数字农科院2.0

SPADE: A DEEP LEARNING FRAMEWORK FOR AUTOMATED SEED POTATO CUTTING

文献类型： 外文期刊

作者： Huang, Jie;Wang, Xiangyou;Cheein, Fernando Auat;Jin, Chengqian

作者机构：

关键词： Potato;Cutting Angle Estimation;K-means Clustering;Support Vector Machine;YOLO

期刊名称： INMATEH-AGRICULTURAL ENGINEERING

ISSN： 2068-4215

年卷期： 2025 年 77 卷 3 期

页码：

收录情况： EI（2025版） ; ; ESCI（2025版）

摘要： Traditional manual cutting of seed potatoes is a labor-intensive, time-consuming, and inconsistent process that limits large-scale agricultural productivity. To address these challenges, this study aimed to develop and validate an automated, high-speed robotic system for precise cutting angle estimation. SPADE (Smart Potato Angle Decision Engine), an innovative framework integrating deep learning and machine learning algorithms, is proposed. The SPADE framework is implemented in three stages. First, a custom detection model, termed BUD-YOLO, was developed for the high-precision identification of potato eyes. Second, the K-means algorithm was employed to partition the spatial coordinates of the detected eyes into two distinct clusters. Finally, a Support Vector Machine (SVM) determined the optimal cutting plane by identifying the maximum-margin hyperplane between these two clusters. The proposed SPADE framework was implemented and tested on a custom-built robotic platform with a sample of 100 potatoes. The system achieved an 85% cutting qualification rate with an average processing time of 2.5 seconds per potato, a speed approximately 2-3 times faster than traditional manual labor (5-9 seconds per potato). This study successfully demonstrates an end-to-end solution for the automated cutting of seed potatoes. The developed SPADE framework not only achieves a competitive qualification rate but also significantly enhances production throughput, offering substantial practical value for the advancement of intelligent agricultural equipment.

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