利用 CNN 识别水稻叶片病害

Chinthalapati Meghana
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引用次数: 0

摘要

摘要:水稻叶片上细菌、病毒和真菌病害的发生严重影响了水稻生产,对满足全球对主要作物的需求构成了挑战。虽然水稻叶片病害的检测至关重要,但现有方法受到图像背景和拍摄条件的限制。卷积神经网络(CNN)模型已成为识别水稻叶片病害的有效途径,但目前的方法在应用于独立数据集时识别率下降,并且受到需要大规模网络参数的限制。在本项目中,我们提出了一种基于 CNN 的创新模型,旨在通过减少网络参数来缓解这些挑战。通过训练多个基于 CNN 的模型来识别三种常见的水稻叶病,我们的研究旨在展示我们的方法与最先进的基于 CNN 的水稻叶病识别模型相比的有效性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rice Leaf Disease Recognition using CNN
Abstract: The occurrence of bacterial, viral, and fungal diseases on rice leaves significantly hampers rice production, posing a challenge to meet global demand for the staple crop. While the detection of rice leaf diseases is crucial, existing methods are constrained by limitations in image backgrounds and capture conditions. Convolutional Neural Network (CNN) models have emerged as a usefull avenue for disease recognition in rice leaves, yet current approaches suffer from decreased recognition rates when applied to independent datasets and are constrained by the need for large-scale network parameters. In this project, we propose an innovative CNN-based model aimed at mitigating these challenges by reducing network parameters. Through training multiple CNN- basedmodels to identify three common rice leaf diseases, our study aims to showcase the effectiveness and superiority of our approach compared to state-of-the-art CNN-based models for rice leaf disease recognition.
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