超参数调整对植物叶病识别和分类的影响:深度学习方法

M. V. Shewale, R. Daruwala
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引用次数: 0

摘要

农业是对国家经济发展做出重大贡献的重要部门,截至 2020-2021 年占国内生产总值(GDP)的 20.19%。物联网、机器学习(ML)、深度学习(DL)和人工神经网络(ANN)等技术提供了最有效、最可行的解决方案。这有助于通过农业领域的自动化,以最少的人工干预实现不同领域的现代化。本文介绍了一种卷积神经网络框架,该框架使用 PlantVillage 数据集来分析受多种疾病影响的番茄植物。通过严格的实验和参数调整,观察了超参数对模型、性能的影响,并在实验中考虑了最适合的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Impact of Hyperparameter Tuning for Identification and Classification of Plant Leaf Diseases: A Deep Learning Approach
Agriculture is a prominent sector that contributes significantly to the country's economic development, accounting for 20.19% of gross domestic product (GDP) as of the year 2020–2021. Technologies like Internet of Things, Machine Learning (ML), Deep Learning (DL), and Artificial Neural Networks (ANN) provide the most effective and feasible solutions. This aids in making different domain modernization through automation in agricultural fields with minimal human intervention. This paper presents a convolutional neural network framework using the PlantVillage dataset for tomato plants affected by several diseases. With rigorous experimentation and parameter tuning the impact of hyperparameter on the model, performance is observed and the best fit model is considered for the experimentation.
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