Özet:

Abstract     Rice disease has a substantial impact on agriculture production, and accurate rice disease diagnosis is more significant for farmers' economic development. Deep learning algorithms have made a massive impression in the context of agricultural disease identification in past few years. However, there are a number of detection techniques available, some of which may not be quite as effective as they might be. Timely detection and identification of specific disease help significantly in disease control and management. In this regard, deep learning-based convolution neural network model is developed to equip the diagnosis process for early detection. The proposed prototype is introduced by integrating XGBOOST ensemble learning model with Keras Inception ResNet V2 Framework for solving various tasks like classification of input images, object segmentation and image feature extraction. Initially, rice plant images undergo pre-processing stage for rotating, flipping, cropping and scaling to enhance image quality for the process of training and classification. The Adam optimizer is used to further optimize the proposed framework by making the learning and training process more efficient. The proposed model is applied to the augmented dataset and establishes a benchmark performance in terms of accuracy, precision, and recall.  The findings of this investigation will help to increase the practice of deep learning technology in agriculture for earlier plant disease diagnosis and prevention.

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