. Deep object detection of crop weeds: Performance of YOLOv7 on a real case dataset from UAV images. Remote Sens. (Basel) 15:539.
DOI: https://doi.org/10.3390/rs15020539
Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J. 2021. Yolox: Exceeding yolo series in 2021. arXiv: 2107.08430.
Girshick, R. 2015. Fast r-cnn. IEEE Int. Conf. on Computer Vision, Santiago. pp. 1440-1448,
DOI: https://doi.org/10.1109/ICCV.2015.169
Gremes, M.F., Fermo, I.R., Krummenauer, R., Flores, F.C., Gonçalves Andrade, C.M., da Motta Lima, O.C. 2023. System of counting green oranges directly from trees using artificial intelligence. AgriEngineering (Basel) 5:1813-1831.
DOI: https://doi.org/10.3390/agriengineering5040111
Hamidisepehr, A., Mirnezami, S.V., Ward, J.K. 2020. Comparison of object detection methods for corn damage assessment using deep learning. T. ASABE 63:1969-1980.
DOI: https://doi.org/10.13031/trans.13791
Haque, M.E., Rahman, A., Junaeid, I., Hoque, S.U., Paul, M. 2022. Rice leaf disease classification and detection using yolov5. arXiv: 2209.01579.
Hobbs, J., Khachatryan, V., Anandan, B.S., Hovhannisyan, H., Wilson, D. 2021. Broad dataset and methods for counting and localization of on-ear corn kernels. Front. Robot. AI 8:627009.
DOI: https://doi.org/10.3389/frobt.2021.627009
Hosang, J., Benenson, R., Schiele, B. 2017. Learning non-maximum suppression. IEEE Conf. Computer Vision and Pattern Recognition, Honolulu. pp. 6469-6477.
DOI: https://doi.org/10.1109/CVPR.2017.685
Hsu, C.-C., Hsu, K.-J., Tsai, C.-C., Lin, Y.-Y., Chuang, Y.-Y. 2019. Weakly supervised instance segmentation using the bounding box tightness prior. 33rd Conf. Neural Information Processing Systems (NeurIPS 2019), Vancouver.
Jiang, P., Ergu, D., Liu, F., Cai, Y., Ma, B. 2022. A review of Yolo algorithm developments. Procedia Comput. Sci. 199:1066-1073.
DOI: https://doi.org/10.1016/j.procs.2022.01.135
Jintasuttisak, T., Edirisinghe, E., Elbattay, A. 2022. Deep neural network based date palm tree detection in drone imagery. Comput. Electron. Agr. 192:106560.
DOI: https://doi.org/10.1016/j.compag.2021.106560
Kulkarni, A., Chong, D., Batarseh, F.A. 2020. Foundations of data imbalance and solutions for a data democracy. In: Feras A. Batarseh, Yang R. (eds.), Data democracy. Cambridge, Academic Press. pp. 83-106.
DOI: https://doi.org/10.1016/B978-0-12-818366-3.00005-8
Kumar, P., Kumar, N. 2023. Drone-based apple detection: Finding the depth of apples using YOLOv7 architecture with multi-head attention mechanism. Smart Agr. Technol. 5:100311.
DOI: https://doi.org/10.1016/j.atech.2023.100311
Lawal, M.O. 2021. Tomato detection based on modified YOLOv3 framework. Sic. Rep. 11:1477.
DOI: https://doi.org/10.1038/s41598-021-81216-5
Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., et al. 2022. YOLOv6: A single-stage object detection framework for industrial applications. arXiv: 2209.02976.
Li, M., Zhang, Z., Lei, L., Wang, X., Guo, X. 2020. Agricultural greenhouses detection in high-resolution satellite images based on convolutional neural networks: Comparison of faster R-CNN, YOLO v3 and SSD. Sensors (Basel) 20:4938.
DOI: https://doi.org/10.3390/s20174938
Lippi, M., Bonucci, N., Carpio, R. F., Contarini, M., Speranza, S., Gasparri, A. 2021. A yolo-based pest detection system for precision agriculture. 29th Mediterranean Conf. Control and Automation (MED). pp. 342-347.
DOI: https://doi.org/10.1109/MED51440.2021.9480344
Liu, S., Qi, L., Qin, H., Shi, J., Jia, J. 2018. Path aggregation network for instance segmentation. IEEE Conf. Computer Vision and Pattern Recognition, Salt Lake City. pp. 8759-8768.
DOI: https://doi.org/10.1109/CVPR.2018.00913
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C. 2016. Ssd: Single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds) Computer Vision – ECCV 2016. Lecture Notes in Computer Science vol 9905. Cham, Springer.
DOI: https://doi.org/10.1007/978-3-319-46448-0_2
Liu, Y., Shao, Z., Hoffmann, N. 2021. Global attention mechanism: Retain information to enhance channel-spatial interactions. arXiv: 2112.05561.
Lu, D., Ye, J., Wang, Y., Yu, Z. 2023. Plant detection and counting: Enhancing precision agriculture in UAV and general scenes. IEEE Access.
DOI: https://doi.org/10.1109/ACCESS.2023.3325747
Madhurya, C., and Jubilson, E. A. (2023). YR2S: efficient deep learning technique for detecting and classifying plant leaf diseases. IEEE Access 11:116196-116205
DOI: https://doi.org/10.1109/ACCESS.2023.3343450
Mamalis, M., Kalampokis, E., Kalfas, I., Tarabanis, K. 2023. Deep learning for detecting verticillium fungus in olive trees: Using YOLO in UAV imagery. Algorithms (Basel) 16:343.
DOI: https://doi.org/10.3390/a16070343
Mathew, M.P., Mahesh, T.Y. (2022). Leaf-based disease detection in bell pepper plant using YOLO v5. Signal Image Video P. 16:841-847.
DOI: https://doi.org/10.1007/s11760-021-02024-y
Narayana, C.L., Ramana, K.V. 2023. An efficient real-time weed detection technique using YOLOv7. Int. J. Adv. Comput. Sci. Appl. 14:550-556.
DOI: https://doi.org/10.14569/IJACSA.2023.0140265
Nath, C.P., Singh, R.G., Choudhary, V.K., Datta, D., Nandan, R., Singh, S.S. (2024). Challenges and alternatives of herbicide-based weed management. Agronomy (Basel) 14:126.
DOI: https://doi.org/10.3390/agronomy14010126
Nugroho, D.P., Widiyanto, S., Wardani, D.T. 2022. Comparison of deep learning-based object classification methods for detecting tomato ripeness. Int. J. Fuzzy Logic Intell. Syst. 22:223-232.
DOI: https://doi.org/10.5391/IJFIS.2022.22.3.223
Nurhabib, I., Seminar, K. 2022. Recognition and counting of oil palm tree with deep learning using satellite image. IOP Conf. Ser. Earth Environ. Sci. 974:012058.
DOI: https://doi.org/10.1088/1755-1315/974/1/012058
Ohnemüller, L., Briassouli, A. 2021. Improving accuracy and efficiency in plant detection on a novel, benchmarking real-world dataset. IEEE Int.Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Trento-Bolzano. pp. 172-176.
DOI: https://doi.org/10.1109/MetroAgriFor52389.2021.9628717
Özer, T., Akdoğan, C., Cengız, E., Kelek, M.M., Yildirim, K., Oğuz, Y., Akkoç, H. 2022. Cherry tree detection with deep learning. IEEE Conf. on Innovations in Intelligent Systems and Applications (ASYU), Antalya. pp. 1-4.
DOI: https://doi.org/10.1109/ASYU56188.2022.9925332
Papageorgiou, C.P., Oren, M., Poggio, T. 1998. A general framework for object detection. IEEE 6th Int. Conf. on Computer Vision, Bombay. pp. 555-562.
DOI: https://doi.org/10.1109/ICCV.1998.710772
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. 2011. Scikit-learn: Machine learning in Python. J. Machin. Learning Res. 12:2825-2830.
Pu, H., Chen, X., Yang, Y., Tang, R., Luo, J., Wang, Y., Mu, J. 2023. Tassel-YOLO: A new high-precision and real-time method for maize tassel detection and counting based on UAV aerial images. Drones 7:492.
DOI: https://doi.org/10.3390/drones7080492
Qin, Z., Wang, W., Dammer, K.-H., Guo, L., Cao, Z. 2021. Ag-YOLO: A real-time low-cost detector for precise spraying with case study of palms. Front. Plant Sci. 12:753603.
DOI: https://doi.org/10.3389/fpls.2021.753603
Qing, Y., Liu, W., Feng, L., Gao, W. 2021. Improved Yolo network for free-angle remote sensing target detection. Remote Sens. (Basel) 13:2171.
DOI: https://doi.org/10.3390/rs13112171
Quach, L.-D., Quoc, K.N., Quynh, A.N., Ngoc, H.T., Nghe, N.T. 2024. Tomato health monitoring system: tomato classification, detection, and counting system based on YOLOv8 model with explainable MobileNet models using Grad-CAM++. IEEE Access 12:9719-9737.
DOI: https://doi.org/10.1109/ACCESS.2024.3351805
Rajamohanan, R., Latha, B.C. 2023. An optimized YOLO v5 model for tomato leaf disease classification with field dataset. Eng. Technol. Appl. Sci. Res. 13:12033-12038.
DOI: https://doi.org/10.48084/etasr.6377
Redmon, J., Divvala, S., Girshick, R., Farhadi, A. 2016. You only look once: Unified, real-time object detection. IEEE Conf. on Computer Vision and Pattern Recognition, Las Vegas. pp. 779-788.
DOI: https://doi.org/10.1109/CVPR.2016.91
Redmon, J., Farhadi, A. 2017. YOLO9000: better, faster, stronger. IEEE Conf. on Computer Vision and Pattern Recognition, Honolulu. pp. 6517-6525.
DOI: https://doi.org/10.1109/CVPR.2017.690
Redmon, J., Farhadi, A. 2018. Yolov3: An incremental improvement. arXiv: 1804.02767.
Roy, A.M., Bhaduri, J., Kumar, T., Raj, K. 2023. WilDect-YOLO: An efficient and robust computer vision-based accurate object localization model for automated endangered wildlife detection. Ecol. Inform. 75:101919.
DOI: https://doi.org/10.1016/j.ecoinf.2022.101919
Sneha, N., Sundaram, M., Ranjan, R. 2024. Acre-scale grape bunch detection and predict grape harvest using YOLO deep learning network. SN Comput. Sci. 5:250.
DOI: https://doi.org/10.1007/s42979-023-02572-9
Sohan, M., Sai Ram, T., Reddy, R., Venkata, C. 2024. A review on YOLOv8 and its advancements. Int. Conf. on Data Intelligence and Cognitive Informatics. pp 529-545
DOI: https://doi.org/10.1007/978-981-99-7962-2_39
Song, C., Wang, C., Yang, Y. 2020. Automatic detection and image recognition of precision agriculture for citrus diseases. IEEE Eurasia Confe. on IOT, Communication and Engineering, Yunlin, Taiwan. pp. 187-190.
DOI: https://doi.org/10.1109/ECICE50847.2020.9301932
Song, Z., Chen, Q., Huang, Z., Hua, Y., Yan, S. 2011. Contextualizing object detection and classification. IEEE T. Pattern Anal. 37:13-27.
DOI: https://doi.org/10.1109/TPAMI.2014.2343217
Sportelli, M., Apolo-Apolo, O.E., Fontanelli, M., Frasconi, C., Raffaelli, M., Peruzzi, A., Perez-Ruiz, M. 2023. Evaluation of YOLO object detectors for weed detection in different turfgrass scenarios. Appl. Sci. 13:8502.
DOI: https://doi.org/10.3390/app13148502
Štancel, M., Hulič, M. 2019. An introduction to image classification and object detection using YOLO detector. Proc. CEUR Workshop.
Straker, A., Puliti, S., Breidenbach, J., Kleinn, C., Pearse, G., Astrup, R., Magdon, P. 2023. Instance segmentation of individual tree crowns with YOLOv5: A comparison of approaches using the ForInstance benchmark LiDAR dataset. ISPRS Open J. Photogramm. Remote Sens. 9:100045.
DOI: https://doi.org/10.1016/j.ophoto.2023.100045
Subramanyam, V.S. 2021. Non Max Suppression (NMS). Available from: https://medium.com/analytics-vidhya/non-max-suppression-nms-6623e6572536
Sulemane, S., Matos-Carvalho, J.P., Pedro, D., Moutinho, F., Correia, S.D. 2022. Vineyard gap detection by convolutional neural networks fed by multi-spectral images. Algorithms 15:440.
DOI: https://doi.org/10.3390/a15120440
Teng, L., Li, H., Karim, S. 2019. DMCNN: a deep multiscale convolutional neural network model for medical image segmentation. J. Healthc. Eng. 2019:8597606.
DOI: https://doi.org/10.1155/2019/8597606
Terven, J., Cordova-Esparza, D. 2023. A comprehensive review of YOLO: From YOLOv1 to YOLOv8 and beyond. arXiv: 2304.00501.
Thomson, S.J., Sullivan, D.G. 2006. Crop status monitoring using multispectral and thermal imaging systems for accessible aerial platforms. 2006 ASAE Annual Meeting 061179.
Tian, Y., Yang, G., Wang, Z., Wang, H., Li, E., Liang, Z. 2019. Apple detection during different growth stages in orchards using the improved YOLO-V3 model. Comput. Electron. Agr. 157:417-426.
DOI: https://doi.org/10.1016/j.compag.2019.01.012
Tishby, N., Zaslavsky, N. 2015. Deep learning and the information bottleneck principle. IEEE Information Theory Workshop, Jerusalem. pp. 1-5
DOI: https://doi.org/10.1109/ITW.2015.7133169
Tundia, C., Tank, P., Damani, O.P. 2020. Aiding irrigation census in developing countries by detecting minor irrigation structures from satellite imagery. Proc. 6th Int. Conf. on Geographical Information Systems Theory, Applications and Management. pp. 208-215.
DOI: https://doi.org/10.5220/0009421302080215
Tzutalin, D. 2015. tzutalin/labelImg. Available from: https://github.com/tzutalin/labelImg
Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M. 2023. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. IEEE/CVF Conf. on Computer Vision and Pattern Recognition, Vancouver. pp. 7464-7475.
DOI: https://doi.org/10.1109/CVPR52729.2023.00721
Wang, C.-Y., Liao, H.-Y. M., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W., Yeh, I.-H. 2020. CSPNet: A new backbone that can enhance learning capability of CNN. IEEE/CVF Conf. on Computer Vision and Pattern Recognition, Seattle. pp. 1571-1580
DOI: https://doi.org/10.1109/CVPRW50498.2020.00203
Wang, C.-Y., Yeh, I.-H., Liao, H.-Y. M. 2024. YOLOv9: learning what you want to learn using programmable gradient information. arXiv: 2402.13616.
DOI: https://doi.org/10.1007/978-3-031-72751-1_1
Wang, C., Wang, C., Wang, L., Wang, J., Liao, J., Li, Y., Lan, Y. 2023. A lightweight cherry tomato maturity real-time detection algorithm based on improved YOLOV5n. Agronomy (Basel) 13:2106.
DOI: https://doi.org/10.3390/agronomy13082106
Wang, H., Fan, Y., Wang, Z., Jiao, L., Schiele, B. 2018. Parameter-free spatial attention network for person re-identification. arXiv: 1811.12150.
Wang, Y., Fu, Q., Ma, Z., Tian, X., Ji, Z., Yuan, W., et al. 2023. YOLOv5-AC: a method of uncrewed rice transplanter working quality detection. Agronomy (Basel) 13:2279.
DOI: https://doi.org/10.3390/agronomy13092279
Wang, Z., Hua, Z., Wen, Y., Zhang, S., Xu, X., Song, H. 2024. E-YOLO: Recognition of estrus cow based on improved YOLOv8n model. Expert Syst. Appl. 238:122212.
DOI: https://doi.org/10.1016/j.eswa.2023.122212
Wiggers, K.L., Pohlod, C.D., Orlovski, R., Ferreira, R., Santos, T.A. 2022. Detection and counting of plants via deep learningusing images collected by RPA. Rev. Bras. Cien. Agr. 17:1.
DOI: https://doi.org/10.5039/agraria.v17i2a1353
Wu, D., Lv, S., Jiang, M., Song, H. 2020. Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments. Comput. Electron. Agr. 178:105742.
DOI: https://doi.org/10.1016/j.compag.2020.105742
Wu, Y., Yang, H., Mao, Y. 2024. Detection of the pine wilt disease using a joint deep object detection model based on drone remote sensing data. Forests (Basel) 15:869.
DOI: https://doi.org/10.3390/f15050869
Xiao, Y., Tian, Z., Yu, J., Zhang, Y., Liu, S., Du, S., Lan, X. 2020. A review of object detection based on deep learning. Multim. Tools Appl. 79:23729-23791.
DOI: https://doi.org/10.1007/s11042-020-08976-6
Xu, S., Wang, R., Shi, W., Wang, X. 2023. Classification of tree species in transmission line corridors based on YOLO v7. Forests (Basel) 15:61.
DOI: https://doi.org/10.3390/f15010061
Yao, J., Song, B., Chen, X., Zhang, M., Dong, X., Liu, H., et al. 2024. Pine-YOLO: a method for detecting pine wilt disease in unmanned aerial vehicle remote sensing images. Forests (Basel) 15:737.
DOI: https://doi.org/10.3390/f15050737
Yeh, J.-F., Lin, K.-M., Yuan, L.-C., Hsu, J.-M. (2024). Automatic counting and location labeling of rice seedlings from unmanned aerial vehicle images. Electronics (Basel) 13:273.
DOI: https://doi.org/10.3390/electronics13020273
Yin, S., Zhang, Y., Karim, S. 2018. Large scale remote sensing image segmentation based on fuzzy region competition and Gaussian mixture model. IEEE Access 6:26069-26080.
DOI: https://doi.org/10.1109/ACCESS.2018.2834960
Yin, S., Zhang, Y., Karim, S. 2019. Region search based on hybrid convolutional neural network in optical remote sensing images. Int. J. Distrib. Sensor N. 15:1550147719852036.
DOI: https://doi.org/10.1177/1550147719852036
Yu, J., Zhang, C., Wang, J., Zhang, M., Zhang, X., Li, X. 2023. Research on asparagus recognition based on deep learning. IEEE Access 11:117362-117367.
DOI: https://doi.org/10.1109/ACCESS.2023.3292530
Zakria, Z., Deng, J., Kumar, R., Khokhar, M.S., Cai, J., Kumar, J. 2022. Multiscale and direction target detecting in remote sensing images via modified YOLO-v4. IEEE J. Sel. Top. Appl. 15:1039-1048.
DOI: https://doi.org/10.1109/JSTARS.2022.3140776
Zhang, H., Cloutier, R.S. 2021. Review on one-stage object detection based on deep learning. EAI Endor. T. e-Learning 7:e5.
DOI: https://doi.org/10.4108/eai.9-6-2022.174181
Zhang, L., Lin, L., Liang, X., He, K. 2016. Is faster R-CNN doing well for pedestrian detection? ECCV 2016. Lecture Notes in Computer Science, vol 9906. Springer, Cham. pp 443-457.
DOI: https://doi.org/10.1007/978-3-319-46475-6_28
Zhang, P., Li, D. 2022. YOLO-VOLO-LS: a novel method for variety identification of early lettuce seedlings. Front. Plant Sci. 13:806878.
DOI: https://doi.org/10.3389/fpls.2022.806878
Zhong, Z., Jin, L., Xie, Z. 2015. High performance offline handwritten chinese character recognition using googlenet and directional feature maps. 3rd Int. Conf. on Document Analysis and Recognition, Tunis. pp. 846-850.
DOI: https://doi.org/10.1109/ICDAR.2015.7333881
Zhou, P., Ni, B., Geng, C., Hu, J., Xu, Y. 2018. Scale-transferrable object detection. IEEE/CVF Conf. on Computer Vision and Pattern Recognition, Salt Lake City. pp. 528-537.
DOI: https://doi.org/10.1109/CVPR.2018.00062
Zhu, S., Ma, W., Wang, J., Yang, M., Wang, Y., Wang, C. 2023. EADD-YOLO: An efficient and accurate disease detector for apple leaf using improved lightweight YOLOv5. Front. Plant Sci. 14:1120724.
DOI: https://doi.org/10.3389/fpls.2023.1120724
Zhu, X., Wang, R., Shi, W., Liu, X., Ren, Y., Xu, S., Wang, X. 2024. Detection of pine-wilt-disease-affected trees based on improved YOLO v7. Forests (Basel) 15:691.
DOI: https://doi.org/10.3390/f15040691
How to Cite
Kanna S, K. (2024) “YOLO deep learning algorithm for object detection in agriculture: a review”, Journal of Agricultural Engineering, 55(4). doi: 10.4081/jae.2024.1641.
Copyright (c) 2024 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Francesco Maria Tangorra, Stefania Leonardi, Valerio Bronzo, Nicola Rota, Paolo Moroni, Pre-milking mechanical teat stimulation and milking performance of dairy buffaloes in early lactation , Journal of Agricultural Engineering: Vol. 48 No. 1 (2017)
- Ren Hiraoka, Yuya Aoyagi, Kazuki Kobayashi, Automatic travelling of agricultural support robot for a fruit farm. Verification of effectiveness of real-time kinematic-global navigation satellite system and developed a simulator for specification design , Journal of Agricultural Engineering: Vol. 54 No. 1 (2023)
- Noureldin Sharaby, Artyom Doroshenko, Andrey Butovchenko, Modelling and verification of sesame seed particles using the discrete element method , Journal of Agricultural Engineering: Vol. 53 No. 2 (2022)
- Luhua Han, Haorui Ma, Menghan Mo, Francis Kumi, Jianping Hu, Hanping Mao, Design and test of an efficient seedling pick-up device with a combination of air jet ejection and mechanical action , Journal of Agricultural Engineering: Vol. 55 No. 3 (2024)
- Angela Calvo, Roberto Deboli, Christian Preti, Antonino De Maria, Daily exposure to hand arm vibration by different electric olive beaters , Journal of Agricultural Engineering: Vol. 45 No. 3 (2014)
- Eleonora Iaccheri, Chiara Cevoli, Santina Romani, Marco Dalla Rosa, Giovanni Molari, Angelo Fabbri, Simple and efficient approach for shelf-life test on frozen spinach and parsley , Journal of Agricultural Engineering: Vol. 52 No. 3 (2021)
- Sara Cucchiaro, Laura Carretta, Paolo Nasta, Federico Cazorzi, Roberta Masin, Nunzio Romano, Paolo Tarolli, Multi-temporal geomorphometric analysis to assess soil erosion under different tillage practices: A methodological case study , Journal of Agricultural Engineering: Vol. 53 No. 1 (2022)
- Francesca Piazzolla, Maria Luisa Amodio, Giancarlo Colelli, Spectra evolution over on-vine holding of Italia table grapes: prediction of maturity and discrimination for harvest times using a Vis-NIR hyperspectral device , Journal of Agricultural Engineering: Vol. 48 No. 2 (2017)
- Volodymyr Bulgakov, Valerii Adamchuk, Ladislav Nozdrovicky, Ivan Holovach, Theory of the interaction of flat sensing organ with the head of the sugar beet root , Journal of Agricultural Engineering: Vol. 48 No. 4 (2017)
- Meizhou Chen, Guangfei Xu, Xianghao Li, Hongda Zhao, Yongli Zhao, Peisong Diao, Yinping Zhang, Optimization design and experiment of double-helix total mixed rations preparation mixer for silage straw feed , Journal of Agricultural Engineering: Vol. 55 No. 2 (2024)
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
