Optimizing Computational Cost in Deep Learning Models Based Binary Feature Map-Splitting Architecture (BFMSA) for Plant Leaf Disease Classification

This paper presents a novel approach of reconstructing topology of a deep learning model to reduce model’s trainable parameters, called Binary Feature Map-Splitting Architecture (BFMSA). The proposed approach is trained using the PlantVillage dataset for plant disease classification. A simple CNN-based BFMSA and various pre-trained models, such as InceptionV3, ResNet50, VGG19, and VGG16 models based on BFMSA, are experimented. The research has two main contributions. First, reducing the computational cost while building a CNN model from scratch based on BFMSA, where the reduction would bein the feature extraction and classification phase. Second, reducing the computational cost while building a transfer learning model, and the reduction would be in the classification phase. The study compares the proposed architecture with traditional architecture and evaluates performance using various metrics such as accuracy, loss, F1-score, precision, and recall. The findings indicate reduced overfitting and improved validation accuracy in the proposed architecture. The CNN model-based BFMSA achieved the highest accuracy of 98.31% on the validation set in comparison with traditional architecture. Whereas VGG16-basedBFMSA achieved the highest accuracy among transfer learning models based BFMSA with a validation accuracy of 97.32%. Additionally, the proposed architecture decreases the trainable parameters by up to 87% compared to traditional models.