Roselle Pest Detection and Classification Using Threshold and Template Matching

Q3 Computer Science

中国图象图形学报 Pub Date : 2023-12-01 DOI:10.18178/joig.11.4.330-342

Ade Bastian, Adie Iman Nurzaman, Tri Ferga Prasetyo, Sri Fatimah

引用次数: 0

Abstract

Roselle is a fiber-producing plant that has broad benefits for health food, so many farmers are interested in starting to cultivate it. This study aims to design a rosella plant pest detection system to reduce the risk of crop failure or reduced yields of rosella calyx. The design of a system for detecting and classifying rosella pests uses the threshold method as a digital image processing method connected via the internet with information media applications and template matching to detect and classify pests on rosella plants. Detection of pests on rosella plants has been successfully built using a detection system using thresholding and template matching methods. Datasets of rosella plant pests that are not yet widely available encourage the detection of rosella plant pests with datasets from rosella plant objects and limited data testing. Testing with 75% accuracy, the detection process is affected by light and camera quality.

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利用阈值和模板匹配进行洛泽尔害虫检测和分类

玫瑰是一种纤维生产植物，对健康食品有广泛的好处，所以许多农民都有兴趣开始种植它。本研究旨在设计一套玫瑰花萼病虫害检测系统，以降低玫瑰花萼歉收或减产的风险。采用阈值法作为数字图像处理方法，通过互联网与信息媒体应用和模板匹配相连接，设计了一套玫瑰属植物害虫检测与分类系统。应用阈值法和模板匹配法成功建立了玫瑰属植物害虫检测系统。尚未广泛获得的玫瑰植物有害生物数据集鼓励使用来自玫瑰植物目标的数据集和有限的数据测试来检测玫瑰植物有害生物。测试精度为75%，检测过程受光线和相机质量的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

中国图象图形学报 Computer Science-Computer Graphics and Computer-Aided Design

CiteScore

1.20

自引率

0.00%

发文量

6776

期刊介绍： Journal of Image and Graphics (ISSN 1006-8961, CN 11-3758/TB, CODEN ZTTXFZ) is an authoritative academic journal supervised by the Chinese Academy of Sciences and co-sponsored by the Institute of Space and Astronautical Information Innovation of the Chinese Academy of Sciences (ISIAS), the Chinese Society of Image and Graphics (CSIG), and the Beijing Institute of Applied Physics and Computational Mathematics (BIAPM). The journal integrates high-tech theories, technical methods and industrialisation of applied research results in computer image graphics, and mainly publishes innovative and high-level scientific research papers on basic and applied research in image graphics science and its closely related fields. The form of papers includes reviews, technical reports, project progress, academic news, new technology reviews, new product introduction and industrialisation research. The content covers a wide range of fields such as image analysis and recognition, image understanding and computer vision, computer graphics, virtual reality and augmented reality, system simulation, animation, etc., and theme columns are opened according to the research hotspots and cutting-edge topics. Journal of Image and Graphics reaches a wide range of readers, including scientific and technical personnel, enterprise supervisors, and postgraduates and college students of colleges and universities engaged in the fields of national defence, military, aviation, aerospace, communications, electronics, automotive, agriculture, meteorology, environmental protection, remote sensing, mapping, oil field, construction, transportation, finance, telecommunications, education, medical care, film and television, and art. Journal of Image and Graphics is included in many important domestic and international scientific literature database systems, including EBSCO database in the United States, JST database in Japan, Scopus database in the Netherlands, China Science and Technology Thesis Statistics and Analysis (Annual Research Report), China Science Citation Database (CSCD), China Academic Journal Network Publishing Database (CAJD), and China Academic Journal Network Publishing Database (CAJD). China Science Citation Database (CSCD), China Academic Journals Network Publishing Database (CAJD), China Academic Journal Abstracts, Chinese Science Abstracts (Series A), China Electronic Science Abstracts, Chinese Core Journals Abstracts, Chinese Academic Journals on CD-ROM, and China Academic Journals Comprehensive Evaluation Database.