利用卷积神经网络识别榴莲叶片病害

Jay Al Gallenero, J. Villaverde
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引用次数: 0

摘要

榴莲叶病是东南亚许多地区严重的农业问题,特别是在菲律宾。传统的检测方法是通过人工人眼监测和实验室检测,这是控制这些疾病发展的关键和耗时的方法。当它存在时,榴莲树不会结出果实。因此,本研究表明,嵌入Duri Premium应用程序的便携式设备能够识别榴莲叶片疾病,并使用卷积神经网络MobileNet提供治疗,这是一个预先训练的模型,足以进行视觉处理,并将大大有助于减少与诸如藻斑,叶枯病,叶斑,健康叶和未知等疾病相关的经济损失。共分析了75个样本,并使用混淆矩阵计算系统的准确率,为93.333%。因此,该技术有效地识别了榴莲叶子上的上述疾病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of Durian Leaf Disease Using Convolutional Neural Network
Durian leaf disease is a severe agricultural issue in many parts of Southeast Asia, especially in the Philippines. The conventional way of detecting these diseases was through manual human eye monitoring and laboratory testing, which are crucial and time-consuming in controlling the development of these diseases. When it is present, the durian tree will not produce crops. Hence, this study has shown that a portable device embedded with the Duri Premium application was able to identify the durian leaf disease and provide treatment using Convolutional Neural Network MobileNet, a pre-trained model which suffices visual processing and would significantly help reduce the economic losses associated with the diseases such as algal spot, leaf blight, leaf spot, healthy leaf, and unknown. A total of seventy-five (75) samples were analyzed and used a confusion matrix to calculate the system's accuracy, which is 93.333%. As a result, the technique efficiently identifies the diseases mentioned above on durian leaves.
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