Paddy Disease Recognition and Classification using Artificial Neural Networks: Introducing a Novel Hybrid Optimization Algorithm
| dc.contributor.author | Farida Siddiqi Prity | |
| dc.date.accessioned | 2025-11-23T04:18:56Z | |
| dc.date.available | 2025-11-23T04:18:56Z | |
| dc.date.issued | 2024-03-20 | |
| dc.description.abstract | Paddy diseases have significantly deteriorated the production capacity of the paddy plant, resulting in substantial economic losses. It becomes imperative to identify paddy diseases early to effectively mitigate and minimize the detrimental impacts of these diseases. Timely detection of infections enables effective management strategies and leads to enhanced yield in terms of quantity and quality. Hence, it is imperious to develop a methodology that can swiftly and accurately detect paddy diseases. Artificial Neural Networks (ANNs) have emerged as a promising approach for paddy disease recognition. Nevertheless, training ANNs pose significant challenges, particularly when dealing with intricate networks containing numerous parameters. Prior research has explored single optimization algorithms for ANN training; however, these algorithms are hindered by constraints such as convergence problems, limited exploration capabilities, inadequate adaptability, and inconsistent performance across diverse problem domains. Consequently, there is a pressing demand for a hybrid optimization algorithm that can effectively address these challenges by offering an optimal solution for optimizing ANN parameters. Therefore, this paper aims to devise a novel hybrid optimization algorithm, namely Adaptive Particle Swarm Water Wave Optimization (APSWWO), for finding the disease in the paddy plant, where the training of ANN is completed using the APSWWO, which is formed by assimilating two nature-inspired optimization algorithms Adaptive Particle Swarm Optimization (APSO) and Water Wave Optimization (WWO). Initially, the images of paddy leaf disease are pre-processed and balanced. Two customized ANN models: 26-layer Convolutional Neural Network model and 13-layer Lightweight Vision Transformer (LiteViT) are used to classify ten types of paddy diseases and one healthy class. The optimization of parameters in the training process is achieved by incorporating the APSWWO algorithm. This study also employs Explainable AI techniques such as Gradient-weighted Class Activation Mapping (Grad-CAM) to provide model interpretability and visualizations in each layer, enabling more transparent and trustable disease detection. Accuracy, recall, precision, and F-measure metrics are utilized to assess classification performance. The hybrid approach's effectiveness is evaluated by comparing ANN techniques performance with and without optimization (Original, PSO, APSO, WWO, and APSWWO), highlighting the importance of hybrid APSWWO in enhancing prediction performance. The hybrid CNN_APSWWO outperforms other forms (CNN_Original, CNN_PSO, CNN_APSO, CNN_WWO, LiteViT_Original, LiteViT_PSO, LiteViT_APSO, LiteViT_WWO, and LiteViT_APSWWO), offering valuable insights for optimizing paddy disease recognition and classification, ultimately enhancing agricultural practices and contributing to improved crop health and food security. Keywords: Paddy; Disease; Artificial Neural Network; Nature-inspired Optimization; Hybrid Algorithm; Explainable AI | |
| dc.identifier.uri | https://dspace.nstu.ac.bd/handle/123456789/111 | |
| dc.title | Paddy Disease Recognition and Classification using Artificial Neural Networks: Introducing a Novel Hybrid Optimization Algorithm |