Advanced diagnosis of common rice leaf diseases using KERTL-BME ensemble approach

The influence of rice leaf diseases has resulted in an annual decrease in rice mass production. This occurs mainly due to the need for more understanding in perceiving and managing rice leaf diseases. However, there has not yet been any appropriate application designed to accurately detect rice leaf diseases. This paper, we proposed a novel method called Kushner Elman Recurrent Transfer Learning-based Boyer Moore Ensemble (KERTL-BME) to detect rice leaf diseases and differentiate between healthy and diseased images. Using the KERTL-BME method, the four most common rice leaf diseases, namely Bacterial leaf blight, Brown spot, Leaf blast, and Leaf scald, are detected. First, the Kushner non-linear filter is applied to the sample images to remove noise and differentiate between measurements and expected values by pixels in the neighborhood according to time instances. This significantly improves the peak signal-to-noise ratio while preserving the edges. The transfer learning in our work uses DenseNet169 pre-trained models to extract relevant features via the Elman Recurrent Network, which improves accuracy for the rice leaf 5 disease dataset. Additionally, the ensemble of transfer learning helps to minimize generalization errors, making the proposed method more robust. Finally, Boyer–Moore majority voting is applied to minimize generalization significantly, thereby improving overall prediction accuracy and reducing prediction error promptly. The rice leaf 5 disease dataset is used for training and testing the method. Performance measures such as prediction accuracy, prediction time, prediction error, and peak signal-to-noise ratio were calculated and monitored. The designed method predicts disease-affected rice leaves with greater accuracy.