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植物病害检测与分类：系统文献综述。

Plant Disease Detection and Classification: A Systematic Literature Review.

机构信息

Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India.

School of Engineering and Technology, CT University, Ludhiana 142024, Punjab, India.

出版信息

Sensors (Basel). 2023 May 15;23(10):4769. doi: 10.3390/s23104769.

DOI:10.3390/s23104769
PMID:37430683
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10223612/
Abstract

A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy.

摘要

印度的绝大多数人口以农业为生。由于天气模式的变化和致病生物体引起的不同疾病会影响各种植物物种的产量。本文从数据源、预处理技术、特征提取技术、数据增强技术、用于检测和分类影响植物的疾病的模型、如何提高图像质量、如何减少模型的过拟合以及准确性等方面分析了一些现有的技术。本研究的研究论文是使用来自各种数据库的同行评审出版物中的各种关键字选择的，这些出版物发表于 2010 年至 2022 年之间。总共确定了 182 篇论文，并根据标题、摘要、结论和全文对其与植物病害检测和分类的直接相关性进行了审查，其中 75 篇论文在基于标题、摘要、结论和全文排除后被选中进行了审查。研究人员将发现这项工作通过数据驱动的方法识别各种现有技术的潜力，并通过提高系统性能和准确性来识别植物病害，这是一项有用的资源。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09fb/10223612/41daa83711df/sensors-23-04769-g016.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09fb/10223612/7f0e847fb45a/sensors-23-04769-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09fb/10223612/9f8c0f04d74b/sensors-23-04769-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09fb/10223612/105a4a31dcf9/sensors-23-04769-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09fb/10223612/46b6fe0fe8fd/sensors-23-04769-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09fb/10223612/2c3ca7cb11c2/sensors-23-04769-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09fb/10223612/230c1c19ff9d/sensors-23-04769-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09fb/10223612/fdad32f2b120/sensors-23-04769-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09fb/10223612/863e97722cfb/sensors-23-04769-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09fb/10223612/e12700f5eb63/sensors-23-04769-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09fb/10223612/d464814a6daa/sensors-23-04769-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09fb/10223612/d36a9807919e/sensors-23-04769-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09fb/10223612/b294636e053b/sensors-23-04769-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09fb/10223612/634ad2470d2d/sensors-23-04769-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09fb/10223612/de11e99af57b/sensors-23-04769-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09fb/10223612/41daa83711df/sensors-23-04769-g016.jpg

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