A Fruit Tree Disease Diagnosis Model Based on Stacking Ensemble Learning

文献类型： 外文期刊

第一作者： Li, Honglei

作者： Li, Honglei;Jin, Ying;Zhong, Jiliang;Zhao, Ruixue

作者机构：

期刊名称：COMPLEXITY （ 影响因子：2.833； 五年影响因子：2.8 ）

ISSN： 1076-2787

年卷期： 2021 年 2021 卷

页码：

收录情况： SCI

摘要： Fruit tree diseases have a great influence on agricultural production. Artificial intelligence technologies have been used to help fruit growers identify fruit tree diseases in a timely and accurate way. In this study, a dataset of 10,000 images of pear black spot, pear rust, apple mosaic, and apple rust was used to develop the diagnosis model. To achieve better performance, we developed three kinds of ensemble learning classifiers and two kinds of deep learning classifiers, validated and tested these five models, and found that the stacking ensemble learning classifier outperformed the other classifiers with the accuracy of 98.05% on the validation dataset and 97.34% on the test dataset, which hinted that, with the small- and middle-sized dataset, stacking ensemble learning classifiers may be used as cost-effective alternatives to deep learning models under performance and cost constraints.

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