Banana Dataset

Bananas are widely farmed and consumed, offering essential nutrients like manganese, vitamin B6, vitamin C, and magnesium. They come in various breeds with distinct visual traits, including size, shape, color, texture, and skin patterns. To classify these varieties, five deep learning models—VGG16, ResNet50, MobileNet, Inception-v3, and a customized CNN—were trained on banana images. These models enhance quality control and supply chain management by accurately identifying banana breeds. Performance evaluation showed that the customized CNN model achieved the highest accuracy of 99.37%, making it the most effective for precise banana classification in agricultural and food industries using machine learning and computer vision.

香蕉是全球广泛种植与消费的作物，富含锰、维生素B6、维生素C及镁等必需营养物质。该作物拥有多个品种，各品种均具备独特的外观特征，涵盖尺寸、形状、颜色、质地与果皮纹路等维度。为实现香蕉品种的自动分类，研究人员在香蕉图像数据集上训练了五种深度学习模型，分别为VGG16、ResNet50、MobileNet、Inception-v3以及自定义卷积神经网络（Convolutional Neural Network，CNN）。上述模型可通过精准识别香蕉品种，助力农业与食品产业的质量管控及供应链管理。性能评估结果显示，自定义卷积神经网络模型的分类准确率最高，达到99.37%，是当前基于机器学习与计算机视觉技术、应用于农业及食品工业香蕉精准分类的最优方案。