基于深度学习框架的植物病害分类

Ayushi Verma, Shashi Shekhar, Hitendra Garg
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引用次数: 1

摘要

农业是印度的主要生计来源。在农业领域发生的主要破坏是由于植物病害。疾病对农田造成严重破坏。需要开发一种自动系统来预防作物在初期阶段的疾病。该自动化系统将对植物病害进行诊断和分类。提出了一种能够识别植物病害种类和健康状况的病害自动检测系统。病害的分类是根据叶片表面出现的症状进行的。本文将这些疾病大致分为真菌、细菌和病毒三大类。使用深度学习方法即卷积神经网络对疾病进行分类。该数据集由64963个样本组成,其中80%的样本进行了训练,20%的样本进行了验证。仿真结果表明,在给定数据集上训练后的卷积神经网络准确率达到99.12%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Plant Disease Classification Using Deep Learning Framework
This Agriculture is the main source of livelihood in India. Major destruction occurs in the field of agriculture is due to the diseases in plants. Disease causes heavy devastation in the field of farming. An automatic system needs to be developed for the prevention of crops at initial stages from diseases. The automatic system will diagnose plant disease and identify its category. The paper proposed an automatic disease detection system which recognize the category of the diseases and healthiness in plants. The classification of diseases have done on the basis of symptoms appeared on the leaf surface. The paper broadly categorises the diseases into three categories such as fungal, bacterial and viral. The deep learning approach namely Convolutional Neural Network is used for the classification of the diseases. The dataset consists of 64963 number of samples in which training has performed on 80% of the samples of dataset and validation has performed on 20% of the samples of dataset. The simulation results shows that the convolutional neural network that have been trained on given dataset acquired an accuracy of 99.12%.
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