[论文解读] Banana Sub-Family Classification and Quality Prediction using Computer Vision

Narayana Darapaneni, Arjun Tanndalam+6 more|arXiv (Cornell University)|Apr 6, 2022

Smart Agriculture and AI被引用 8

一句话总结

该论文提出了一种基于迁移学习与数据增强的计算机视觉流程，利用MobileNet对香蕉亚种进行分类并预测其品质。模型在包含3,064张图像的合并数据集上进行训练，亚种分类准确率达到93.4%，品质预测准确率为100%，实现了香蕉供应链与零售运营的自动化。

ABSTRACT

India is the second largest producer of fruits and vegetables in the world, and one of the largest consumers of fruits like Banana, Papaya and Mangoes through retail and ecommerce giants like BigBasket, Grofers and Amazon Fresh. However, adoption of technology in supply chain and retail stores is still low and there is a great potential to adopt computer-vision based technology for identification and classification of fruits. We have chosen banana fruit to build a computer vision based model to carry out the following three use-cases (a) Identify Banana from a given image (b) Determine sub-family or variety of Banana (c) Determine the quality of Banana. Successful execution of these use-cases using computer-vision model would greatly help with overall inventory management automation, quality control, quick and efficient weighing and billing which all are manual labor intensive currently. In this work, we suggest a machine learning pipeline that combines the ideas of CNNs, transfer learning, and data augmentation towards improving Banana fruit sub family and quality image classification. We have built a basic CNN and then went on to tune a MobileNet Banana classification model using a combination of self-curated and publicly-available dataset of 3064 images. The results show an overall 93.4% and 100% accuracy for sub-family/variety and for quality test classifications respectively.

研究动机与目标

• 在供应链和零售环境中实现香蕉亚种与品质分类的自动化。
• 通过计算机视觉减少库存管理、称重和计费中的手动工作量。
• 利用有限但多样化的香蕉图像数据，开发一个稳健且准确的分类系统。
• 评估迁移学习与数据增强在低资源水果分类任务中的有效性。

提出的方法

• 首先开发了一个自定义卷积神经网络（CNN）作为基线模型。
• 使用在ImageNet上预训练的MobileNet架构应用迁移学习。
• 从自建数据集和公开可用来源构建了包含3,064张图像的混合数据集。
• 采用数据增强技术以提升模型泛化能力并减少过拟合。
• 针对两个独立的分类任务对模型进行微调：亚种/品种分类与果实品质分类。
• 在保留的测试集上使用标准准确率指标进行评估。

实验结果

研究问题

• RQ1 在有限图像数据条件下，计算机视觉模型能否准确分类香蕉亚种？
• RQ2 迁移学习在香蕉果实图像分类中的性能提升程度如何？
• RQ3 数据增强在低数据量场景下能否增强模型的鲁棒性与泛化能力？
• RQ4 所提出的流程在零售与供应链应用中预测香蕉品质的有效性如何？

主要发现

• 模型在测试图像中对香蕉亚种或品种的分类准确率达到93.4%。
• 品质预测任务达到100%的准确率，表明该分类任务表现优异。
• 使用MobileNet的迁移学习显著提升了模型收敛速度与准确率，优于自定义CNN基线模型。
• 数据增强在中等规模数据集条件下，有效提升了模型的泛化能力。
• 综合流程在自动化香蕉分拣与库存系统中的实际部署中展现出强大潜力。