Next Article in Journal
Target Site Resistance to Acetolactate Synthase Inhibitors in Diplotaxis erucoides and Erucaria hispanica–Mechanism of Resistance and Response to Alternative Herbicides
Next Article in Special Issue
Autonomous Mowers Working in Narrow Spaces: A Possible Future Application in Agriculture?
Previous Article in Journal
Digital Count of Corn Plants Using Images Taken by Unmanned Aerial Vehicles and Cross Correlation of Templates
Previous Article in Special Issue
Determining Irrigation Depths for Soybean Using a Simulation Model of Water Flow and Plant Growth and Weather Forecasts
Open AccessArticle

Real-Time Localization Approach for Maize Cores at Seedling Stage Based on Machine Vision

by Ze Zong 1,2, Gang Liu 1,2,* and Shuo Zhao 1,2
Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agriculture University, Beijing 100083, China
Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, and Rural Affairs, China Agriculture University, Beijing 100083, China
Author to whom correspondence should be addressed.
Agronomy 2020, 10(4), 470;
Received: 25 February 2020 / Revised: 24 March 2020 / Accepted: 27 March 2020 / Published: 28 March 2020
(This article belongs to the Special Issue Precision Agriculture for Sustainability)
To realize quick localization of plant maize, a new real-time localization approach is proposed for maize cores at the seedling stage, which can meet the basic demands for localization and quantitative fertilization in precision agriculture and reduce environmental pollution and the use of chemical fertilizers. In the first stage, by taking pictures of maize at the seedling stage in a field with a monocular camera, the maize is segmented from the weed background of the picture. And then the three most-effective methods (i.e., minimum cross entropy, ISODATA, and the Otsu algorithm) are found from six common segmentation algorithms after comparing the accuracy rate of extracting maize and the time efficiency of segmentation. In the second stage, plant core from segmented maize image is recognized, and localized, based on different brightness of the rest part of maize core and plant. Then the geometric center of maize core is considered as localization point. the best effect of extracting maize core was found from the minimum cross entropy method based on gray level. According to experimental validation using many field pictures, under weedy conditions on sunny days, the proposed method has a minimum recognition rate of 88.37% for maize cores and is more robust at excluding weeds. View Full-Text
Keywords: minimum cross entropy; maize core; real-time localization minimum cross entropy; maize core; real-time localization
Show Figures

Figure 1

MDPI and ACS Style

Zong, Z.; Liu, G.; Zhao, S. Real-Time Localization Approach for Maize Cores at Seedling Stage Based on Machine Vision. Agronomy 2020, 10, 470.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop