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: 4
PDF: 10
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.
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.
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
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
Buzzy, M., Thesma, V., Davoodi, M., Mohammadpour Velni, J. 2020. Real-time plant leaf counting using deep object detection networks. Sensors (basel) 20:6896.
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.
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.
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.
Chen, L.-P. 2021. Practical statistics for data scientists: 50+ essential concepts using R and Python. Technometrics 63:272-273.
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.
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.
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.
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.
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.
Dai, G., Fan, J. 2022. An industrial-grade solution for crop disease image detection tasks. Front. Plant Sci. 13:921057.
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.
Diwan, T., Anirudh, G., Tembhurne, J.V. 2023. Object detection using YOLO: Challenges, architectural successors, datasets and applications. Multimed. Tools Appl. 82:9243-9275.
Dollár, P., Appel, R., Belongie, S., Perona, P. 2014. Fast feature pyramids for object detection. IEEE T. Pattern Anal. 36:1532-1545.
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.
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.
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.
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.
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.
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.
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,
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.
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.
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.
Hosang, J., Benenson, R., Schiele, B. 2017. Learning non-maximum suppression. IEEE Conf. Computer Vision and Pattern Recognition, Honolulu. pp. 6469-6477.
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.
Jintasuttisak, T., Edirisinghe, E., Elbattay, A. 2022. Deep neural network based date palm tree detection in drone imagery. Comput. Electron. Agr. 192:106560.
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.
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.
Lawal, M.O. 2021. Tomato detection based on modified YOLOv3 framework. Sic. Rep. 11:1477.
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.
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.
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.
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.
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.
Madhurya, C., and Jubilson, E. A. (2023). YR2S: efficient deep learning technique for detecting and classifying plant leaf diseases. IEEE Access 11:116196-116205
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Qing, Y., Liu, W., Feng, L., Gao, W. 2021. Improved Yolo network for free-angle remote sensing target detection. Remote Sens. (Basel) 13:2171.
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.
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.
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.
Redmon, J., Farhadi, A. 2017. YOLO9000: better, faster, stronger. IEEE Conf. on Computer Vision and Pattern Recognition, Honolulu. pp. 6517-6525.
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.
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.
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
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.
Song, Z., Chen, Q., Huang, Z., Hua, Y., Yan, S. 2011. Contextualizing object detection and classification. IEEE T. Pattern Anal. 37:13-27.
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.
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.
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.
Teng, L., Li, H., Karim, S. 2019. DMCNN: a deep multiscale convolutional neural network model for medical image segmentation. J. Healthc. Eng. 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.
Tishby, N., Zaslavsky, N. 2015. Deep learning and the information bottleneck principle. IEEE Information Theory Workshop, Jerusalem. pp. 1-5
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Zhang, H., Cloutier, R.S. 2021. Review on one-stage object detection based on deep learning. EAI Endor. T. e-Learning 7:e5.
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.
Zhang, P., Li, D. 2022. YOLO-VOLO-LS: a novel method for variety identification of early lettuce seedlings. Front. Plant Sci. 13:806878.
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.
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.
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.
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.
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
- P. D'Antonio, C. D'Antonio, C. Evangelista, V. Doddato, Satellite guidance systems in agriculture: experimental comparison between EZ-Steer/RTK and AUTOPILOT/EGNOS , Journal of Agricultural Engineering: Vol. 44 No. s2 (2013): Proceedings of the 10th Conference of the Italian Society of Agricultural Engineering
- R. Gubiani, G. Pergher, S.R.S. Cividino, R. Lombardo, F. Blanchini, A Wii-controlled safety device for electric chainsaws , Journal of Agricultural Engineering: Vol. 44 No. s2 (2013): Proceedings of the 10th Conference of the Italian Society of Agricultural Engineering
- Enrico Fabrizio, Gabriele Garnero, The assessment of the visual perception in viewshed analysis for the landscape settings , Journal of Agricultural Engineering: Vol. 44 No. s2 (2013): Proceedings of the 10th Conference of the Italian Society of Agricultural Engineering
- J. Moretto, F. Delai, E. Rigon, L. Picco, M.A. Lenzi, Erosion - deposition evaluation through hybrid DTMs derived by LiDAR and colour bathymetry: the case study of the Brenta, Piave and Tagliamento rivers , Journal of Agricultural Engineering: Vol. 44 No. s2 (2013): Proceedings of the 10th Conference of the Italian Society of Agricultural Engineering
- Pietro Catania, Mariangela Vallone, Noise levels of a track-laying tractor during field operations in the vineyard , Journal of Agricultural Engineering: Vol. 44 No. s2 (2013): Proceedings of the 10th Conference of the Italian Society of Agricultural Engineering
- Ileana Blanco, Simone Pascuzzi, Alexandros Sotirios Anifantis, Giacomo Scarascia-Mugnozza, Study of a pilot photovoltaic-electrolyser-fuel cell power system for a geothermal heat pump heated greenhouse and evaluation of the electrolyser efficiency and operational mode , Journal of Agricultural Engineering: Vol. 45 No. 3 (2014)
- Simone Pascuzzi, Emanuele Cerruto, An innovative pneumatic electrostatic sprayer useful for tendone vineyards , Journal of Agricultural Engineering: Vol. 46 No. 3 (2015)
- Aldo Calcante, Luca Fontanini, Fabrizio Mazzetto, Repair and maintenance costs of 4WD tractors and self propelled combine harvesters in Italy , Journal of Agricultural Engineering: Vol. 44 No. s2 (2013): Proceedings of the 10th Conference of the Italian Society of Agricultural Engineering
- Artur Altunyan, Arshaluys Tarverdyan, Geometric and kinematic parameters of vibrating knife in the development of cutting machines , Journal of Agricultural Engineering: Vol. 52 No. 3 (2021)
- Molla Mekonnen Alemu, Hussein Gadain, Brigadier Libanda, Understanding land use and land cover change in a prolonged conflict affected farming system: the case of Tuban district, Yemen , Journal of Agricultural Engineering: Early Access
<< < 30 31 32 33 34 35 36 37 38 39 > >>
You may also start an advanced similarity search for this article.
