An Efficient Hybrid CNN Classification Model for Tomato Crop Disease
An Efficient Hybrid CNN Classification Model for Tomato Crop Disease
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DOI：
10.3390/technologies11010010
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发表时间：
2023-02-01
期刊：
影响因子：
3.6
通讯作者：
Dasygenis, Minas
中科院分区：
文献类型：
--
作者：
Sanida, Maria Vasiliki;Sanida, Theodora;Dasygenis, Minas
番茄植株容易受到多种疾病的侵袭，每一种疾病都有可能造成重大损害。影响农作物的疾病对农产品的数量和质量产生了很大的负面影响。关于优质作物维护，及时和准确的诊断的重要性怎么强调都不为过。深度学习策略目前是作物病害诊断的一个重要研究领域。一个独立的系统可以根据植物的外部表现来诊断植物疾病，这是一个可以解决这些问题的智能农业解决方案的例子。这项工作提出了一种稳健的混合卷积神经网络(CNN)诊断工具，用于可能影响番茄叶片组织的各种疾病。CNN和INSITION模块是组成这一混合技术的两个组件。本研究使用的数据集包括9个不同的番茄病害类别和来自PlantVillage的一个健康类别。在测试集上的结果是有希望的，分别为99.17%的准确率、99.23%的召回率、99.13%的准确率、99.56%的AUC和99.17%的F1分数。所提出的方法为实际农业环境中的番茄作物诊断提供了一种具有高性能的解决方案。
Tomato plants are vulnerable to a broad number of diseases, each of which has the potential to cause significant damage. Diseases that affect crops substantially negatively impact the quantity and quality of agricultural products. Regarding quality crop maintenance, the importance of a timely and accurate diagnosis cannot be overstated. Deep learning (DL) strategies are now a critical research field for crop disease diagnoses. One independent system that can diagnose plant illnesses based on their outward manifestations is an example of an intelligent agriculture solution that could address these problems. This work proposes a robust hybrid convolutional neural network (CNN) diagnostic tool for various disorders that may affect tomato leaf tissue. A CNN and an inception module are the two components that make up this hybrid technique. The dataset employed for this study consists of nine distinct categories of tomato diseases and one healthy category sourced from PlantVillage. The findings are promising on the test set, with 99.17% accuracy, 99.23% recall, 99.13% precision, 99.56% AUC, and 99.17% F1-score, respectively. The proposed methodology offers a solution that boasts high performance for the diagnostics of tomato crops in the actual agricultural setting.