Identification of Cotton Species and Luxury Product Recommendation using Deep Learning

Abstract

Cotton species determination has major implications for agriculture, textile manufacturing, and luxury product design. A brief yet deployable deep learning pipeline is proposed for accurately classifying four cotton species of commercial importance: G. arboreum, G. barbadense, G. herbaceum, and G. hirsu tum. A curated dataset of 20,850 high-resolution images collected from public repositories and controlled photography was standardized and further augmented to promote generalization. For fiber texture, boll morphology, and vein patterns, the model utilizes ResNet50 in conjunction with a Convolutional Block At tention Module (CBAM) to emphasize spatial and channel-wise features. Transfer learning with ImageNet weights, coupled with suitable regularization, established robust performance with a Top-1 test accuracy of 94.2% and a macro F1-score of 0.938. Grad-CAM visualizations confirm that the model attended to discriminative fiber regions, enhancing interpretability. A mobile-friendly Streamlit web application delivers real-time species predictions with an average latency of 0.84 seconds and maintains 93.4% accuracy under f ield trials. Predicted species map to luxury product recommendations via a transparent rule-based mapper that synthesizes species identity and an image-derived quality score to suggest product categories (e.g., pre mium apparel, high-end home textiles, artisanal linens) with brief reasoning behind each recommendation. This integrated pipeline connects farmers, agronomists, and textile professionals to translate species-level identification into informed product decisions, quality assurance, and value-added marketing. The study demonstrates that attention-augmented residual networks constitute an effective solution for fine-grained agricultural image classification and its real-world industry implementation.