Fruits Sorting Machine
In Guilin, I built a machine vision-based orange sorting system from the ground up. Using an industrial camera, we collected over 100,000 images of oranges to create a robust dataset, trained deep learning models, and achieved automatic recognition and classification of fruit quality.
在桂林，我们搭建了一套 基于机器视觉的橘子分选机。系统从零开始设计，利用工业相机采集了超过 10 万张橘子图像，构建数据集并训练深度学习模型，实现了对橘子外观质量的自动识别与分类。
To make the system not only accurate but also fast, I deployed the model on an embedded platform and optimized inference performance for real-time operation. Through careful hardware–software co-design, the sorter ran with both stability and efficiency in practice.
为了让分选机不仅能“看得准”，还能“反应快”，我们将模型部署到嵌入式系统上，并针对实时性进行了推理优化和算力调度。硬件与软件的协同设计，让这套机器在实际分选中稳定高效运行。
Prototype： This project brought computer vision, deep learning, and embedded systems together into a working industrial automation solution—turning research ideas into a real machine that could sort fruit in the field.
这是一次把计算机视觉、深度学习和嵌入式系统真正结合起来的实践，它不仅解决了具体的农业分选问题，也让我在工业自动化的场景里，看到技术如何落地成一个真正可用的系统。
Mar 1, 2022
