A deep learning-based model for biotic rice leaf disease detection

The detection of rice leaf disease is an essential step for implementing precise and timely interventions, thereby mitigating the spread and minimizing the ecological and economic consequences. This work proposes a deep learning-based biotic rice leaf disease detection model wherein ensemble models have been proposed using pre-trained models as feature extractors and machine learning/deep learning classifiers for performing classification. VGG16, SqueezeNet, and InceptionV3, have been used as feature extractors. The fine-tuning of various pre-trained models has been done by setting the hyperparameters to the prominent values for achieving optimal results. These hyperparameters include batch size, learning rate, optimizer, epochs, train, and test ratio. The model utilizes various machine learning and deep learning classifiers to perform the multiclass classification on extracted features from a pooled rice leaf disease dataset. In the proposed work, SqueezeNet with neural network classifier achieved the highest accuracy of 93.3%, Area Under Curve (AUC) of 0.989, precision of 0.928, recall of 0.931, and F1-score of 0.928.