Remora-CNN:一种新颖有效的水稻叶病检测和分类方法

IF 1.1 4区 农林科学 Q3 PLANT SCIENCES
Devchand J. Chaudhari, Malathi Karunakaran
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引用次数: 0

摘要

对于全世界数百万人来说,水稻是主要的粮食作物之一。然而,水稻在生长过程中容易受到多种病害的侵袭。大多数水稻植物病害都受到生物和非生物因素的影响,包括线虫、病毒、真菌、病毒、细菌和其他微生物,以及温度和其他环境因素。因此,有必要对叶片病害进行自动早期分类,以提高水稻产量。本文使用卷积神经网络(CNN)模型对水稻叶病进行识别和分类,并使用 Remora 优化算法(ROA)对 CNN 进行训练。通过使用 K-means 和分数切向-非球面核(FTSK)算法执行分割过程,可以获得更好的分类结果。此外,所开发的 Remora 优化-卷积神经网络(Remora-CNN)方法在水稻叶病图像样本数据集的测试准确性、灵敏度和特异性方面分别达到了 0.925、0.931 和 0.941 的最佳性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Remora-CNN: A Novel and Effective Method for Rice Leaf Disease Detection and Classification

For millions of people worldwide, rice is one of the main food crops. Nevertheless, while being grown, rice is susceptible to many diseases. Most rice plant diseases are influenced by biotic and abiotic factors, including nematodes, viroids, fungus, viruses, bacteria, and other microorganisms, as well as temperature and other environmental factors. Thus, an automatic early classification of leaf disease is necessary to improve the rice yield. In this paper, for identifying and categorizing the rice leaf disease, a convolutional neural network (CNN) model is used, and the CNN is trained using the Remora Optimization Algorithm (ROA). A better classification outcome is attained by performing the segmentation process using K-means with the Fractional Tangential-Spherical Kernel (FTSK) algorithm. Furthermore, the developed Remora Optimization- Convolutional Neural Network (Remora-CNN) method achieved the optimal performance based on the testing accuracy, sensitivity and specificity of 0.925, 0.931, and 0.941 using the Rice Leaf Disease Image Samples Dataset.

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来源期刊
Journal of Phytopathology
Journal of Phytopathology 生物-植物科学
CiteScore
2.90
自引率
0.00%
发文量
88
审稿时长
4-8 weeks
期刊介绍: Journal of Phytopathology publishes original and review articles on all scientific aspects of applied phytopathology in agricultural and horticultural crops. Preference is given to contributions improving our understanding of the biotic and abiotic determinants of plant diseases, including epidemics and damage potential, as a basis for innovative disease management, modelling and forecasting. This includes practical aspects and the development of methods for disease diagnosis as well as infection bioassays. Studies at the population, organism, physiological, biochemical and molecular genetic level are welcome. The journal scope comprises the pathology and epidemiology of plant diseases caused by microbial pathogens, viruses and nematodes. Accepted papers should advance our conceptual knowledge of plant diseases, rather than presenting descriptive or screening data unrelated to phytopathological mechanisms or functions. Results from unrepeated experimental conditions or data with no or inappropriate statistical processing will not be considered. Authors are encouraged to look at past issues to ensure adherence to the standards of the journal.
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