Mingzheng Zhang

Mingzheng Zhang
China Agricultural University | CAU · College of Information and Electrical Engineering

About

6
Publications
2,832
Reads
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114
Citations
Citations since 2017
6 Research Items
114 Citations
2017201820192020202120222023010203040
2017201820192020202120222023010203040
2017201820192020202120222023010203040
2017201820192020202120222023010203040
Introduction

Publications

Publications (6)
Article
Full-text available
Background: The timely, rapid, and accurate near real-time observations are urgent to monitor the damage of corn armyworm, because the rapid expansion of armyworm would lead to severe yield losses. Therefore, the potential of machine learning algorithms for identifying the armyworm infected areas automatically and accurately by multispectral Unmann...
Preprint
Full-text available
Background: Monitoring armyworm (Mythimna separata Walker) damage in crops requires timely, rapid and accurate observations to avoid severe yield losses. Results: The Random Forest (RF) classifier was more effective at automatically and accurately monitoring armyworm damage compared with Support Vector Machine (SVM), Multilayer Perceptron Classifie...
Article
Sentinel-2 satellite is a new generation of multi-spectral remote sensing technique with high spatial, temporal and spectral resolution. Especially, Sentinel-2 incorporates three red-edge bands with central wavelength at 705, 740 and 783 nm, which are very sensitive to vegetation changing, heath and variations. Unfortunately, their spatial resoluti...
Article
Full-text available
Phenotyping provides important support for corn breeding. Unfortunately, the rapid detection of phenotypes has been the major limiting factor in estimating and predicting the outcomes of breeding programs. This study was focused on the potential of phenotyping to support corn breeding using unmanned aerial vehicle (UAV) images, aiming at mining and...
Article
Full-text available
Leaf angle is a critical structural parameter for retrieving canopy leaf area index (LAI) using the PROSAIL model. However, the traditional method using default leaf angle distribution in the PROSAIL model does not capture the phenological dynamics of canopy growth. This study presents a LAI retrieval method for corn canopies using PROSAIL model wi...
Article
Terrestrial LiDAR data can be used to extract accurate structure parameters of corn plant and canopy, such as leaf area, leaf distribution, and 3D model. The first step of these applications is to extract corn leaf points from unorganized LiDAR point clouds. This paper focused on an automated extraction algorithm for identifying the points returnin...

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