Intelligent 3D Potato Cutting Simulation System Based on Multi-View Images and Point Cloud Fusion

Abstract

The quality of seed pieces is crucial for potato planting. Each seed piece should contain viable potato eyes and maintain a uniform size for mechanized planting. However, existing intelligent methods are limited by a single view, making it difficult to satisfy both requirements simultaneously. To address this problem, we present an intelligent 3D potato cutting simulation system. A sparse 3D point cloud of the potato is reconstructed from multi-perspective images, which are acquired with a single-camera rotating platform. Subsequently, the 2D positions of potato eyes in each image are detected using deep learning, from which their 3D positions are mapped via back-projection and a clustering algorithm. Finally, the cutting paths are optimized by a Bayesian optimizer, which incorporates both the potato’s volume and the locations of its eyes, and generates cutting schemes suitable for different potato size categories. Experimental results showed that the system achieved a mean absolute percentage error of 2.16% (95% CI: 1.60–2.73%) for potato volume estimation, a potato eye detection precision of 98%, and a recall of 94%. The optimized cutting plans showed a volume coefficient of variation below 0.10 and avoided damage to the detected potato eyes, producing seed pieces that each contained potato eyes. This work demonstrates that the system can effectively utilize the detected potato eye information to obtain seed pieces containing potato eyes and having uniform size. The proposed system provides a feasible pathway for high-precision automated seed potato cutting.

Plant phenotyping relevance

多視点画像と点群融合によりジャガイモの体積および芽の3D位置を推定し、精度を検証するシステム開発が中心であるため、植物形質取得手法として含める。

Code and data availability

The supplied blocks describe a potato 3D cutting simulation system (multi-view images, COLMAP point clouds, YOLOv5 eye detection, Bayesian cutting optimization) but contain no data availability statement, no public dataset/image deposit, and no author code or model release. The only external URL (FAOSTAT) is cited as a

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