# Detecting Volunteer Cotton Plants in a Corn Field with Deep Learning on UAV Remote-Sensing Imagery

arXiv: https://arxiv.org/abs/2207.06673 · HTML: https://mlsanity.com/paper/2207.06673/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 54 references that are in the map.
- First posted on arXiv 14 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [An Analysis of Scale Invariance in Object Detection - SNIP](https://mlsanity.com/paper/1711.08189/) (2017)
- [Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving](https://mlsanity.com/paper/1904.04620/) (2019)
- [Uncertainty Estimation in One-Stage Object Detection](https://mlsanity.com/paper/1905.10296/) (2019)
- [Real-Time Apple Detection System Using Embedded Systems With Hardware Accelerators: An Edge AI Application](https://mlsanity.com/paper/2004.13410/) (2020)
- [Aerial Images Processing for Car Detection using Convolutional Neural Networks: Comparison between Faster R-CNN and YoloV3](https://mlsanity.com/paper/1910.07234/) (2019)

## Built on by (most cited first, 7 of 7)

- [SoybeanNet: Transformer-Based Convolutional Neural Network for Soybean Pod Counting from Unmanned Aerial Vehicle (UAV) Images](https://mlsanity.com/paper/2310.10861/) (2023)
- [Assessing The Performance of YOLOv5 Algorithm for Detecting Volunteer Cotton Plants in Corn Fields at Three Different Growth Stages](https://mlsanity.com/paper/2208.00519/) (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)
- [Plastic Contaminant Detection in Aerial Imagery of Cotton Fields with Deep Learning](https://mlsanity.com/paper/2212.07527/) (2022)
- [Selection of appropriate multispectral camera exposure settings and radiometric calibration methods for applications in phenotyping and precision agriculture](https://mlsanity.com/paper/2402.18553/) (2024)
- [Advancements in Weed Mapping: A Systematic Review](https://mlsanity.com/paper/2507.01269/) (2025)
- [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 cotton boll weevil, Anthonomus grandis Boheman is a serious pest to the U.S. cotton industry that has cost more than 16 billion USD in damages since it entered the United States from Mexico in the late 1800s. This pest has been nearly eradicated; however, southern part of Texas still faces this issue and is always prone to the pest reinfestation each year due to its sub-tropical climate where cotton plants can grow year-round. Volunteer cotton (VC) plants growing in the fields of inter-seasonal crops, like corn, can serve as hosts to these pests once they reach pin-head square stage (5-6 leaf stage) and therefore need to be detected, located, and destroyed or sprayed . In this paper, we present a study to detect VC plants in a corn field using YOLOv3 on three band aerial images collected by unmanned aircraft system (UAS). The two-fold objectives of this paper were : (i) to determine whether YOLOv3 can be used for VC detection in a corn field using RGB (red, green, and blue) aerial images collected by UAS and (ii) to investigate the behavior of YOLOv3 on images at three different scales (320 x 320, S1; 416 x 416, S2; and 512 x 512, S3 pixels) based on average precision (AP), mean average precision (mAP) and F1-score at 95% confidence level. No significant differences existed for mAP among the three scales, while a significant difference was found for AP between S1 and S3 (p = 0.04) and S2 and S3 (p = 0.02). A significant difference was also found for F1-score between S2 and S3 (p = 0.02). The lack of significant differences of mAP at all the three scales indicated that the trained YOLOv3 model can be used on a computer vision-based remotely piloted aerial application system (RPAAS) for VC detection and spray application in near real-time.