A feature-enhanced deep learning model for multi-class paddy ailment prediction in smart farming

Abstract

Paddy is an essential staple food crop eaten by more than 50% of the world population, particularly in Asia, where countries like China and India account for over 50% of both production and consumption. However, paddy cultivation is frequently threatened by numerous ailments that badly impact crop production and quality. Normal ailment identification approaches are often manual, time-consuming, and error-prone. To address these challenges, deep learning (DL)-based approaches have been widely adopted for automated paddy ailment detection. While these models have achieved commendable accuracy, many fail to focus on effective feature extraction, which is vital for accurate classify across diverse ailment categories. Hence, this study proposes an advanced convolutional neural network (CNN) based approach associated with convolutional block attention module (CBAM) for enhancing feature representation by focusing on both spatial and channel-wise importance. The approach was trained and executed on the widely used “paddy doctor” dataset, covering 2-class, 10-class, and 13-class classify scenarios. The proposed CNN-CBAM achieved outcomes with 99.75%, 99.93%, and 99.92% accuracy, respectively, outperforming existing models. These outcomes show the model’s effectiveness in capturing discriminative features and improving classify performance. This model contributes a foundation for future extensions in precision agriculture, including pest detection and real-time monitoring.