A new AI-based approach for automatic identification of tea leaf disease using deep neural network based on hybrid pooling.

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.