A NOVEL HYBRID APPROACH FOR PADDY LEAF DISEASE SEVERITY CLASSIFICATION USING SPATIAL BIDIRECTIONAL CONVOLUTIONAL NEURAL NETWORK CLASSIFIER

Abstract

Bacterial, viral, and fungal pathogens that cause Paddy Leaf Diseases (PLD) result in significant yield losses in rice crops. Early detection and effective management are crucial for ensuring optimal growth and productivity. For severity analysis, they used the publicly accessible Mendeley Rice Leaf Disease Dataset, which contains images of the illnesses Brown Spot, Blight, Blast, and Tungro. Gaussian Blur is a multi-step pre-processing technique that smoothens noisy images by identifying regions where intensity changes rapidly.Further image segmentation using Otsu's thresholding, which automatically determines the optimal threshold to separate the foreground and background based on image histogram analysis and feature extraction using an autoencoder, can be used to learn high-level features, and Scale-Invariant Feature Transform (SIFT) can capture local spatial information, enhancing the overall feature set for downstream tasks. Select KBest for the feature selection method that selects the top k features, evaluates each feature's relevance to the target variable, and retains the most informative ones. The proposed Spatial Bidirectional Convo Neural Feedforward Net (SBCNFN) integrates Convolutional Neural Networks (CNN) for spatial feature extraction, Feedforward Neural Networks (FNN) for nonlinear feature mapping, and Bidirectional Long Short-Term Memory (Bi-LSTM) networks for capturing bidirectional temporal dependencies in spatial sequences. Grid Search optimizes the model by fine-tuning hyperparameters to achieve the best performance. Python tools and libraries were used for model development. The proposed model used to classify the severity levels of paddy leaf disease, like Mild 98.62%, Average 96.7%, Severe 98.12%, and Profound Accuracy of 98.66%.  The findings demonstrate that the suggested approach works better than the current approaches by offering more precise and trustworthy estimates of disease severity.