用CNN实现芒果自动分类，提升农场质检效率
CNN-based solution for mango classification in agricultural environments
- 基于ResNet-18与级联检测器构建检测分类系统
- 在农业环境下实现高精度芒果分类，兼顾速度与资源消耗
- 适合智能农业、自动化质检场景使用
本文提出一种基于卷积神经网络（CNN）的水果检测与分类系统，旨在实现农场库存管理中的果实品质自动评估。针对芒果分类任务，采用图像处理方法，确保分类的准确性和高效性。以ResNet-18作为分类主干架构，结合级联检测器完成目标检测，在执行速度与计算资源消耗间取得平衡。检测与分类结果通过MatLab App Designer开发的图形化界面实时展示，优化系统交互体验。该集成方案为农业质量控制提供可靠技术路径，具备实际应用潜力。
原文摘要 · Abstract (English)
This article exemplifies the design of a fruit detection and classification system using Convolutional Neural Networks (CNN). The goal is to develop a system that automatically assesses fruit quality for farm inventory management. Specifically, a method for mango fruit classification was developed using image processing, ensuring both accuracy and efficiency. Resnet-18 was selected as the preliminary architecture for classification, while a cascade detector was used for detection, balancing execution speed and computational resource consumption. Detection and classification results were displayed through a graphical interface developed in MatLab App Designer, streamlining system interaction. The integration of convolutional neural networks and cascade detectors proffers a reliable solution for fruit classification and detection, with potential applications in agricultural quality control.
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