植物病害自动检测研究进展

IF 1.3 Q3 ENGINEERING, MULTIDISCIPLINARY
Radhika Bhagwat, Y. Dandawate
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引用次数: 5

摘要

植物病害造成重大的产量和经济损失。为了在早期发现植物病害,选择合适的技术是必要的,因为它影响到成本、诊断时间和准确性。本研究综述了基于图像和处理算法的各种植物病害检测方法。系统分析了用于处理可见光和光谱范围图像的各种传统机器学习和深度学习算法,并从使用的数据集、使用的各种图像处理技术、使用的模型和实现的效率等方面对文献所做的工作进行了比较评价。本研究讨论了每种方法的优点和局限性,以及快速准确地检测植物病害所面临的挑战。结果表明,对于植物病害检测,深度学习优于传统的机器学习算法,而可见光范围图像比光谱图像应用更广泛。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Review on Advances in Automated Plant Disease Detection
Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images.
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来源期刊
CiteScore
2.80
自引率
0.00%
发文量
18
审稿时长
12 weeks
期刊介绍: The IJETI journal focus on the field of engineering and technology Innovation. And it publishes original papers including but not limited to the following fields: Automation Engineering Civil Engineering Control Engineering Electric Engineering Electronic Engineering Green Technology Information Engineering Mechanical Engineering Material Engineering Mechatronics and Robotics Engineering Nanotechnology Optic Engineering Sport Science and Technology Innovation Management Other Engineering and Technology Related Topics.
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