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

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

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

Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472

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

Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333

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

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566

Microsoft COCO: Common Objects in Context

Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684

Feature Pyramid Networks for Object Detection

Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939

Software survey: VOSviewer, a computer program for bibliometric mapping

van Eck, N. J., & Waltman, L. (2010) · Scientometrics · doi:10.1007/s11192-009-0146-3 · Cited by 21,484

YOLO-Light: Automatic Lightweight You-Only-Look-Once Generation in Different Scenarios Through NeuroEvolution

Shuai, Z., Yu, T., Zhang, B., Ren, C., Wang, L., Jiang, X., Li, L., & Shuai, J. (2025) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2025.3617095 · Cited by 2

Research on Road Traffic Sign Detection Based on Edge Intelligence

He, Y., & Xie, B. (2025) · International Conference on Artificial Intelligence, Big Data and Algorithms (CAIBDA) · doi:10.1109/caibda65784.2025.11182886 · Cited by 2

Chili Ripeness Level Detection with YOLOv8 and Fuzzy Logic for Harvest Decision Making

Rahmawati, N. Y. D., & Rochim, F. P. (2025) · Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) · doi:10.23960/jtepl.v14i5.1807-1818 · Cited by 2

A Deployment-Oriented Benchmarking of You Look Only Once (YOLO) Models for Orange Detection and Segmentation in Agricultural Robotics

Beldek, C., Sarıyıldız, E., & Alıcı, G. (2025) · Agriculture · doi:10.3390/agriculture15202170 · Cited by 2

YOLOv11-MFF: A multi-scale frequency-adaptive fusion network for enhanced CXR anomaly detection

Guan, L., Zhang, R., & Zhao, Y. M. (2025) · PLoS ONE · doi:10.1371/journal.pone.0334283 · Cited by 2

Design, integration, and evaluation of a dual-arm robotic system for high throughput sampling from potato tubers

Divyanth, L. G., Sabir, S. U. B., Rathore, D. S., Khot, L. R., Mattupalli, C., & Karkee, M. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.111266 · Cited by 2

Cover damage detection in round cotton modules using convolutional neural networks (CNNs)

Iqbal, M. Z., Hardin, R. G., Peeples, J., & Barnes, E. M. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.111023 · Cited by 2

Application of the Improved YOLOv8-DeepSORT Framework in Motion Tracking of Pepper Leaves Under Droplet Occlusion

Guo, F., Liu, K., Ma, J., & Qiu, B. (2026) · Agronomy · doi:10.3390/agronomy16030384 · Cited by 2

HDA-YOLO: a hierarchical and densely-fused attention network for rice pest detection in complex agricultural environments

Yuan, S., Duan, Y., Su, H., Zhou, X., & Hao, Y. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2026.1763650 · Cited by 2

RGE-YOLO: a lightweight and robust detector for infrared ship detection via global–local feature alignment and empirical attention

Wang, Y., Zhang, Y., Li, X., & Chen, Z. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae5730 · Cited by 2

Review of automatic row alignment technology for intelligent agricultural machinery in the field

Cui, S., Kong, F., Tian, K., Xie, Q., Liu, Y., Zhang, B., & Mu, Z. (2026) · Smart Agricultural Technology · doi:10.1016/j.atech.2026.102059 · Cited by 2

Advanced deep learning vision transformer models for intelligent grain counting in agricultural data analytics

Jiangping, C., Jing, D., & Cao, X. (2026) · Scientific Reports · doi:10.1038/s41598-026-49819-y · Cited by 2

High-throughput phenotypic analysis of plant and curd growth dynamics during the whole growth period of cauliflower based on instance segmentation

Cheng, S., Wu, S., Shao, Q., Chen, M., Liu, J., Qiu, B., & Zhu, H. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2026.1836813 · Cited by 2

Citrus instance segmentation and yield prediction based on parallel multiscale channel enhanced

Zhang, D., & Chu, H. (2026) · Scientia Horticulturae · doi:10.1016/j.scienta.2026.114895 · Cited by 2

YOLO-CornSeg: A Lightweight Segmentation Model for Corn Seedlings with an Indirect Weed Detection Strategy

Lei, J., Yu, J., Han, K., Li, M., Jin, X., & Yin, H. (2026) · Agronomy · doi:10.3390/agronomy16111091 · Cited by 2

Automated detection of defective coffee beans based on improved YOLOv10 framework

Hong, S., Zhang, D., Chi, H. ‐., He, J., Guo, X., Fu, H., & (45927), L. W. (2026) · Current Research in Food Science · doi:10.1016/j.crfs.2026.101461 · Cited by 2

Agri-DETR: An Efficient Visual Obstacle Detection Framework for Intelligent Agricultural Machinery in Unstructured Field Environments

Fan, H., Xi, J., Chen, X., & Sun, B. (2026) · Agriculture · doi:10.3390/agriculture16121361 · Cited by 2

Small Target Detection in Agricultural Visual Perception: Progress and Challenges

Li, H., Cheng, H., Niu, Q., Li, C., Wang, L., He, X., Yang, Y., & Wang, P. (2026) · Agriculture · doi:10.3390/agriculture16131366 · Cited by 2

Autonomous crop and weed detection in multiple agricultural fields using YOLO-based models with combined real and synthesized images

Anh, P. T. Q., Ota, K., Yoshida, T., & Chiba, M. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.112102 · Cited by 2

SA-SRYOLOv8: A Research on Star Anise Variety Recognition Based on a Lightweight Cascaded Neural Network and Diversified Fusion Dataset

Chen, H., Zhang, F., Guo, C., Yi, J., & Ma, X. (2024) · Agronomy · doi:10.3390/agronomy14102211 · Cited by 2

Detection and Quantification of Citrus Long-horned Beetle (Anoplophora chinensis) in Hazelnut Orchards Using YOLOv5 Deep Learning

Şin, B., & Öztürk, L. (2025) · Applied Fruit Science · doi:10.1007/s10341-025-01418-3 · Cited by 2

YOLO-AWK: A Model for Injurious Bird Detection in Complex Farmland Environments

Yang, X., Cheng, Y., Dong, M., & Xie, X. (2025) · Symmetry · doi:10.3390/sym17081210 · Cited by 2

Review of The Current State of Deep Learning Applications in AgriculturePreprint

Keskes, M. I. (2025) · doi:10.31219/osf.io/xg4rs_v2 · Cited by 2

Review of The Current State of Deep Learning Applications in AgriculturePreprint

Keskes, M. I. (2025) · Preprints.org · doi:10.20944/preprints202504.1290.v2 · Cited by 2

Enhancing Bee Mite Detection with YOLO: The Role of Data Augmentation and Stratified Sampling

Lee, H., Shin, J.-Y., Kim, S.-B., Kim, M.-J., Kim, M. S., Lee, H., & Mo, C. (2025) · Agriculture · doi:10.3390/agriculture15111221 · Cited by 3

YOLO-PTHD: A UAV-Based Deep Learning Model for Detecting Visible Phenotypic Signs of Pine Decline Induced by the Invasive Woodwasp Sirex noctilio (Hymenoptera, Siricidae)

Yang, W., Zhao, J., Zhu, D., Wang, Z., Song, M., Chen, T., Liang, T., & Shi, J. (2025) · Insects · doi:10.3390/insects16080829 · Cited by 1

WSF: A Transformer-Based Framework for Microphenotyping and Genetic Analyzing of Wheat Stomatal Traits

Zhou, H.-H., Min, H., Liang, S., Qin, B., Sun, Q., Pei, Z., Pan, Q., Wang, X., Cai, J., Zhou, Q., et al. (2025) · Plants · doi:10.3390/plants14193016 · Cited by 1

Crop Disease Detection Based on Unetmamba with Hierarchical Feature Fusion

Wang, X., Zhang, T., Shao, Y., & Zhang, Y. (2025) · IEEE International Conference on Signal Processing and Machine Learning (SPML) · doi:10.1109/spml66318.2025.11199761 · Cited by 1

Comparative Analysis of Series YOLO Models for Loose Fruits Palm Oil Detection: A Comprehensive Performance Evaluation

Warni, E., Indrabayu, & Achmad, A. (2025) · International Conference on Wireless Networks and Mobile Communications (WINCOM) · doi:10.1109/wincom65874.2025.11313397 · Cited by 1

YOLOv5-based dense rice seed counting method integrating C3CBAM and Soft-NMS

Liu, X., Huang, X., Zhu, R., Hu, C., Wang, C., & Sun, C. (2026) · Smart Agricultural Technology · doi:10.1016/j.atech.2026.101779 · Cited by 1

From Wide-Area Screening to Precise Diagnosis: A Two-Step Air-Ground Collaborative Approach for the Detection of Citrus Huanglongbing

Dou, S., Yuan, S., Zhang, X., Hou, Y., Wu, G., Mei, Z., Song, Y., & Qi, G. (2026) · Agriculture · doi:10.3390/agriculture16020180 · Cited by 1

Intelligent Food Portioning System Using Vision-Language-Action (VLA) Models for Small-Scale Food Operations

Yang, R., Cai, S.-W., Zhou, L.-F., & Feng, Y.-M. (2026) · Journal of Future Foods · doi:10.1016/j.jfutfo.2025.12.047 · Cited by 2

Mobile AI-Powered Impurity Removal System for Decentralized Potato Harvesting

Kim, J., TOKUDA, K., Miho, Y., Kim, G., Yoshitoshi, R., Tsuchiya, S., Deguchi, N., & FUNABIKI, K. (2026) · Agronomy · doi:10.3390/agronomy16030383 · Cited by 1

Recognition, Localization and 3D Geometric Morphology Calculation of Microblind Holes in Complex Backgrounds Based on the Improved YOLOv11 Network and AVC Algorithm

Zhang, C., Xia, D., Chen, R., niu, Q., Wang, T., & Wang, L. (2026) · Journal of Imaging · doi:10.3390/jimaging12030096 · Cited by 1

Parameter-Reduced YOLOv8n with GhostConv and C3Ghost for Automated Blood Cell Detection

Yang, J., Yang, B., Li, Z., Yamaguchi, Y., & Xiao, W. (2026) · Bioengineering · doi:10.3390/bioengineering13030321 · Cited by 1

YOLO11-based deep learning and machine vision framework for automated detection and counting of Rhopalosiphum padi (Hemiptera: Aphididae)

Qin, W., Qian, C., Hamid, A., Abbas, S., Bi, C., Zhang, C., Zhao, J., Abbas, N., Ali, J., Wang, L., et al. (2026) · Journal of Economic Entomology · doi:10.1093/jee/toag048 · Cited by 1

MEGNet: A Multi-Scale Edge Geometry-Aware Network for Green Plum Detection in Picking Orchard Environment

Huang, W., Wang, J., Zhang, S., Chen, T., Zhao, C., Huang, G., & Zhou, Y. (2026) · Horticulturae · doi:10.3390/horticulturae12060682 · Cited by 1

A transformer-based path fusion detector network for tea bud detection

Zhang, T., Yang, Q., Tong, X., Hu, L., & Shao, J. (2026) · Smart Agricultural Technology · doi:10.1016/j.atech.2026.102321 · Cited by 1

Challenges for Mexican sheep production in the era of precision livestock farming and artificial intelligence

Arias, M. I. V., Camacho-Pérez, E., Lugo, F. C., Herrera-Camacho, J., Vargas‐Bello‐Pérez, E., & Chay‐Canul, A. J. (2025) · Veterinaria México OA · doi:10.22201/fmvz.24486760e.2025.1479 · Cited by 1

Review of Quasi-Solid Aqueous Zinc Batteries: A Bibliometric Analysis

Liu, Z., Zhou, X., Shen, T., Yu, M., Zhu, L., Xu, G., & Zhu, M. (2025) · Batteries · doi:10.3390/batteries11080293 · Cited by 1

Automatic Microsleep Detection Appoach for Car Drivers Using YOLO5 Based on Image Feature

Anifah, L., Nurhayati, N., Haryanto, H., & Zuhrie, M. S. (2025) · TEM Journal · doi:10.18421/tem143-07 · Cited by 1

Global impact and thematic evolution of object detection in the deep learning era

Roslin, A. H., & Muhammad, N. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00528-x · Cited by 1

A Capsule Network-Based Hybrid Model for Lung Nodule Detection

Mattmann, C., Hahn, D., Klaiber, M., Wagner, S., Fernandes, M., & Rössle, M. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.09.273 · Cited by 1

Real-Time Sugarcane Bud Detection Using Efficient Lightweight Convolutional Blocks

Kumar, P., Arora, S., & Arora, S. (2025) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-025-10906-3 · Cited by 1

Open-vocabulary models for object detection and segmentation in visual art: survey and comparative study

Yemelianenko, T., Tkachenko, I., Masclef, T., Scuturici, M., & Miguet, S. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21443-y · Cited by 1

A drone- and deep-learning-based harvest planning model for determining the optimal harvesting sequence of broccoli fields

Zhang, X., Jiang, L., Shen, A., & Wang, Z. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.111693 · Cited by 1

Machine Vision–Based Insect Recognition in Agriculture: A Comprehensive Review

Islam, M. M., Akter, M., Uddin, M. S., Rahman, M. A., & Habib, M. T. (2026) · Engineering Reports · doi:10.1002/eng2.70729 · Cited by 1

YOLO-LSBA: A high-precision model for detecting stems of small-sized cherry tomatoes

Liu, Q., Feng, C. H. E. N., Zhang, H., CAO, B., Yang, J., Zhang, N., Qiao, Y., Liu, Z., Mao, J., & Chen, M. (2026) · Scientific Reports · doi:10.1038/s41598-026-46348-6 · Cited by 1

Lightweight attention enhanced YOLOv11 for accurate multi class detection of brinjal diseases

Athiraja, A., Sujatha, E., Gurulakshmi, A. B., & Nagarajan, S. (2026) · Scientific Reports · doi:10.1038/s41598-026-47734-w · Cited by 1

Efficient dual-modality object detection with state-space fusion and Mix attention

Yang, H., Wang, S., Wang, J., Bo, Y., Wang, J., & Fortino, G. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132308 · Cited by 1

A truss-type field phenotyping platform combining Beidou georeferencing for in-situ monitoring of individual rapeseed in multi-growth stage

Wu, Z., Wang, Y., Bai, Z., Li, X., Dai, Z., Zhang, K., Yang, Y., lv, X., Zhang, F., Liu, L., et al. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.111792 · Cited by 1

Lightweight YOLOv11n model for efficient and accurate residual-film detection in cotton fields of arid regions

Yang, X., Niu, C., Shi, J., Lian, X., Zhang, S., Wang, H., Wang, X., Hu, C., & Zhang, H. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.111874 · Cited by 1

S2G-Net: an asymmetric cross-modal network for tiny cotton terminal-bud detection and sparse depth completion in real fields

Xue, S., Li, N., Li, Z., Wang, D., Zhu, T., Jing, X., Guo, H., & Ni, C. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.111875 · Cited by 1

Tomato-Adaptive Attention YOLOv8 for Accurate and Interpretable Maturity Detection Across Diverse Environments

Rahim, U. F., Rahman, M. M., & Mineno, H. (2026) · Agriculture · doi:10.3390/agriculture16101130 · Cited by 1

AEP-PEST: adaptive edge-enhanced pyramid network for real-time detection of small and occluded agricultural pests

Tang, Z., Wang, Y., Zou, D., & Qi, F. (2026) · Journal of Real-Time Image Processing · doi:10.1007/s11554-026-01901-4 · Cited by 1

A Stage-Aware Cascaded Detection–Segmentation Framework for Leaf Phenotyping and Leaf Dry Biomass Estimation of Pepper Seedlings

Li, H., Shi, D., Shi, H., Li, M., & Diao, M. (2026) · Plants · doi:10.3390/plants15121912 · Cited by 1

An appearance-independent multi-object tracking framework for group-housed pigs

Chen, L. D., Lü, Y., Wu, J., Tang, X., Cheng, G., Zhang, H., Fang, J., Y, C., & Han, S. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.112204 · Cited by 1

Comparison of Clove Quality Detection Performance Across Different Versions of the YOLO Algorithm

Sadimantara, M. S., & Herdhiansyah, D. (2026) · Journal of Global Innovations in Agricultural Sciences · doi:10.22194/jgias/26.1833 · Cited by 1

Fruit-stem structural visual perception for picking point localization in strawberry harvesting robots

Qing, J., Zeng, Z., Chen, J., Liang, X., Mo, B., Liu, Y., Zhou, A., & Wu, S. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2026.1894265 · Cited by 1

A Lightweight Deep Learning Framework for Real-Time Brinjal Detection Under Field Conditions

Pandey, A., Sahoo, P. K., Khura, T. K., Kushwaha, D. K., Parray, R. A., Ranjan, J. K., Haque, M. A., Sarkar, S. K., Gaddamwar, R. B., & Pradhan, N. C. (2026) · Automation · doi:10.3390/automation7040120 · Cited by 1

CBD-YOLO: a lightweight small-object detection algorithm for bud-stage Hangzhou white chrysanthemum in complex field environments

Wu, Y., Yu, C., Chen, J., Liu, X., & Wang, X. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2026.1928425 · Cited by 1

A comprehensive review of deep learning methods for weed classification in precision agriculture

Ekene, N. C., Okoye, F. A., Chidi, E. U., & NNENNA, O. M. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01916-7 · Cited by 1

On-Line Detection Method of Salted Egg Yolks with Impurities Based on Improved YOLOv7 Combined with DeepSORT

Gong, D., Zhao, S., Wang, S., Li, Y., Ye, Y., Huo, L., & Bai, Z. (2024) · Foods · doi:10.3390/foods13162562 · Cited by 1

Evaluation of segmentation and semi‐supervised learning for spotted spurge recognition in bermudagrass turf

Joseph, M., Gawron, K. A., Zhao, C., MacDonald, G. E., Schumann, A. W., Boyd, N. S., & Petelewicz, P. (2024) · International Turfgrass Society research journal · doi:10.1002/its2.152 · Cited by 1

Classification Of Hookah Images With Deep Learning

Arslan, M. T., & Özkaynak, E. (2024) · International Artificial Intelligence and Data Processing Symposium (IDAP) · doi:10.1109/idap64064.2024.10711111 · Cited by 1

Improved Grapes Detection and Tracking in Drones Imagery by Integrating the Coordinate Attention Mechanism

Ptak, B., & Kraft, M. (2024) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp63557.2024.10792986 · Cited by 1

Recognition Method of Tomato Ripeness Based on YOLOv7 and YOLOv8

Hung, L. Y., & Jalani, J. (2024) · International Conference on Robotics, Automation and Artificial Intelligence (RAAI) · doi:10.1109/raai64504.2024.10949513 · Cited by 1

Pre-harvest loss quantification in grain crops

Yamada, W., Francis, J., Jewison, J., Runge, T. M., & Digman, M. F. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110405 · Cited by 1

Accelerating Solar PV Site Selection: YOLO-Based Identification of Sound Barriers Along Highways

Tavares, J., & Silva, C. (2025) · Energies · doi:10.3390/en18092366 · Cited by 1

YOLO‐D: A high‐precision identification model for sugar beet and weeds at different growth stages

Liu, R., Tan, Y., Qin, J., & Xiang, X. (2025) · Agronomy Journal · doi:10.1002/agj2.70092 · Cited by 1

Scalable pipelines for instant object detection: deploying YOLO models with Apache Kafka for high-throughput inference

Öztel, İ., & Çeken, C. (2025) · Vestnik of M Kozybayev North Kazakhstan University · doi:10.54596/2958-0048-2025-2-194-206 · Cited by 1

An End-to-End Particle Gradation Detection Method for Earth–Rockfill Dams from Images Using an Enhanced YOLOv8-Seg Model

Tang, Y., Zhao, S., Qin, H., Ming, P., Fang, T., & Zeng, J. (2025) · Sensors · doi:10.3390/s25154797 · Cited by 1

An Approach for Enhancing Ripe Tomato Classification and Counting by Using Deep Learning and SAHI Image Processing

Khiem, N. M., Thanh, T. V., Hai, N. T., Bao, L. H. Q., & Thai-Nghe, N. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-98167-8_7 · Cited by 1

A Scale-Adaptive and Frequency-Aware Attention Network for Precise Detection of Strawberry Diseases

Zhang, K., Ye, Y., Chen, K., Li, Z., & Peng, H. (2025) · Agronomy · doi:10.3390/agronomy15081969 · Cited by 1

SimMolCC: A Similarity of Automatically Detected Bio-Molecule Clusters between Fluorescent Cells

Hattori, S., Miki, T., Sanjo, A., Kobayashi, D., & Takahara, M. (2024) · Applied Sciences · doi:10.3390/app14177958

MLENS: A Web Solution for Detection and Identification of Myxozoa using Computer Vision and Light MicroscopyPreprint

Carneiro, G., Xavier, K. C., Sindeaux-Neto, J. L., & SILVA, M. D. (2024) · doi:10.22541/au.172873288.84417088/v1

Enhancing safety during harvesting with a deep learning-based action determination method for picking robots

孙, 腾., 左, 好., Zhou, Z., Wang, Y., & Miao, Z. (2025) · International Conference on Optical and Photonic Engineering (icOPEN) · doi:10.1117/12.3058025

Development of Innovative Model for Waste Management System Using Internet of Things (IoT) and Machine Learning

Putera, D. A., Lawi, A., Saputra, F. O., Handayani, S. S., Fatimah, Y. A., Reza, I. M., Sholikun, S., Hasibuan, Z. A., & Aranski, A. W. (2024) · International Conference on Informatics and Computing (ICIC) · doi:10.1109/icic64337.2024.10957619

A novel method of Detection of Hot spots in Solar Panels using Deep learning-based Object Detection Algorithm

Rohan, R., Tanti, D. K., & Chandra, M. (2024) · International BIT Conference (BITCON) · doi:10.1109/bitcon63716.2024.10985634

Research on the Performance Improvement of YOLO Algorithm Based on C3 Module Optimization in Agricultural Harvesting

Mu, L., Wang, C., Wang, H., Shan, K. Y., Lian, Y., & Liu, X. (2025) · X-Disciplinarity · doi:10.63174/xdi.bwsn5805

YOLO-Blueberry: A Lightweight and Accurate Multi-Scale Detection Model for Blueberry Maturity RecognitionPreprint

Yu, B., Chan, Z., Павленко, П. М., & Zhang, X. (2025) · Preprints.org · doi:10.20944/preprints202505.1707.v1

Application of Fiber Membrane in Environment and Energy Field: A Review Based on Bibliometric Analysis

Liu, Z., Zhou, X., Shen, T., Li, M., Guo, S., Yu, M., Zhu, L., Xu, G., & Zhu, M. (2025) · ACS Applied Energy Materials · doi:10.1021/acsaem.5c00774

Development of Automated Guided Vehicles Using Software Engineering

Khalil, R. I., & Naktal Moaid, E. D. A. N. (2025) · doi:10.1109/csase63707.2025.11054012

Deep Learning Architecture for Tomato Plant Leaf Detection in Images Captured in Complex Outdoor Environments

Meraz-Hernández, A., Fuentes-Pacheco, J., Magadán-Salazar, A., Pinto-Elías, R., & González-Franco, N. (2025) · Mathematics · doi:10.3390/math13152338

HTMDet: A multi-scale adaptive feature-perception model for honey tangerine maturity detection under lime-covered conditions

Peng, Y., Yang, L., Yang, H., Wu, T. H., Li, X., & Zhang, J. (2025) · Food Control · doi:10.1016/j.foodcont.2025.111580

Wheat Seed Quality Detection For Enhanced Agriculture using YOLOv8 and IOT

Janapati, M., Kakarla, M., & Tirumalasetty, B. S. S. K. (2025) · International Conference on Recent Advances in Information Technology · doi:10.1109/rait65068.2025.11089009

CSCA-YOLOv8: A lightweight network model for evaluating drought resistance in mung bean

Jiang, D., Liu, J., Zhang, H., Liang, W., Luo, Z., An, W., Li, S., Chen, X., Yuan, X., & Gao, S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0326328 · Cited by 1

Autonomous Herbicide Sprayer Robot for Controlling Weeds in Sesame Field

Sonawane, S. R., Patil, N. N., & Mali, M. L. (2025) · Studies in big data · doi:10.1007/978-981-96-7492-3_3

Design, Integration, and Evaluation of a Dual-Arm Robotic System for High Throughput Sampling from Potato TubersPreprint

Divyanth, L. G., Sabir, S. U. B., Rathore, D. S., Khot, L. R., Mattupalli, C., & Karkee, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5397621

Maize Ear Object Detection Method Based on Improved YOLOv8 for Intelligent Breeding Unmanned Vehicle

Wang, H., & Chen, J. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-0848-8_29

Leaf Disease Detection Using YOLOv8

Kumari, L., & Majumder, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-5822-0_7

Growth stage detection for food consumption management in smart cricket farming using a deep learning technique

Nutnoi, N., Yindeesuk, W., Kamoldilok, S., Srinuanjan, K., & Limsuwan, P. (2025) · Artificial Life and Robotics · doi:10.1007/s10015-025-01064-8

MLens: Advancing the Real-Time Detection, Identification, and Counting of Pathogenic Microparasites Through a Web Interface

Carneiro, G. S., Xavier, K. C., Sindeaux-Neto, J. L., da Silva, A. D. S. L., & Velasco, M. (2025) · Parasitologia · doi:10.3390/parasitologia5040050

A Novel Application of Deep Learning–Based Estimation of Fish Abundance and Temporal Patterns in Agricultural Drainage Canals for Sustainable Ecosystem Monitoring

Maeda, S., & Akiba, T. (2025) · Sustainability · doi:10.3390/su17198578

Drone-Based Tomato Fruit Detection Through Hardware-Accelerated YOLO Deployment

Kafi, A. I., Sanfilippo, A. P., Jovanović, R., & Shannak, S. A.-H. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6438-2_35

A Survey on Deep Learning-Based Approaches for Tomato Ripeness Detection

Lestari, D., Aradea, A., Rianto, R., & Mardiati, R. (2025) · International Conference on Wireless and Telematics (ICWT) · doi:10.1109/icwt66752.2025.11181847

Advancing marine debris counting during extreme weather events: Deep learning applications in Typhoons Saola and Haikui

Zhang, B., Zhang, F., Hui, J., Peng, X., Zhang, J., & Zhang, Y. (2025) · Marine Environmental Research · doi:10.1016/j.marenvres.2025.107563

Comparison of circular and rectangular bounding boxes for apple detection in orchards

Maliszewski, P., & Sztyber, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-05802-7_2

АГРЕГАЦІЯ РЕЗУЛЬТАТІВ РОЗПІЗНАВАННЯ ПЕРЦЕПТРОННИХ СТРУКТУР ПЛАТФОРМИ YOLO V8N НА ОСНОВІ МЕТОДУ МЕДІАНИ

КОСМІРАК, В., ВОЛИНСЬКИЙ, О., КОСМІРАК, Р., & Topalov, A. (2025) · Herald of Khmelnytskyi National University Technical sciences · doi:10.31891/2307-5732-2025-355-36

LUNTIAN: Optimizing Crop Health Utilizing YOLOv8 Object Detection Algorithm for Plant Disease Detection

Santos, M. L. C. D., Tano, I. M., Bucaling, R. G., Escoto, C. B., Lucero, H. M., & Sistoso, A. P. (2025) · International Journal of Research and Innovation in Applied Science · doi:10.51584/ijrias.2025.101000009

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