Maize plant detection using UAV-based RGB imaging and YOLOv5

Abstract

In recent years, computer vision (CV) has made enormous progress and is providing great possibilities in analyzing images for object detection, especially with the application of machine learning (ML). Unmanned Aerial Vehicle (UAV) based high-resolution images allow to apply CV and ML methods for the detection of plants or their organs of interest. Thus, this study presents a practical workflow based on the You Only Look Once version 5 (YOLOv5) and UAV images to detect maize plants for counting their numbers in contrasting development stages, including the application of a semi-auto-labeling method based on the Segment Anything Model (SAM) to reduce the burden of labeling. Results showed that the trained model achieved a mean average precision (mAP@0.5) of 0.828 and 0.863 for the 3-leaf stage and 7-leaf stage, respectively. YOLOv5 achieved the best performance under the conditions of overgrown weeds, leaf occlusion, and blurry images, suggesting that YOLOv5 plays a practical role in obtaining excellent performance under realistic field conditions. Furthermore, introducing image-rotation augmentation and low noise weight enhanced model accuracy, with an increase of 0.024 and 0.016 mAP@0.5, respectively, compared to the original model of the 3-leaf stage. This work provides a practical reference for applying lightweight ML and deep learning methods to UAV images for automated object detection and characterization of plant growth under realistic environments.

Chenghao Lu

Research assistant & Joint Phd student

My research interests are Agricultural Digital Twin and Smart Breeding. My project focuses on the construction of a Digital Twin system for maize, aiming to fuse Genetic x Environmental x Management data to construct a high-precision and dynamically updated maize growth virtual twin. By introducing a DNA language model with a multimodal fusion approach, I hope to achieve accurate simulation of maize phenotypes and breeding to support sustainable agricultural development.

PD. Dr. Yuncai Hu

Senior Researcher

My research interests include Remote sensing, Precision N nutrient management, Plant phenotyping for complex traits of abiotic stress tolerance, Agricultural N emissions, and Physiological mechanisms of plant responses to abiotic stresses.

Prof. Dr. Kang Yu

Professor of Precision Agriculture

My research interests include precision crop farming, hyperspectral remote sensing, and AI in agriculture.