An Automatic Rice Plant Disease Classification Using Hierarchical Vision Transformers with Spatial Reduction Attention Mechanism

Rice is a staple crop for world food security, but its yield is susceptible to several diseases.Timely recognition of rice leaf diseases (RLD) is critical to decrease yield losses.Recent studies offered improved solutions by harnessing deep networks to precisely diagnose and categorize RLDs.Conversely, due to extreme scale deviations, data redundancy, and the high-resolution multi-spectral nature of leaf images, the conventional deep-learning classifiers underperform in detecting rice diseases.Also, training deep learning models involves major challenges such as class imbalance, overfitting, and vanishing gradient problems.Nowadays, vision transformers are infiltrated into the field of image processing, in which the self-attention unit is employed to learn local and distant correlations among the pixels in an image.However, the processing and storage overheads of analyzing image patches are very high.In this context, we propose a new vision transformer-based RLD classifier, called Faster Hierarchical Vision Transformer (FHViT) which employs a Spatial Reduction Attention Mechanism (SRAM) to speed up the classification process.The SRAM module enables the transformer to estimate the significance of each pixel in image patches and optimize their effect on the result.We evaluate our model on an open-access Ade F. Rice Leaf Diseases (AFRLD) database and relate its performance with other advanced models in terms of performance indicators.Our model delivers 99.2% detection accuracy, 99.3% precision, 99.2% sensitivity, 99.1% specificity, 99.0% recall, and 99.0%F1 measure.The extensive experimentations demonstrate that the FHViT realizes a viable solution for RLD diagnosis.