Summary of Real-time Object Detection and Robotic Manipulation For Agriculture Using a Yolo-based Learning Approach, by Hongyu Zhao et al.
Real-time object detection and robotic manipulation for agriculture using a YOLO-based learning approach
by Hongyu Zhao, Zezhi Tang, Zhenhong Li, Yi Dong, Yuancheng Si, Mingyang Lu, George Panoutsos
First submitted to arxiv on: 28 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a new framework that combines two convolutional neural networks (CNNs) to automate crop detection and harvesting using machine vision. The framework uses augmented images of crops with random rotations, cropping, brightness, and contrast adjustments to train the CNNs. The first CNN detects crops, while the second CNN localizes grasping positions for robotic manipulation. This approach aims to enhance harvesting efficiency in agricultural industrialisation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Farmers can use machine vision to help them pick crops more efficiently. Researchers have been trying to make this process work better by combining two computer models that look at pictures of crops. The first model finds the crop, and the second model tells a robot where to grab it. To train these models, scientists created fake pictures of crops with different lighting and angles. This new approach could help make farming more efficient. |
