Agricultural object detection with You Only Look Once (YOLO) Algorithm: A bibliometric and systematic literature review

Deep learning

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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

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You Only Look Once: Unified, Real-Time Object Detection

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Densely Connected Convolutional Networks

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Microsoft COCO: Common Objects in Context

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Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

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Feature Pyramid Networks for Object Detection

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Software survey: VOSviewer, a computer program for bibliometric mapping

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YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series

Sapkota, R., Flores-Calero, M., Qureshi, R., Badjugar, C., Nepal, U., Poulose, A., Zeno, P. J., Vaddevolu, U. B. P., Khan, S., Shoman, M., et al. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11253-3 · Cited by 528

The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection

Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383

Surveying You Only Look Once (YOLO) Multispectral Object Detection Advancements, Applications, and Challenges

Gallagher, J., & Oughton, E. J. (2025) · IEEE Access · doi:10.1109/access.2025.3526458 · Cited by 157

YOLOv11 optimization for efficient resource utilization

Rasheed, A. F., & Zarkoosh, M. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07520-3 · Cited by 136

ObjectDetection in Agriculture: A Comprehensive Review of Methods, Applications, Challenges, and Future Directions

Khan, Z., Shen, Y., & Liu, H. (2025) · Agriculture · doi:10.3390/agriculture15131351 · Cited by 113

Edge Computing-Enabled Smart Agriculture: Technical Architectures, Practical Evolution, and Bottleneck Breakthroughs

Gong, R., Zhang, H., Li, G., & He, J. (2025) · Sensors · doi:10.3390/s25175302 · Cited by 108

Comparative Performance Evaluation of YOLOv5, YOLOv8, and YOLOv11 for Solar Panel Defect Detection

Khanam, R., Asghar, T., & Hussain, M. (2025) · Solar · doi:10.3390/solar5010006 · Cited by 107

Analyzing Real-Time Object Detection with YOLO Algorithm in Automotive Applications: A Review

Gheorghe, C., Duguleană, M., Boboc, R. G., & Postelnicu, C. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.054735 · Cited by 84

Harnessing large vision and language models in agriculture: a review

Zhu, H., Qin, S., Su, M., Lin, C., Li, A., & Gao, J. (2025) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1579355 · Cited by 97

Generative Adversarial Networks (GANs) for Image Augmentation in Farming: A Review

Rahman, Z. U., Asaari, M. S. M., Ibrahim, H., Abidin, I. S. Z., & Ishak, M. K. (2024) · IEEE Access · doi:10.1109/access.2024.3505989 · Cited by 71

YOLO-Granada: a lightweight attentioned Yolo for pomegranates fruit detection

Zhao, J., Du, C., Li, Y., Mudhsh, M., Guo, D., Fan, Y., Wu, X., Wang, X., & Almodfer, R. (2024) · Scientific Reports · doi:10.1038/s41598-024-67526-4 · Cited by 68

Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI

Gomez, D., Selvaraj, M. G., Casas, J., Kavino, M., Rodriguez, M., Assefa, T., Mlaki, A., Nyakunga, G., Kato, F., Mukankusi, C. M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-66281-w · Cited by 52

YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once SeriesPreprint

Sapkota, R., Qureshi, R., Flores-Calero, M. J., Hussain, M., Badjugar, C., Nepal, U., Poulose, A., Zeno, P. J., Vaddevolu, U. B. P., Yan, H., et al. (2024) · Preprints.org · doi:10.20944/preprints202406.1366.v1 · Cited by 47

Detection of small foreign objects in Pu-erh sun-dried green tea: An enhanced YOLOv8 neural network model based on deep learning

Wang, Z., Zhang, S., Chen, Y., Xia, Y., Wang, H., Jin, R., Wang, Y., Fan, Z., Wang, Y., & Wang, B. (2024) · Food Control · doi:10.1016/j.foodcont.2024.110890 · Cited by 44

Comprehensive Performance Evaluation of YOLO11, YOLOv10, YOLOv9 and YOLOv8 on Detecting and Counting Fruitlet in Complex Orchard EnvironmentsPreprint

Sapkota, R., Meng, Z., Churuvija, M., Du, X., Ma, Z., & Karkee, M. (2024) · Qeios · doi:10.32388/e9y7xi · Cited by 43

Automated defects detection in extrusion 3D printing using YOLO models

Sani, A. R., Zolfagharian, A., & Kouzani, A. Z. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02543-8 · Cited by 47

Application of Digital Twin Technology in Smart Agriculture: A Bibliometric Review

Gund, R., Badgujar, C., Samiappan, S., & Jagadamma, S. (2025) · Agriculture · doi:10.3390/agriculture15171799 · Cited by 41

A Systematic Review of Deep Learning-Based Object Detection in Agriculture: Methods, Challenges, and Future Directions

Dalal, M., & Mittal, P. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.066056 · Cited by 40

Real-Time Object Detection for Edge Computing-Based Agricultural Automation: A Case Study Comparing the YOLOX and YOLOv12 Architectures and Their Performance in Potato Harvesting Systems

Kim, J., Kim, G., Yoshitoshi, R., & Tokuda, K. (2025) · Sensors · doi:10.3390/s25154586 · Cited by 39

BED-YOLO: An Enhanced YOLOv10n-Based Tomato Leaf Disease Detection Algorithm

Wang, Q., Yan, N., Qin, Y., Zhang, X., & Li, X. (2025) · Sensors · doi:10.3390/s25092882 · Cited by 39

From vineyard to vision: Multi-domain analysis and mitigation of grape cluster detection failures in complex viticultural environments

Rana, S., Hensel, O., & Nasirahmadi, A. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.108833 · Cited by 42

Pear Object Detection in Complex Orchard Environment Based on Improved YOLO11

Zhang, M., Ye, S., Zhao, S., Wang, W., & Xie, C. (2025) · Symmetry · doi:10.3390/sym17020255 · Cited by 37

Enhancing optical non-destructive methods for food quality and safety assessments with machine learning techniques: A survey

Wang, X., Feng, Y., Wang, Y. J., Zhu, H., Song, D., Shen, C., & Luo, Y. (2025) · Journal of Agriculture and Food Research · doi:10.1016/j.jafr.2025.101734 · Cited by 37

Ripe Tomato Detection Algorithm Based on Improved YOLOv9

Wang, Y., Rong, Q., & Hu, C. (2024) · Plants · doi:10.3390/plants13223253 · Cited by 33

Sunflower-YOLO: Detection of sunflower capitula in UAV remote sensing images

Jing, R., Niu, Q., Tian, Y., Zhang, H., Zhao, Q., Li, Z., Zhou, X., & Li, D. (2024) · European Journal of Agronomy · doi:10.1016/j.eja.2024.127332 · Cited by 37

Multi-scale adaptive YOLO for instance segmentation of grape pedicels

Shen, Q., Zhang, X., Shen, M., & Xu, D. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109712 · Cited by 32

Recent Developments and Applications of Crop Disease Detection, Prediction, and Early Warning: A Review

Yan, J., Wu, H., Diao, Z., Miao, Y., Zhang, B., & Zhao, C. (2025) · Engineering · doi:10.1016/j.eng.2025.10.032 · Cited by 34

YOLO-ALDS: an instance segmentation framework for tomato defect segmentation and grading based on active learning and improved YOLO11

Xue, S., Li, Z., Wang, D., Zhu, T., Zhang, B., & Ni, C. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110820 · Cited by 29

Ship_YOLO: General ship detection based on mixed distillation and dynamic task-aligned detection head

Wu, C.-M., Lei, J., Li, Z.-Q., & Ren, M.-L. (2025) · Ocean Engineering · doi:10.1016/j.oceaneng.2025.120616 · Cited by 30

Cellulase- and pH-Stimulating Selenium Nanopesticide Enhances Antifungal Efficacy to Anthracnose: Action Mechanism and Environmental Safety

Wang, J., Li, X., Chen, P., Li, R. P., Hu, J., Wu, X., Wang, Y. N., & Li, H. (2025) · ACS Nano · doi:10.1021/acsnano.5c05437 · Cited by 27

YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once SeriesPreprint

Sapkota, R., Qureshi, R., Flores-Calero, M. J., Badgujar, C., Nepal, U., Poulose, A., Zeno, P. J., Vaddevolu, U. B. P., Yan, H., & Karkee, M. (2024) · doi:10.36227/techrxiv.171995313.38075268/v1 · Cited by 33

Benchmarking YOLO-based deep learning models for real-time object detection in hybrid ADAS and intelligent transportation systems

Chaman, M., Maliki, A. E., Dahou, H., & Hadjoudja, A. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.108942 · Cited by 25

A Novel 5G PMN-Driven Approach for AI-Powered Irrigation and Crop Health Monitoring

Nguyen-Tan, T., & Le‐Trung, Q. (2024) · IEEE Access · doi:10.1109/access.2024.3452719 · Cited by 25

SGW-YOLOv8n: An Improved YOLOv8n-Based Model for Apple Detection and Segmentation in Complex Orchard Environments

Wu, T., Miao, Z., Huang, W., Han, W., Guo, Z., & Li, T. (2024) · Agriculture · doi:10.3390/agriculture14111958 · Cited by 24

Comprehensive Investigation of Machine Learning and Deep Learning Networks for Identifying Multispecies Tomato Insect Images

Praharsha, C. H., Poulose, A., & Badgujar, C. (2024) · Sensors · doi:10.3390/s24237858 · Cited by 24

Oil palm tree detection in UAV imagery using an enhanced RetinaNet

Lee, S. S., Lim, L. G., Shivakumara, P., Cheong, J. X., Lock, S. S. M., & Ayub, M. N. B. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109530 · Cited by 26

Wheat Powdery Mildew Detection with YOLOv8 Object Detection Model

Önler, E., & Köycü, N. D. (2024) · Applied Sciences · doi:10.3390/app14167073 · Cited by 23

EAD-YOLOv10: Lightweight Steel Surface Defect Detection Algorithm Research Based on YOLOv10 Improvement

Haoyan, H., Tong, J., Haibin, W., & Xin-yun, L. (2025) · IEEE Access · doi:10.1109/access.2025.3552683 · Cited by 21

A Comprehensive Review of Sensing, Control, and Networking in Agricultural Robots: From Perception to Coordination

Nkwocha, C. L., Adewumi, A., Folorunsho, S. O., Eze, C., Jjagwe, P., Kemeshi, J., & Wang, N. (2025) · Robotics · doi:10.3390/robotics14110159 · Cited by 21

HCRP-YOLO: A lightweight algorithm for potato defect detection

Liao, H., Wang, G., Jin, S., Liu, Y., Sun, W., Yang, S., & Wang, L. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.100849 · Cited by 21

Research on an Apple Recognition and Yield Estimation Model Based on the Fusion of Improved YOLOv11 and DeepSORT

Yan, Z., Wu, Y.-W., Zhao, W., Zhang, S., & Li, X. (2025) · Agriculture · doi:10.3390/agriculture15070765 · Cited by 21

GPC-YOLO: An Improved Lightweight YOLOv8n Network for the Detection of Tomato Maturity in Unstructured Natural Environments

Dong, Y., Qiao, J., Liu, N., He, Y., Li, S., Hu, X., Yu, C., & Zhang, C. (2025) · Sensors · doi:10.3390/s25051502 · Cited by 20

Deep Learning for Fluorescence Lifetime Predictions Enables High-Throughput In Vivo Imaging

Kapsiani, S., Läubli, N. F., Ward, E., Fernández‐Villegas, A., Mazumder, B., Kaminski, C. F., & Schierle, G. S. K. (2025) · Journal of the American Chemical Society · doi:10.1021/jacs.5c03749 · Cited by 20

UCIW-YOLO: Multi-category and high-precision obstacle detection model for agricultural machinery in unstructured farmland environments

Liu, G., Jin, C., Ni, Y., Yang, T., & Liu, Z. “. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128686 · Cited by 22

High‐Performance Deep Learning for Instant Pest and Disease Detection in Precision Agriculture

Bilal, M., Shah, A. A., Abbas, S., & Khan, M. A. (2025) · Food Science & Nutrition · doi:10.1002/fsn3.70963 · Cited by 20

RA-CottNet: A Real-Time High-Precision Deep Learning Model for Cotton Boll and Flower Recognition

Wang, R.-F., Qin, Y.-M., Zhao, Y.-Y., Xu, M., Schardong, I. B., & Cui, K. (2025) · AI · doi:10.3390/ai6090235 · Cited by 23

YOLO for early detection and management of Tuta absoluta-induced tomato leaf diseases

Shehu, H. A., ACKLEY, A. U., Mark, M., Eteng, O. E., Sharif, M. H., & Kusetoğulları, H. (2025) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1524630 · Cited by 22

Advances in Object Detection and Localization Techniques for Fruit Harvesting Robots

Shi, X., Wang, S., Zhang, B., Ding, X., Qi, P., Qu, H., Li, N., Wu, J., & Yang, H. (2025) · Agronomy · doi:10.3390/agronomy15010145 · Cited by 23

Review: Multiobject tracking in livestock − from farm animal management to state-of-the-art methods

Nidhi, M. H., Liu, K., & Flay, K. J. (2025) · animal · doi:10.1016/j.animal.2025.101503 · Cited by 19

YOLOv11-AIU: a lightweight detection model for the grading detection of early blight disease in tomatoes

Tang, X., Sun, Z., Yang, L., Chen, Q., Liu, Z., Wang, P., & Zhang, Y. (2025) · Plant Methods · doi:10.1186/s13007-025-01435-z · Cited by 19

YOLOv8s-CFB: a lightweight method for real-time detection of apple fruits in complex environments

Zhao, B., Guo, A., Ma, R., Zhang, Y., & Gong, J. (2024) · Journal of Real-Time Image Processing · doi:10.1007/s11554-024-01543-4 · Cited by 19

Green synthesis of carbon dots encapsulated MoO3:La3+ for enhanced photocatalytic degradation, dactyloscopy and real-time FP detection using YOLOv8x

Gagana, M., Krushna, B. R. R., Sharma, S., Sharmila, S., Meenakshi, R., Devikala, A., Sahu, S., Manjunatha, K., Wu, S. Y., Arunakumar, R., et al. (2025) · Journal of the Taiwan Institute of Chemical Engineers · doi:10.1016/j.jtice.2025.106032 · Cited by 19

Efficient visual inspection of fire safety equipment in buildings

Lin, F., Wang, B., Chen, Z., Zhang, X., Song, C., Yang, L., & Cheng, J. C. P. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.105970 · Cited by 18

YOLOv11n for precision agriculture: lightweight and efficient detection of guava defects across diverse conditions

Raja, G. M., Selvaraju, P., Pugazhendi, P., & Antony, C. G. M. (2025) · Journal of the Science of Food and Agriculture · doi:10.1002/jsfa.14331 · Cited by 18

Hybrid attention transformer integrated YOLOV8 for fruit ripeness detection

Tang, J., Yu, Z., & Shao, C. (2025) · Scientific Reports · doi:10.1038/s41598-025-04184-0 · Cited by 18

Deep learning method for cucumber disease detection in complex environments for new agricultural productivity

Liu, J., Wang, X., Chen, Q., Peng, Y., & Liu, X. (2025) · BMC Plant Biology · doi:10.1186/s12870-025-06841-y · Cited by 18

Automatic fish weight estimation and 3D surface reconstruction with a lightweight instance segmentation model

Wang, G., Yu, J., Liu, S., Xu, W., Li, X. S., Hao, Y., & Li, D. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128275 · Cited by 17

Image and Point Cloud-Based Neural Network Models and Applications in Agricultural Nursery Plant Protection Tasks

Xu, J., Liu, H., & Shen, Y. (2025) · Agronomy · doi:10.3390/agronomy15092147 · Cited by 17

Transformasi Penyuluhan Pertanian Menuju Society 5.0: Analisis Peran Teknologi Informasi dan Komunikasi

Tapi, T., Mikhael, M., & Makabori, Y. Y. (2024) · Journal of Sustainable Agriculture Extension · doi:10.47687/josae.v2i1.820 · Cited by 17

ToRLNet: A Lightweight Deep Learning Model for Tomato Detection and Quality Assessment Across Ripeness Stages

Sun, H., Xi, X., Wu, A.-Q., & Wang, R.-F. (2025) · Horticulturae · doi:10.3390/horticulturae11111334 · Cited by 17

A Coffee Plant Counting Method Based on Dual-Channel NMS and YOLOv9 Leveraging UAV Multispectral Imaging

Wang, X., Zhang, C., Qiang, Z., Liu, C., Wei, X., & Cheng, F. (2024) · Remote Sensing · doi:10.3390/rs16203810 · Cited by 15

Integrated detection of coconut clusters and oriented leaves using improved YOLOv8n-obb for robotic harvesting

Fu, Y., Wang, Z., Zheng, H., Yin, X., Fu, W., & Gu, Y. M. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.109979 · Cited by 15

Improving photovoltaics hotspot and dirt accumulation detection with computer vision: A systematic YOLO model comparison based on field aerial images

Vourkos, E. G., Agathokleous, R. A., Panayides, A. S., Kalogirou, S. A., & Christoforou, E. G. (2025) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2025.104355 · Cited by 14

Multimodal estimation of fish weight via two-stream Transformer and adaptive RGB-D fusion

Wang, G., Hao, Y., & Li, D. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.111248 · Cited by 14

A facile approach towards large-scale synthesis of TiO2 nanoparticles derived from egg shell waste with enhanced UV shielding, nano priming and fingerprint real time object detection through YOLOv8x

Navya, N., Krushna, B. R. R., Sharma, S., Kumar, V., Sahoo, S., Sudarmani, R., Babu, K. S., Manjunatha, K., Wu, S. Y., Chiu, H., et al. (2024) · Inorganic Chemistry Communications · doi:10.1016/j.inoche.2024.113422 · Cited by 14

AgriScout: AI-powered robot for precise detection of PVY-infected potato plants

Singh, C., Randhawa, G. S., Farooque, A. A., Gill, Y. S., Fraser, A., Lokesh Kumar, K. M., Barrett, R., & Al-Mughrabi, K. I. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110781 · Cited by 14

Pod-pose : an efficient top-down keypoint detection model for fine-grained pod phenotyping in mature soybean

Liu, F., Liu, H., Wu, Q., Han, Z., Pang, S., Wang, S., & Zhao, L. (2025) · Plant Methods · doi:10.1186/s13007-025-01399-0 · Cited by 13

YOLOv8-DDS: A lightweight model based on pruning and distillation for early detection of root mold in barley seedling

Junjie, H., Ma, Z., Wu, Y., Yujian, B., Wang, Y., Su, Z., & Lifeng, G. (2025) · Information Processing in Agriculture · doi:10.1016/j.inpa.2025.07.004 · Cited by 15

Investigation of an Efficient Multi-Class Cotton Leaf Disease Detection Algorithm That Leverages YOLOv11

Hu, F., Abula, M., Wang, D., Li, X., Yan, N., Xie, Q., & Zhang, X. (2025) · Sensors · doi:10.3390/s25144432 · Cited by 13

P3DFusion: A cross-scene and high-fidelity 3D plant reconstruction framework empowered by vision foundation models and 3D Gaussian splatting

Wang, Z., Xiao, S., Miao, Z., Liu, R., Chen, H., Wang, Q., Shao, K., Wang, R., & Ma, Y. (2025) · European Journal of Agronomy · doi:10.1016/j.eja.2025.127811 · Cited by 13

Deep Learning Approaches for Automatic Livestock Detection in UAV Imagery: State-of-the-Art and Future Directions

Adam, M., Song, J., Yu, W., & Li, Q. (2025) · Future Internet · doi:10.3390/fi17090431 · Cited by 13

YOLOv8n-CA: Improved YOLOv8n Model for Tomato Fruit Recognition at Different Stages of Ripeness

Gao, X., Ding, J., Zhang, R., & Xi, X. (2025) · Agronomy · doi:10.3390/agronomy15010188 · Cited by 13

A Novel Curriculum Learning Training Strategy for Pomegranate Growth Stage Classification Using YOLO Models on Multi-Source Datasets for Precision Agriculture

Rani, N. S., Bhavya, K. R., Vadivel, A., Vasudev, T., Devadas, R. M., & Hiremani, V. (2025) · IEEE Access · doi:10.1109/access.2025.3581794 · Cited by 12

Advances in agricultural fruit detection using you only look once (YOLO) algorithm: A review

Pugazhendi, P., Badgujar, C., Sapkota, R., Dhillon, R., Rajesh, S., Sheela, J. J. J., & Raja, G. M. (2026) · Smart Agricultural Technology · doi:10.1016/j.atech.2026.101896 · Cited by 12

JassidNet as a quantization-aware lightweight phenotyping framework for high-throughput cotton jassid (Amrasca biguttula) detection and counting toward objective resistance screening

Wang, R.-F., Cui, K., Schardong, I. B., Bauer, M. C., Somala, R. V., Xu, M., West, D., Jones, D. C., Taylor, S. V., Roberts, P. M., et al. (2026) · Industrial Crops and Products · doi:10.1016/j.indcrop.2026.123716 · Cited by 13

Optimizing Deep Learning Algorithms for Effective Chicken Tracking through Image Processing

Mehdizadeh, S. A., Siriani, A. L. R., & Pereira, D. F. (2024) · AgriEngineering · doi:10.3390/agriengineering6030160 · Cited by 12

Performance Analysis of YOLO and Detectron2 Models for Detecting Corn and Soybean Pests Employing Customized Dataset

de Almeida, G. P. S., dos Santos, L. N. S., Souza, L. R. D. S., Gontijo, P. D. C., de Oliveira, R., Teixeira, M. C., Oliveira, M. D., Teixeira, M. B., & França, H. F. D. C. (2024) · Agronomy · doi:10.3390/agronomy14102194 · Cited by 12

Research on inferior litchi fruit detection in orchards based on YOLOv8n-BLS

Xie, J., Liu, J., Chen, S., Gao, Q., Chen, Y., Wu, J., Gao, P., Sun, D., Wang, W., Shen, J., et al. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110736 · Cited by 12

Unharvested palm fruit bunch ripeness detection with hybrid color correction

Chang, C., Parthiban, R., Kalavally, V., Hung, Y. M., & Wang, X. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100643 · Cited by 11

A computer vision and RFID fusion-based method for measuring individual feed intake and its application for detecting individual differences in feed efficiency of large yellow croaker (Larimichthys crocea)

Feng, M., Jiang, P., Ke, Q., Liu, S., Chen, Y., Du, Y., Luo, W., Liu, Y., Cai, Q., Zeng, Z., et al. (2024) · Water Biology and Security · doi:10.1016/j.watbs.2024.100332 · Cited by 11

From Crown Detection to Boundary Segmentation: Advancing Forest Analytics with Enhanced YOLO Model and Airborne LiDAR Point Clouds

Liu, Y., Ai, Z., & Gao, P. (2025) · Forests · doi:10.3390/f16020248 · Cited by 11

WeedsSORT: A weed tracking-by-detection framework for laser weeding applications within precision agriculture

Jin, T., Liang, K., Lu, M., Zhao, Y., & Xu, Y. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.100883 · Cited by 11

C3Ghost and C3k2: performance study of feature extraction module for small target detection in YOLOv11 remote sensing images

Xiao, R., Wang, H., Wang, L., & Hong, Y. (2025) · International Conference on Big Data, Computational Intelligence, and Applications (BDCIA) · doi:10.1117/12.3059792 · Cited by 13

A novel Er3+ activated Y4Al2O9 nanophosphors with high thermal stability for applications in w-LEDs and latent fingerprints detection

Arunakumar, R., Krushna, B. R. R., Mamatha, G. R., Sharma, S., Bommalingaiah, B., Samantsinghar, P., Inbanathan, J., Meyyammai, C. T., Ramakrishna, G., Manjunatha, K., et al. (2025) · Journal of Luminescence · doi:10.1016/j.jlumin.2025.121215 · Cited by 11

YOLOv9-LSBN: An Improved YOLOv9 Model for Cotton Pest and Disease

He, R., Li, P., Zhu, J., Zhang, F., Yang, D., Zhang, T., Yang, D., & Zhou, B. (2025) · IEEE Access · doi:10.1109/access.2025.3578967 · Cited by 11

Advancing Intelligent Logistics: YOLO-Based Object Detection with Modified Loss Functions for X-Ray Cargo Screening

Tee, J. H., Solihin, M. I., Chong, K. S., Tiang, S. S., Tham, W. Y., Ang, C. K., Lee, Y. J., Goh, C. L., & Lim, W. H. (2025) · Future Transportation · doi:10.3390/futuretransp5030120 · Cited by 14

Progress in Implementing the European Union Coordinated Plan on Artificial Intelligence (Volume 2)

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Round-the-clock accurate sheep face recognition via frequency enhancement and cross-modal embedding generation

Xu, X., Song, H., Pan, H., Chen, D., & Yang, S. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110918 · Cited by 10

Optimized YOLOv8 based on SGW for surface defect detection of silicon wafer

Zhu, G., Peng, J., Sheng, L., Chen, T., He, Z., & Lu, X. (2024) · Physica Scripta · doi:10.1088/1402-4896/ad8cad · Cited by 10

YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11

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Smart Edge Computing Framework for Real-Time Brinjal Harvest Decision Optimization

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A Systematic Literature Review of You Only Look Once Architectures (v1–v12) in Healthcare Systems

Şahingöz, Ö. K., Baydoğmus, G. K., & Kugu, E. (2026) · Diagnostics · doi:10.3390/diagnostics16060935 · Cited by 10

CBLN-YOLO: An Improved YOLO11n-Seg Network for Cotton Topping in Fields

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YOLOv8-DBW: An Improved YOLOv8-Based Algorithm for Maize Leaf Diseases and Pests Detection

Gan, X., Cao, S., Wang, J., Wang, Y., & Hou, X. (2025) · Sensors · doi:10.3390/s25154529 · Cited by 9

Real‐Time Automatic Detection of Nutrient Deficiency in Lettuce Plants With New YOLOV12 model

Bakır, Ç., & Gezer, A. (2025) · Journal of Sensors · doi:10.1155/js/5592225 · Cited by 9

A human location and action recognition method based on improved Yolov11 model

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BGWL-YOLO: A Lightweight and Efficient Object Detection Model for Apple Maturity Classification Based on the YOLOv11n Improvement

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Few-Shot Adaptation of Grounding DINO for Agricultural Domain

Singh, R., Puhl, R. B., Dhakal, K., & Sornapudi, S. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00530 · Cited by 12

AI-powered UAV remote sensing for drought stress phenotyping: Automated chlorophyll estimation in individual plants using deep learning and instance segmentation

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Quantum-omni: a quantum computing and GPT-4o solution for camel-vehicle collisions

Abaddi, S. (2025) · International Journal of Transportation Science and Technology · doi:10.1016/j.ijtst.2025.02.002 · Cited by 9

YOLO deep learning algorithm for object detection in agriculture: a review

Kamalesh Kanna, S., Kumaraperumal, R., Pazhanivelan, P., Jagadeeswaran, R., & Prabu, P. C. (2024) · Journal of Agricultural Engineering · doi:10.4081/jae.2024.1641 · Cited by 65

Advancing sustainable computing in agriculture: a review of YOLO-based object detectionPreprint

Junior, K. D. S., Maggi, M. F., Mahl, D., & Oliveira, D. N. D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5465457

YOLO-Based Vision Perception and Automation for Agricultural Robots: A Comprehensive Survey of Algorithms, Applications, and Deployment

Ping, H., Zhang, Z., Song, C., & Ping, T. (2026) · Sensors · doi:10.3390/s26206637

YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domainPreprint

Alif, M. A. R., & Hussain, M. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2406.10139 · Cited by 173

Advances in agricultural fruit detection using you only look once (YOLO) algorithm: A review

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