发表机构
Faridpur Engineering College; University of Dhaka（法里德布尔工程学院; 达卡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对孟加拉国芒果品种识别难题，构建基于EfficientNetB0的轻量级模型并集成至Streamlit网络应用，实现高准确率自动识别，为当地农业提供实用工具。
AI 中文摘要
孟加拉国的芒果品种识别颇具挑战，因为亲缘相近的栽培品种视觉特征相似，且图像常在不同现实场景下拍摄。本研究提出一种基于深度学习的自动识别孟加拉国芒果品种的网络系统。我们从当地市场和农场收集了2013张高质量芒果图像（分辨率3024×4032像素），将其分为9个类别，其中Bari-4和Bari-7合并为单个Bari类。数据集按70%、15%、15%的比例划分为训练集、验证集和测试集，并应用图像增强技术提升模型泛化能力。我们在一致的训练设置下微调了三种预训练CNN架构：ResNet18、ResNet50和EfficientNetB0。EfficientNetB0取得最优性能，验证集准确率达98.01%，测试集准确率达97.36%；ResNet18和ResNet50的测试集准确率分别为86.47%和78.55%。EfficientNetB0的类别F1分数范围为0.93至0.99，其中Bari类的F1分数为0.97。所选EfficientNetB0模型约有400万个参数，适合轻量级部署。我们将该模型集成到Streamlit网络应用中，用户可上传芒果图像并获得预测品种及类别概率。该系统为芒果识别提供了便捷实用的工具，展现了深度学习在支持孟加拉国农业应用中的潜力。
英文摘要
Mango variety identification in Bangladesh is challenging because closely related cultivars can have similar visual characteristics and images are often captured under varying real-world conditions. This work presents a deep learning-based web system for automatic identification of Bangladeshi mango varieties. We collected 2,013 high-quality mango images (3024x4032 pixels) from local markets and farms and organized them into nine classes, combining Bari-4 and Bari-7 as a single Bari class. The dataset was divided into training (70%), validation (15%), and test (15%) sets, with image augmentation applied to improve model generalization. Three pretrained CNN architectures, ResNet18, ResNet50, and EfficientNetB0, were fine-tuned under consistent training settings. EfficientNetB0 achieved the best performance, obtaining 98.01% validation accuracy and 97.36% test accuracy, compared with 86.47% and 78.55% test accuracy for ResNet18 and ResNet50, respectively. Class-wise F1-scores for EfficientNetB0 ranged from 0.93 to 0.99, while the Bari class achieved an F1-score of 0.97. The selected EfficientNetB0 model has approximately 4 million parameters, making it suitable for lightweight deployment. We integrated the model into a Streamlit web application that enables users to upload a mango image and receive a predicted variety with class probabilities. The system provides an accessible, practical tool for mango identification and demonstrates the potential of deep learning for supporting agricultural applications in Bangladesh.
CommentsAccept in Journal of Bangladesh Academy of Sciences, Volume 50, Supplement 1, April 2026. 2 authors
Journal refJournal of Bangladesh Academy of Sciences, vol. 50, Supplement 1, p. 114, 2026
