柑橘叶斑病严重程度检测的深度学习技术实现

Nutika, Rishabh Sharma, V. Kukreja, Prince Sood, Ankit Bansal
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引用次数: 0

摘要

利用计算机视觉方法对植物病害进行识别和分类的研究正在进行中。柑橘是植物科的一员,对病害非常敏感,但对柑橘病害检测的研究并不多。柑桔叶斑病(CLB)可以通过柑桔叶斑病检测和分类模型,根据疾病的严重程度进行检测和分类。为了对8000张柑橘叶片健康和clb感染图像进行分类,提出了一种基于卷积神经网络(CNN)的深度学习(DL)模型。二分类和多分类对CLB疾病的排序准确率分别为97.81%和98.81%。此外,还比较了尖端的预训练模型。,表明它在CLB疾病的多重分类方面优于它们。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of Deep Learning Technique for Citrus Leaf Blotch Disease Severity Detection
Utilising computer vision methods, there has been an ongoing study in recognizing and categorizing plant diseases. Citrus is a member of the plant family and is highly susceptible to disease, there hasn't been much research done on citrus disease detection. Citrus leaf blotch (CLB) disease can be detected and categorized based on how severe the illness is through a model for citrus leaf disease detection and classification. To categorize 8000 real-phase images of citrus leaves which include healthy and CLB-infected images, A deep learning (DL) model based on convolutional neural networks (CNN) has been presented.. This ranking accuracy of the CLB disease is 97.81% for binary classification and 98.81% for multi-classification, respectively. Additionally, cutting-edge pre-trained models have been compared., showing that it outperforms them in terms of multiple classifications of CLB sickness.
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