# Assessing The Performance of YOLOv5 Algorithm for Detecting Volunteer Cotton Plants in Corn Fields at Three Different Growth Stages

arXiv: https://arxiv.org/abs/2208.00519 · HTML: https://mlsanity.com/paper/2208.00519/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 31 Jul 2022.

## Builds on (most cited first, 11 of 11)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Augmentor: An Image Augmentation Library for Machine Learning](https://mlsanity.com/paper/1708.04680/) (2017)
- [A Robust Illumination-Invariant Camera System for Agricultural Applications](https://mlsanity.com/paper/2101.02190/) (2021)
- [Precision-Recall Curve (PRC) Classification Trees](https://mlsanity.com/paper/2011.07640/) (2020)
- [Detecting Volunteer Cotton Plants in a Corn Field with Deep Learning on UAV Remote-Sensing Imagery](https://mlsanity.com/paper/2207.06673/) (2022)
- [Computer Vision for Volunteer Cotton Detection in a Corn Field with UAS Remote Sensing Imagery and Spot Spray Applications](https://mlsanity.com/paper/2207.07334/) (2022)

## Built on by (most cited first, 3 of 3)

- [Knowledge Distillation in YOLOX-ViT for Side-Scan Sonar Object Detection](https://mlsanity.com/paper/2403.09313/) (2024)
- [Plastic Contaminant Detection in Aerial Imagery of Cotton Fields with Deep Learning](https://mlsanity.com/paper/2212.07527/) (2022)
- [Robotic System with AI for Real Time Weed Detection, Canopy Aware Spraying, and Droplet Pattern Evaluation](https://mlsanity.com/paper/2507.05432/) (2025)

## Abstract (arXiv)

The boll weevil (Anthonomus grandis L.) is a serious pest that primarily feeds on cotton plants. In places like Lower Rio Grande Valley of Texas, due to sub-tropical climatic conditions, cotton plants can grow year-round and therefore the left-over seeds from the previous season during harvest can continue to grow in the middle of rotation crops like corn (Zea mays L.) and sorghum (Sorghum bicolor L.). These feral or volunteer cotton (VC) plants when reach the pinhead squaring phase (5-6 leaf stage) can act as hosts for the boll weevil pest. The Texas Boll Weevil Eradication Program (TBWEP) employs people to locate and eliminate VC plants growing by the side of roads or fields with rotation crops but the ones growing in the middle of fields remain undetected. In this paper, we demonstrate the application of computer vision (CV) algorithm based on You Only Look Once version 5 (YOLOv5) for detecting VC plants growing in the middle of corn fields at three different growth stages (V3, V6, and VT) using unmanned aircraft systems (UAS) remote sensing imagery. All the four variants of YOLOv5 (s, m, l, and x) were used and their performances were compared based on classification accuracy, mean average precision (mAP), and F1-score. It was found that YOLOv5s could detect VC plants with a maximum classification accuracy of 98% and mAP of 96.3 % at the V6 stage of corn while YOLOv5s and YOLOv5m resulted in the lowest classification accuracy of 85% and YOLOv5m and YOLOv5l had the least mAP of 86.5% at the VT stage on images of size 416 x 416 pixels. The developed CV algorithm has the potential to effectively detect and locate VC plants growing in the middle of corn fields as well as expedite the management aspects of TBWEP.