# DP-MaizeTrack: a software for tracking the number of maize plants and leaves information from UAV image

## TL;DR

DP-MaizeTrack is a software that uses improved AI models to accurately track maize plants and leaves from drone images, helping improve agricultural management.

## Contribution

DP-MaizeTrack introduces an enhanced YOLOv8 model with modules for better detection accuracy and integrates tools for precision agriculture.

## Key findings

- DP-YOLOv8 outperforms baseline YOLOv8 with 3.9% higher precision, 4.1% higher recall, and 4.0% higher mAP50 in single-plant detection.
- DP-MaizeTrack provides accurate visualization for both plant and leaf detection tasks.
- The software includes tools for region segmentation and data statistics to support precision agriculture.

## Abstract

In modern agricultural production, accurate monitoring of maize growth and leaf counting is crucial for precision management and crop breeding optimization. Current UAV-based methods for detecting maize seedlings and leaves often face challenges in achieving high accuracy due to issues such as low spatial-resolution, complex field environments, variations in plant scale and orientation. To address these challenges, this study develops an integrated detection and visualization software, DP-MaizeTrack, which incorporates the DP-YOLOv8 model based on YOLOv8. The DP-YOLOv8 model integrates three key improvements. The Multi-Scale Feature Enhancement (MSFE) module improves detection accuracy across different scales. The Optimized Spatial Pyramid Pooling–Fast (OSPPF) module enhances feature extraction in diverse field conditions. Experimental results in single-plant detection show that the DP-YOLOv8 model outperforms the baseline YOLOv8 with improvements of 3.9% in Precision (95.1%), 4.1% in Recall (91.5%), and 4.0% in mAP50 (94.9%). The software also demonstrates good accuracy in the visualization results for single-plant and leaf detection tasks. Furthermore, DP-MaizeTrack not only automates the detection process but also integrates agricultural analysis tools, including region segmentation and data statistics, to support precision agricultural management and leaf-age analysis. The source code and models are available at https://github.com/clhclhc/project.