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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Feature Pyramid Networks for Object Detection

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Software survey: VOSviewer, a computer program for bibliometric mapping

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YOLOv8-based real-time obstacle detection in farmland environments for heavy-load agricultural UAVs

Liu, S., Li, Y., Wu, M. T., Manman, D., & Jing, M. (2026) · Scientific Reports · doi:10.1038/s41598-026-63187-7

Identification and reconstruction of grid-distributed wires in dense kiwifruit canopies based on geometric feature grouping-connection for robotic harvesting

Liu, X., Murengami, B. G., Gong, Q., Ma, X., Li, R., Zhou, X., Zhou, B., Chen, J., Sahni, R. K., Majeed, Y., et al. (2026) · Information Processing in Agriculture · doi:10.1016/j.inpa.2026.07.007

DCC-YOLOv8n: a lightweight model for maize seedling and weed recognition in complex farmland environments

Cui, J., Hao, S., Yang, Y., Chen, Z., & Bai, N. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2026.1901787

LGM-Net: Morphology-Guided Multi-scale Representation Learning for Stellera chamaejasme Detection in Complex GrasslandsPreprint

Wang, S., 严付桃, Fan, Z., Feng, L., Wu, Y., Yao, Q., Gao, D., & Zhang, Y. (2026) · Research Square · doi:10.21203/rs.3.rs-10955832/v1

A Lightweight Identification Method for Banana Leaf Diseases Based on Improved YOLOv11n

Wu, J., Huang, Y., Chen, Q., Li, C., Lan, Y., & Xiong, W. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.112273

MC-RT-DETR: A Lightweight Real-Time Cotton Top Bud Detection Model Based on Improved RT-DETR

Kuwan, Y., Ailimujiang, G., Qi, X., Liu, X., Cao, W., & Hudabaierdi, M. (2026) · Applied Sciences · doi:10.3390/app16157726

Automated Grape Classification Using a YOLOv11n-Based Computer Vision System

León, R. A. L., Tapia, M. S. R., & Pineda, B. A. S. (2026) · LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI) · doi:10.18687/laccei2026.1.1.1477

Optimizing Oil Palm Detection: A Deep Dive into Algorithm Performance and Software Metrics

Harefa, J., Soeparno, H., Arifin, Y., & Gaol, F. L. (2026) · International Conference on Intelligent Engineering and Next-Gen Healthcare Systems (IEHNS) · doi:10.1109/iehns68708.2026.11606837

Real Time Automated Surface Defect Detection in Food Products

Yazhini, M. C., & Mahalakshmi, M. (2026) · International Conference on Data Science and Business Systems (ICDSBS) · doi:10.1109/icdsbs69077.2026.11634642

NCSSD-YOLO: a non-causal state space duality-based instance segmentation model for pacific white shrimp

Ni, W., Zhang, C., Zhang, Y., Li, S., Wang, H., Wu, C., Luo, L., Liang, Q., Peng, Z., Li, S., et al. (2026) · Aquaculture International · doi:10.1007/s10499-026-02644-6

CNN-enabled roguing implement for parental-line management and purity assurance in hybrid rapeseed via a mobile field robot

Xu, Y., Ding, S., Sun, Z., Liu, Q., Le, F., Cui, L., Cai, C., Teki̇nerdoğan, B., & Xue, X. (2026) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2026.07.012

Arquitecturas YOLO y RF DETR para el diagnóstico temprano de enfermedades en Solanum tuberosum L

Espinoza, M. S. M. (2026) · CIENTÍFICA CEDIA · doi:10.61854/rccedia.v1n1.005

ADDDeN: Asymmetric dual-backbone network for cross-species NIR-TIR death detection in high-density caged farmingPreprint

Han, Y., Wang, L., Chen, Z., & Wang, H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7289611

YOLO-Guided Visual Information System for Autonomous Robotic Arm Control in A 3D Simulated Environment

Ahmadi, I. F., Putra, S. A., & Siregar, S. (2026) · International Conference on Smart Mechatronics (ICSMech) · doi:10.1109/icsmech69390.2026.11647238

ECA-Enhanced YOLOv8n for Lightweight Mango Pest and Disease Detection

Liu, X., Zhang, Q., Wang, Z., & Cheng, J. (2026) · International Conference on IoT, Data Science and Advanced Computing (IDSAC) · doi:10.1109/idsac70973.2026.11650629

High-Precision Detection of Homona magnanima and Adoxophyes honmai in Mandarin Orchards Using Deep Learning

Kwon, S. H., Chung, Y. S., Ahn, J., Park, K. J., & Tuan, T. T. (2026) · ACS Agricultural Science & Technology · doi:10.1021/acsagscitech.5c00978

A Grape Leaf Disease Detection Method Based on Improved YOLOv10n

Chen, K., Liu, P., Fang, C., Zhang, Y., & Huang, L. (2026) · International Conference on Bioinformatics and Intelligent Computing (BIC) · doi:10.1145/3809986.3810015

YOLO11-FAL: An Improved YOLO11 Model for Tomato Flowering Stage Detection in Greenhouses

Zhang, H., Hu, W., Zhou, Z., Ma, X., Xu, S., Miao, Z., Xie, Y., & Wang, Y. (2026) · Applied Sciences · doi:10.3390/app16178393

Yolov8 with P2 Multi-Scale Fusion and Adaptive ECA for Wheat Head Detection

Zhu, H., & Wu, S. (2026) · World Conference on Computer and Information Security (WCCIS) · doi:10.1109/wccis70285.2026.11650996

Machine Learning and Deep Learning Techniques for Weed Detection in Agricultural Fields: Recent Advances, Challenges, and Future Directions

Bodhale, A., & Verma, S. (2026) · International Journal of Computer Information Systems and Industrial Management Applications · doi:10.70917/ijcisim-2026-5034

Towards Intelligent: A Robust Attention-Enhanced YOLO Framework for Oudemansiella raphanipes Detection in Factory Cultivation Systems

Zhou, H., Wang, K., Wang, H., Yin, H., & Huang, J. (2026) · Agronomy · doi:10.3390/agronomy16171639

RESEARCH ON GRAPE DISEASE DETECTION METHOD BASED ON PWE-YOLO

Haoyue, L. I. U., Benzhi, Y. A. N. G., Gao, M., Dong, W. A. N. G., Yuepeng, S. O. N. G., & Longlong, R. E. N. (2026) · INMATEH Agricultural Engineering · doi:10.35633/inmateh-79-40

A scheduling framework for heterogeneous tractor fleets under operational disturbances: A case study of crop protection operations at Zhaoguang Red-Star Farm

Xu, Y., Liu, Q., Cai, C., Gu, W., Sun, Z., Jin, Y., Qin, W., Sun, Y., Cui, L., Chang, C., et al. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.112356

Deteksi Hewan Perusak Tanaman Menggunakan YOLOv8 Berbasis ESP32-CAM

Rizal, S., & Idifitriani, F. (2026) · Techno Com · doi:10.62411/tc.v25i3.16251

Paddy parcel detection method using drone images based on YOLO-Seg model and tiling movement

Jeon, J., Kim, S., & Kim, T. (2026) · Paddy and Water Environment · doi:10.1007/s10333-026-01092-5

Bridging the domain gap: A transferable dynamic routing enhancer for robust aerial detection under adverse weather

Wan, Y., Li, J., Lin, L., Lv, Z., Zhang, Z., Yuan, Q., & Shen, H. (2026) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2026.08.041

UAV Based Automated Surveillance of Ganoderma boninense in Oil Palm Canopies Using YOLO26 Architecture

Pribadi, M. R., Irsyad, H., Widiyanto, E. P., Setianto, M. T., & Pratiwi, S. I. (2026) · Journal of Embedded Systems Security and Intelligent Systems · doi:10.59562/jessi.v7i3.11502

Real-time hass avocado ripeness detection using close-range UAV imagery and YOLO-based deep learning

TACO, V. L. T., ACCOSTUPA, W. E. Q., & Espinoza, E. S. (2026) · Smart Agricultural Technology · doi:10.1016/j.atech.2026.102547

Method for lightweight litchi fruit detection in complex environments via improved YOLO11n-MAS

Jing, L., Yaozhong, M., Zuo, Z., & Shengkang, P. (2026) · International journal of agricultural and biological engineering · doi:10.25165/j.ijabe.20261904.10519

Detection of tobacco seedlings in plastic-mulched fields using vegetation indices and YOLOv11

Zhao, L., Wang, H., Wu, D., Liu, J., Li, Z., & Shi, Y. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2026.1951012

Class merging degrades learned representations in potato-harvesting object detection despite operational equivalence

Kim, J., Yoshitoshi, R., Kim, G., Deguchi, N., Kainuma, H., Tsuchiya, S., & Tokuda, K. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2026.1847038

AI for Sugarcane Stem Node Quality Assessment and Bud Germination Suitability: A Review

Magar, M., & Bagane, P. (2026) · International Journal of Artificial Intelligence and Machine Learning · doi:10.51483/ijaiml.6.9s.2026.1891-1904

SunFarming: Deep Neural Networks for Farming Under Photovoltaic Power Stations

Thottempudi, S. G., Sarawgi, M., Mnkandla, E., Moulla, D. K., Agbo-Ajala, J. O., Attipoe, D. S., & Akinyemi, L. A. (2026) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-032-08820-8_6

Comparative Performance of Lightweight and Medium YOLO Models for Conveyor-Based Chili Ripeness Detection

Taqiya, M. A. H., Khalil, F. I., Sadimantara, M. S., Arief, M. A. A., & Kurniawan, H. (2026) · Jurnal Keteknikan Pertanian Tropis dan Biosistem · doi:10.21776/ub.jkptb.2026.014.02.05

A crop cultivation monitoring platform for evaluating the early growth of cucumbers

Lei, D., Zhu, Z., Penghong, S., Zhong, Z., Moustafa, M. A., Xian, J., Wu, H., & Fu, X. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2026.1869455

An Annotated Aerial Dataset for Training and Benchmarking Computer Vision Models in Turkey Behavior Analysis

Calderone, G., Palazzetti, L., & Casella, E. (2026) · Scientific Data · doi:10.1038/s41597-026-08245-5

CSO-YOLO: A Cross-Species Occlusion-Aware Object Detection Framework for Soybean–Maize Strip Intercropping Fields

宋昊华, & Cai, L. (2026) · International Conference on Intelligent Systems and Robotics (CISR) · doi:10.1109/cisr70722.2026.11680279

Stored-product insect pest detection and identification using deep learning-based computer vision methods: A systematic literature review

Tadiparthi, S., Badgujar, C., Gerken, A. R., Scheff, D. S., Pn, G., & Samiappan, S. (2026) · Journal of Stored Products Research · doi:10.1016/j.jspr.2026.103228

Structure-Aware RGB Channel Reconstruction for YOLOv8-Based Oyster Mushroom Detection

Duman, S., Gözükara, F., Yetgín, Z., & Avaroğlu, E. (2026) · Agriculture · doi:10.3390/agriculture16181985

TB-YOLOv8n: an improved YOLOv8n-based model for tip-burn detection in plant factory-grown pakchoi under LED lighting

Wang, P., Hu, W., Ma, X., Xu, S., Zhou, Z., & Wang, Y. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2026.1933084

A Lightweight RGB Vision-Based Express Parcel Detection Framework with Active Learning and Quantization-Aware Training

Wu, A.-Q., Guo, F., Yang, W., Wang, R.-F., & Hu, P. (2026) · Sensors · doi:10.3390/s26196036

Real-Time Detection and Yield Estimation of Rosa damascena Using YOLOv10-N: A Case Study in the Dades Oasis, Drâa-Tafilalet, Southeastern Morocco

Ohamouddou, M., & Ohamouddou, S. (2026) · Journal of Global Innovations in Agricultural Sciences · doi:10.22194/jgias/27.1790

An Edge-Based Computational Intelligence Framework for Vision-Driven Cattle Health Monitoring in Smart Agriculture

Geetha, S., Panda, S., Pagala, P. S., & Anandhan, K. (2026) · IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · doi:10.1109/conecct68978.2026.11700669

A Terraced Tea Bud Detection Algorithm Based on Multi-Scale Feature Fusion

Liu, S., Li, X., Li, P., & Jing, M. (2026) · IEEE Access · doi:10.1109/access.2026.3737867

Performance Oriented Faster YOLOv7 Variant for Real Time Industrial SystemsPreprint

Coban, C. (2026) · Research Square · doi:10.21203/rs.3.rs-9439428/v1

Detection of Leaf Diseases in Potato Crops Using a Lightweight Deep Learning Model for Mobile Devices

Pusdá-Chulde, M., Tirira, D. E. C., Caranqui-Sánchez, V., & García-Santillán, I. D. (2026) · Symmetry · doi:10.3390/sym18101652

An Edge-Oriented Spatio-Temporal Weed Tracking Framework for Robotic Laser Weeding

Jin, T., Liang, K., Zhao, Y., & Ye, R. (2026) · International Conference on Embodied Intelligence, Robotics, and Control Systems (EIRCS) · doi:10.1109/eircs70783.2026.11705775

Cabbage Detection and Counting Using YOLOv8-CBAM and Improved BoT-SORT for Greenhouse and Open Field Cultivation in Korea

Reza, M. N., Ali, M. R., Rashid, M. M., Lee, H., Jang, Y. K., Zakir, E., Atulinda, C. N., & Chung, S. (2026) · Sensors · doi:10.3390/s26196268

Research Evolution and Emerging Trends in Fruit and Vegetable Quality Assessment and Sorting: A Bibliometric Analysis of Analytical and Intelligent Sensing TechnologiesPreprint

Zhang¹, Y., Wu¹, C., Ding¹, Y., & Wu¹, D. (2026) · Research Square · doi:10.21203/rs.3.rs-11126039/v1

Deep Learning for Precision Viticulture: A YOLOv8 Approach to Automated Pruning Segmentation

Manoni, L., Falaschetti, L., Angelo, E. D., Senesi, A., Orcioni, S., & Conti, M. (2026) · IEEE International Conference on Omni-layer Intelligent Systems (COINS) · doi:10.1109/coins69995.2026.11715611

A rapid detection algorithm for unsound kernels in wheat acquisition based on RSA-YOLO

Zhao, Z., Xue, W., & CaiZhang, W. (2026) · Journal of Food Measurement & Characterization · doi:10.1007/s11694-026-05016-z

Design and development of IoT based solar power agriculture robot

Ali, M., Ullah, S., Ullah, A., Khan, M. D., & Hassan, M. (2026) · Discover Robotics · doi:10.1007/s44430-026-00040-6

Feature Refinement via C2f-Attention: An Enhanced YOLOv8 Approach for Tomato Ripeness Detection

Vo, H.-T., Phuc, V. N., Thien, N. N., & Mui, K. C. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-6510-7_34

An Energy-Efficient Edge Vision System for Front-End Quality Monitoring in Sustainable Cold Chains: Coffee Fruit Ripeness Detection and Embedded Validation

Cui, X., & Guo, S. (2026) · Sensors · doi:10.3390/s26206535

Object Detection for Pepper Harvesting: A Comparative Evaluation of Backbone-Adapted, Contemporary, and Open-Vocabulary Models

Vilcapoma, P., Vásconez, I. N., Prado, A., Moya, V., Guevara, L., & Vásconez, J. P. (2026) · Agriculture · doi:10.3390/agriculture16202243

Intelligent network image analysis-driven diffusion model and improved YOLOv12 wire rope damage detection

Hu, X., Zhang, Y., Wen, P., & Khan, M. (2026) · International Journal of Intelligent Networks · doi:10.1016/j.ijin.2026.10.004

Growth-Point Detection and 3D–2D Two-Stage Localization of Cucurbit Rootstock Seedlings Using YOLO11-Pose-LGA

Chen, Z., Wu, Z., Shang, X., Zheng, J., Li, X., Gu, M., Mou, Y., Ouyang, Q., & Gu, S. (2026) · Agriculture · doi:10.3390/agriculture16212304

YOLO12n-ACOD for Multi-Class Strawberry Ripeness Detection in Smart Agriculture

Ram, M. L., Oraño, J. F. V., Flores, J. B., Olaybar, J. A., Dayola, G. A., & Malangsa, R. D. (2026) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec70158.2026.11736018

DLMI-SAM: Direct Latent Memory Injection into Foundation Models for Robust Human-in-the-Loop Annotation in Precision AgriculturePreprint

Sim, M., Seo, D., Noh, D. H., Lee, H.-M., & Baek, N. R. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7661437

Dynamic real-time detection of maize ear dehusking clearance rate based on deep learning and multi-object tracking

Wang, N., Zhang, X., Yin, Y., Xia, J., Zhang, J., Li, G., Yang, C., Wang, W., & Zhao, L. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.112575

Enhanced cotton leaf disease classification Through ConvNeXt and Random Walk Grey Wolf Optimization

Wagh, Y., Dobariya, A. R., & Nayak, A. K. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02566-5

From morphology to species identity: CA-MFE-SwinT for fine-grained grasshopper recognition

Liu, M., Zhang, Y., Chen, J., Yin, H., Zou, B., Hao, H., Xiong, P., & Li, X.-J. (2026) · Smart Agricultural Technology · doi:10.1016/j.atech.2026.102670

Aprendizaje profundo para la detección de malezas en sistemas agrícolas y forestales mesoamericanos

Guevara-Bonilla, M., León, R. G., & Gómez, R. G. (2026) · Agronomía Mesoamericana · doi:10.15517/eyatrf23

Investigation of the cognitive saturation threshold of the YOLOv8 model under limited training data

Karelina, M. Y., Akulov, A. A., & Taldykin, D. S. (2026) · T-Comm - Телекоммуникации и Транспорт · doi:10.36724/2072-8735-2026-20-9-68-75

A review of artificial intelligence technologies for mechanized Camellia oleifera fruit harvesting

Jin, S., Zhou, L., Zhou, H., & Hongzhe, 姜. J. (2026) · Industrial Crops and Products · doi:10.1016/j.indcrop.2026.124677

Integrating YOLOv11 with Robotic Arms for Real-Time Shape Detection and Classification

Owoeye, S. O., Durodola, F. O., Adedokun, M., & Erinkitola, M. (2026) · ASRIC Journal on Engineering Sciences · doi:10.68327/ondi6119

Computer vision and edge AI for crop health monitoring: a critical review of field validation and deployment evidence

Lal, S., Kashtriya, P., & Singh, P. (2026) · Discover Computer Vision · doi:10.1007/s44584-026-00006-9

HCF-YOLO: High-Resolution Center-Guided Feature Fusion for Dense Cotton Seedling Detection in UAV Imagery

Li, Y., Su, H., Zhang, W., Zhao, Y., & Wang, Y. (2026) · Sensors · doi:10.3390/s26216842

Data-Partitioning Sensitivity and Within-Session Stage Transfer in Vision-Based Garlic Soil Residue Grading on a Soil-Removal Test Bench

Qiu, S., Zhu, X., Fu, J., Zhao, Q., Zang, J., & Li, H. (2026) · Agriculture · doi:10.3390/agriculture16212366

Pocket Phenomics: A Smartphone-based Framework for Quantifying Maize Architectural Traits in Field

Gao, Y., Chen, J., Wang, B., Feng, H., Xue, G., Wu, C., Xiao, Y., Li, W., Zhang, J., Zhang, S., et al. (2026) · Smart Agricultural Technology · doi:10.1016/j.atech.2026.102693

Research progress and technical pathways of deep learning in agricultural remote sensing: a bibliometric and Task–Data–Model analysis

Qian, Y., Zhou, Z., Li, Q., Huang, D., Ao, G., & Wei, L. (2026) · Geocarto International · doi:10.1080/10106049.2026.2756784

CO-YOLO: An Improved YOLOv11 Detector for Camellia oleifera Fruit Detection Under Complex Orchard Conditions

Huang, Q., Pei, D., Rao, H., Ye, L., Wang, Y., & Wei, Q. (2026) · Agronomy · doi:10.3390/agronomy16212168

Deep Learning-Based Quality Grading of Oranges: An Implementation of Real-Time Object Detection for Automated Fruit Sorting

Hariyanto, D., Na’am, J., Marlinda, L., & Haris, M. (2026) · International Conference on Information Technology Research and Innovation (ICITRI) · doi:10.1109/icitri71999.2026.11751235

A Lightweight Strawberry Ripeness Detection Network for Edge-Deployed Harvesting Devices

Luo, J., Wu, X., Liu, G., & Han, H. (2026) · doi:10.1109/iotaiee70863.2026.11749425

Harvest-Oriented Maturity Recognition and Bud Orientation Estimation in Kushui Roses

Wang, K., Zhang, Z., Manzoor, S. H., Ahmed, A., Wang, Z., Niu, Y., Hu, C., & Wang, Y. (2026) · Smart Agricultural Technology · doi:10.1016/j.atech.2026.102711

Pedestrian detection in occluded maize harvesting environments based on thermal infrared imaging

Liang, Y., Xiang, Y., Zhang, Z., & Zhou, K. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.112599

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

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