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In India, rice crops are very significant. Rice cultivation comprises several phases, and it is crucial to keep an eye on the crop's development to avoid any leaf diseases and to provide a good yield. To avoid yield loss, crop diseases need to be determined at the initial stage. Deep learning-based pre-trained CNN architecture is used in this study to identify rice leaf diseases. This paper discusses four different CNN architectures to classify and identify healthy and diseased leaves such as Brown spot, Hispa, and Leaf Blast. Initially, to avoid vanishing gradient problems that degrade the performance of the Network, ResNet34 and ResNet50 are used. Even though the CNN model performs the feature extraction, Self-attention with ResNet18 and ResNet34 architecture is utilized to improve the feature selection process. As a result of enhanced feature extraction, the accuracy of rice leaf disease identification and classification has improved. Finally, high accuracy of 98.54% is achieved with the proposed ResNet34 with self-attention architecture when compared to other CNN models used in this paper. In terms of multiclass classification, the proposed model offers improved outcomes when compared to state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Feature selection plays a key role in reducing the dimensionality of a feature vector by discarding redundant and irrelevant ones. In this paper, a Genetic Algorithm-based hierarchical feature selection (HFS) model has been designed to optimize the local and global features extracted from each of the handwritten word images under consideration. In this context, two recently developed feature descriptors based on shape and texture of the word images have been taken into account. Experimentation is conducted on an in-house dataset of 12,000 handwritten word samples written in Bangla script. This database comprises names of 80 popular cities of West Bengal, a state of India. Proposed model not only reduces the feature dimension by nearly 28%, but also enhances the performance of the handwritten word recognition (HWR) technique by 1.28% over the recognition performance obtained with unreduced feature set. Moreover, the proposed HFS-based HWR system performs better in comparison with some recently developed methods on the present dataset. [ABSTRACT FROM AUTHOR]