Drone-based reconnaissance for Paddy Leaf Disease Classification using a Superior Hybrid CNN Model: A Deep Learning Comparative Analysis
Keywords:
Paddy leaves, Leaf diseases, CLAHE, GLCM and hybrid convolutional neural network.Abstract
Unmanned Aerial Vehicles (UAVs), commonly known as drones, offer a revolutionary tool for precision agriculture. By leveraging their capabilities, we can efficiently gather high-resolution aerial imagery of agricultural landscapes, facilitating advanced techniques for disease detection and crop management. The paper presents a comprehensive framework for accurate and efficient paddy leaf disease sensing analysis with a hybrid convolutional neural network model. The combination of contrast-limited Adaptive Histogram Equalization (CLAHE) for image pre-processing and gray-level co-occurrence metrics (GLCM) for feature extraction seems well thought out, as these techniques can enhance the quality of input data for the classification model. Utilizing a hybrid CNN model to classify paddy leaves’ diseases, demonstrates a sophisticated approach. The model can effectively learn complex patterns and features from the pre-processed images by incorporating convolutional neural networks (CNNs), which excel at image classification tasks. The reported superiority of the proposed model in paddy leaf classification compared to previous approaches is encouraging and highlights the potential of deep learning methods in agricultural applications. Farmers can take timely preventive measures to control the spread of infections and optimize crop yield by automating detection and classifying leaf diseases.
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