作物/杂草分类模型综述

IF 0.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Web Intelligence Pub Date : 2023-07-18 DOI:10.3233/web-220115
Bikramaditya Panda, M. Mishra, B. P. Mishra, A. K. Tiwari
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引用次数: 0

摘要

作物和杂草的识别仍然是无人杂草控制的挑战。由于刈割时间和重要作物位置之间的范围很小,因此针对一年生作物的杂草鉴定必须非常准确。这项研究包括文献评估,其中包括IEEE、Science Direct和Springer期刊上最重要的50篇研究论文。从2012年到2022年,所有这些论文都被收集。实际上,诊断步骤包括:预处理、特征提取和作物/杂草分类。本研究从评价的数据集、预处理的不同策略、特征提取、分类等几个方面对这50篇研究论文进行了分析,得到了一个清晰的图景。此外,还展示了每个工作在准确性,灵敏度和精度方面的高性能。此外,还描述了目前在作物和杂草识别方面的障碍,为未来的研究人员提供了一个基准。
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An extensive review on crop/weed classification models
Crop and weed identification remains a challenge for unmanned weed control. Due to the small range between the chopping tine and the important crop location, weed identification against the annual crops must be extremely exact. This study endeavor included a literature evaluation, which included the most important 50 research publications in IEEE, Science Direct, and Springer journals. From 2012 until 2022, all of these papers are gathered. In fact, the diagnosis steps include: preprocessing, feature extraction, and crop/weed classification. This research analyzes the 50 research articles in several aspects, such as the dataset used for evaluations, different strategies used for pre-processing, feature extraction, and classification to get a clear picture of them. Furthermore, each work’s high performance in accuracy, sensitivity, and precision is demonstrated. Furthermore, the present hurdles in crop and weed identification are described, which serve as a benchmark for upcoming researchers.
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来源期刊
Web Intelligence
Web Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
0.90
自引率
0.00%
发文量
35
期刊介绍: Web Intelligence (WI) is an official journal of the Web Intelligence Consortium (WIC), an international organization dedicated to promoting collaborative scientific research and industrial development in the era of Web intelligence. WI seeks to collaborate with major societies and international conferences in the field. WI is a peer-reviewed journal, which publishes four issues a year, in both online and print form. WI aims to achieve a multi-disciplinary balance between research advances in theories and methods usually associated with Collective Intelligence, Data Science, Human-Centric Computing, Knowledge Management, and Network Science. It is committed to publishing research that both deepen the understanding of computational, logical, cognitive, physical, and social foundations of the future Web, and enable the development and application of technologies based on Web intelligence. The journal features high-quality, original research papers (including state-of-the-art reviews), brief papers, and letters in all theoretical and technology areas that make up the field of WI. The papers should clearly focus on some of the following areas of interest: a. Collective Intelligence[...] b. Data Science[...] c. Human-Centric Computing[...] d. Knowledge Management[...] e. Network Science[...]
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