# YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain arXiv: https://arxiv.org/abs/2406.10139 ยท HTML: https://mlsanity.com/paper/2406.10139/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 24 of its 115 references that are in the map. - First posted on arXiv 14 Jun 2024. ## Builds on (most cited first, 24 of 24) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014) - [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015) - [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016) - [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016) - [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015) - [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015) - [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015) - [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020) - [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016) - [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022) - [RepVGG: Making VGG-style ConvNets Great Again](https://mlsanity.com/paper/2101.03697/) (2021) - [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024) - [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024) - [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018) - [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019) - [Oriented R-CNN for Object Detection](https://mlsanity.com/paper/2108.05699/) (2021) - [DAMO-YOLO : A Report on Real-Time Object Detection Design](https://mlsanity.com/paper/2211.15444/) (2022) - [Fast and Accurate Recurrent Neural Network Acoustic Models for Speech Recognition](https://mlsanity.com/paper/1507.06947/) (2015) - [Cross-Iteration Batch Normalization](https://mlsanity.com/paper/2002.05712/) (2020) - [YOLOv9 for Fracture Detection in Pediatric Wrist Trauma X-ray Images](https://mlsanity.com/paper/2403.11249/) (2024) - [Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1809.03193/) (2018) - [PAN: Path Integral Based Convolution for Deep Graph Neural Networks](https://mlsanity.com/paper/1904.10996/) (2019) - [Structured Model Pruning of Convolutional Networks on Tensor Processing Units](https://mlsanity.com/paper/2107.04191/) (2021) ## Built on by (most cited first, 14 of 14) - [YOLOv8 to YOLO11: A Comprehensive Architecture In-depth Comparative Review](https://mlsanity.com/paper/2501.13400/) (2025) - [YOLOv5, YOLOv8 and YOLOv10: The Go-To Detectors for Real-time Vision](https://mlsanity.com/paper/2407.02988/) (2024) - [YOLOv11 for Vehicle Detection: Advancements, Performance, and Applications in Intelligent Transportation Systems](https://mlsanity.com/paper/2410.22898/) (2024) - [A Comparative Analysis of YOLOv5, YOLOv8, and YOLOv10 in Kitchen Safety](https://mlsanity.com/paper/2407.20872/) (2024) - [Comparing YOLOv5 Variants for Vehicle Detection: A Performance Analysis](https://mlsanity.com/paper/2408.12550/) (2024) - [Enhancing Construction Site Safety: A Lightweight Convolutional Network for Effective Helmet Detection](https://mlsanity.com/paper/2409.12669/) (2024) - [What is YOLOv6? A Deep Insight into the Object Detection Model](https://mlsanity.com/paper/2412.13006/) (2024) - [YOLO Object Detectors for Robotics -- a Comparative Study](https://mlsanity.com/paper/2603.27029/) (2026) - [Surveying You Only Look Once (YOLO) Multispectral Object Detection Advancements, Applications And Challenges](https://mlsanity.com/paper/2409.12977/) (2024) - [Morphological Detection and Classification of Microplastics and Nanoplastics Emerged from Consumer Products by Deep Learning](https://mlsanity.com/paper/2409.13688/) (2024) - [Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study](https://mlsanity.com/paper/2505.11424/) (2025) - [A Low-Cost Machine Learning Approach for Timber Diameter Estimation](https://mlsanity.com/paper/2507.17219/) (2025) - [Real-Time Fish Detection in Indonesian Marine Ecosystems Using Lightweight YOLOv10-nano Architecture](https://mlsanity.com/paper/2509.17406/) (2025) - [YOLOBirDrone: Dataset for Bird vs Drone Detection and Classification and a YOLO based enhanced learning architecture](https://mlsanity.com/paper/2601.08319/) (2026) ## Abstract (arXiv) This survey investigates the transformative potential of various YOLO variants, from YOLOv1 to the state-of-the-art YOLOv10, in the context of agricultural advancements. The primary objective is to elucidate how these cutting-edge object detection models can re-energise and optimize diverse aspects of agriculture, ranging from crop monitoring to livestock management. It aims to achieve key objectives, including the identification of contemporary challenges in agriculture, a detailed assessment of YOLO's incremental advancements, and an exploration of its specific applications in agriculture. This is one of the first surveys to include the latest YOLOv10, offering a fresh perspective on its implications for precision farming and sustainable agricultural practices in the era of Artificial Intelligence and automation. Further, the survey undertakes a critical analysis of YOLO's performance, synthesizes existing research, and projects future trends. By scrutinizing the unique capabilities packed in YOLO variants and their real-world applications, this survey provides valuable insights into the evolving relationship between YOLO variants and agriculture. The findings contribute towards a nuanced understanding of the potential for precision farming and sustainable agricultural practices, marking a significant step forward in the integration of advanced object detection technologies within the agricultural sector.