Heng Qidong, Yu Sibo, Zhang Yandong
School of Public Administration, Beijing City University, Beijing, 100083, China.
School of Information Science and Engineering, Beijing City University, Beijing, 100083, China.
Heliyon. 2024 Feb 18;10(5):e26465. doi: 10.1016/j.heliyon.2024.e26465. eCollection 2024 Mar 15.
The degree of production efficiency and the quality of the commodities produced may both be directly impacted by the presence of illnesses in tea leaves. These days, this procedure may be automated with the use of artificial intelligence tools, and a number of approaches have been put out to satisfy these needs. Nonetheless, current research efforts have focused on improving diagnosis accuracy and expanding the variety of illnesses that might affect tea leaves. In this article, a new method is proposed for accurately diagnosing tea leaf diseases using artificial intelligence techniques. In the proposed method, the input images are preprocessed to remove redundant information. Then, a hybrid pooling-based Convolutional Neural Network (CNN) is employed to extract image features. In this method, the pooling layers of the CNN model are randomly adjusted based on either max pooling or average pooling functions. This strategy can enhance the efficiency of the CNN-based feature extraction model. In this method, the pooling layers of the CNN model are randomly adjusted based on either max pooling or average pooling functions. This strategy can enhance the efficiency of the CNN-based feature extraction model. After feature extraction, a weighted Random Forest (WRF) model is used for the detection of tea leaf diseases. The outputs of the decision tree models and their corresponding weights are used to identify tea leaf illnesses in this classification model, where each tree in the random forest is given a weight depending on how well it performs. The Cuckoo Search Optimization (CSO) method is used in the proposed classification model to give a weight to each tree. Tea Sickness Dataset (TSD) has been used as the basis for evaluating the suggested method's effectiveness. The findings show that the suggested approach has an average accuracy of 92.47% in identifying seven different forms of tea leaf illnesses. Additionally, the recall and accuracy metrics indicate results of 92.35 and 92.26, respectively, indicating improvements over earlier techniques.
茶叶中的病害可能会直接影响生产效率和所生产商品的质量。如今，这一过程可以通过使用人工智能工具实现自动化，并且已经提出了多种方法来满足这些需求。尽管如此，目前的研究工作主要集中在提高诊断准确性和扩大可能影响茶叶的病害种类上。在本文中，提出了一种使用人工智能技术准确诊断茶叶病害的新方法。在所提出的方法中，对输入图像进行预处理以去除冗余信息。然后，采用基于混合池化的卷积神经网络（CNN）来提取图像特征。在该方法中，CNN模型的池化层根据最大池化或平均池化函数进行随机调整。这种策略可以提高基于CNN的特征提取模型的效率。在该方法中，CNN模型的池化层根据最大池化或平均池化函数进行随机调整。这种策略可以提高基于CNN的特征提取模型的效率。特征提取后，使用加权随机森林（WRF）模型进行茶叶病害检测。在这个分类模型中，决策树模型的输出及其相应权重用于识别茶叶病害，其中随机森林中的每棵树根据其表现被赋予一个权重。在所提出的分类模型中使用布谷鸟搜索优化（CSO）方法为每棵树赋予权重。茶叶病害数据集（TSD）已被用作评估所提方法有效性的基础。结果表明，所提方法在识别七种不同形式的茶叶病害时平均准确率为92.47%。此外，召回率和准确率指标分别显示为92.35和92.26，表明比早期技术有所改进。
