基于深度学习的植物病害检测算法综述

Manivarsh Adi, Abhishek Singh, Harinath Reddy A, Y. Kumar, Venkata Reddy Challa, Pooja Rana, Usha Mittal
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引用次数: 4

摘要

植物病害检测是当今农民关注的主要问题之一。由于深度学习等许多新技术能够深入分析和计算,使其成为植物叶片病害检测的突出技术之一。内置深度学习模型的移动应用程序正在帮助世界各地的农民检测和分类这种疾病。它由神经网络和CNN等不同的技术组成,用于诊断植物叶片中的疾病。它使用图像的关键特征来检测和诊断叶子中存在的疾病类型。一些预训练的模型,如AlexNet、GoogleNet、LeNet、ResNet、VGGNET和Inception,具有大量可学习的参数,已经显示出对叶片疾病的分类或检测。本文重点介绍了用于植物叶片病害检测的不同架构,如预定义模型和用户自定义模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Overview on Plant Disease Detection Algorithm Using Deep Learning
Disease detection in plants is one of the major concerns for farmers nowadays. As many new techniques like Deep Learning capability to dive into deep analysis and computation made it one of the prominent techniques for plant leaf disease detection. Mobile applications with inbuilt deep learning models are helping farmers to detect and classify the disease throughout the world. It consists of disparate techniques like ANN and CNN to diagnose the disease in plant leaves. It uses key features of images to detect and diagnose the type of diseases present in leaves. Some pre-trained models like AlexNet, GoogleNet, LeNet, ResNet, VGGNET and Inception with a huge number of learnable parameters had shown classification or detection of disease in leaves. This paper focused on different architecture like predefined and user defined models that were used for detection of diseases in plant leaves.
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