基于机器视觉的玉米作物识别定位方法研究与应用

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY

Information Processing in Agriculture Pub Date : 2023-03-01 DOI:10.1016/j.inpa.2021.07.004

Bingrui Xu , Li Chai , Chunlong Zhang

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引用次数: 5

摘要

杂草生长在作物之间是不受欢迎的植物，对作物生长和产量有不利影响。因此，本研究探索了基于机器视觉的玉米识别与定位方法。采用超绿特征算法和最大类间方差法(OTSU)对玉米、杂草和土地进行分割;分割效果显著，可以满足以下形状特征提取要求。最后，通过形态学重构和像素投影直方图方法实现玉米的识别和定位。实验表明，当除草机器人以1.6 km/h的速度行驶时，识别准确率可达到94.1%。本研究所采用的技术对正常情况可及，识别效果良好;提高了机器人识别的精度和实时性要求，减少了计算时间。

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Research and application on corn crop identification and positioning method based on Machine vision

Weeds that grow among crops are undesirable plants and have adversely affected crop growth and yield. Therefore, the study explores corn identification and positioning methods based on machine vision. The ultra-green feature algorithm and maximum between-class variance method (OTSU) were used to segment maize corn, weeds, and land; the segmentation effect was significant and can meet the following shape feature extraction requirements. Finally, the identification and positioning of corn were achieved by morphological reconstruction and pixel projection histogram method. The experiment reveals that when a weeding robot travels at a speed of 1.6 km/h, the recognition accuracy can reach 94.1%. The technique used in this study is accessible for normal cases and can make a good recognition effect; the accuracy and real-time requirements of robot recognition are improved and reduced the calculation time.

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来源期刊

Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology

CiteScore

21.10

自引率

0.00%

发文量

期刊介绍： Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining