Comparative Data Analysis of Non-Destructive Testing for Hollow Heart in Potatoes

Mary M. Hofle (), Nusrat Farheen, Mathew Zachary Shumway, Evan D. Mosher, Keyave C. Hone and Marco P. Schoen
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Mary M. Hofle: Department of Mechanical and Measurement & Control Engineering (MMCE), Idaho State University, Pocatello, ID 83209, USA
Nusrat Farheen: Department of Mechanical and Measurement & Control Engineering (MMCE), Idaho State University, Pocatello, ID 83209, USA
Mathew Zachary Shumway: Department of Mechanical and Measurement & Control Engineering (MMCE), Idaho State University, Pocatello, ID 83209, USA
Evan D. Mosher: Department of Mechanical and Measurement & Control Engineering (MMCE), Idaho State University, Pocatello, ID 83209, USA
Keyave C. Hone: Department of Mechanical and Measurement & Control Engineering (MMCE), Idaho State University, Pocatello, ID 83209, USA
Marco P. Schoen: Department of Mechanical and Measurement & Control Engineering (MMCE), Idaho State University, Pocatello, ID 83209, USA

Data, 2025, vol. 10, issue 10, 1-16

Abstract: Hollow heart, and other crop defects, can be devastating to farmers. Hollow heart is not a disease but a physiological disorder affected by temperature, soil moisture, plant density, and other factors. These defects can cause substantial annual losses for farmers. Currently, potatoes are shipped and inspected from producers to shipping points and markets. At these facilities, samples are inspected for defects. Detection of hollow heart consists of halving potatoes and visually inspecting for defects. The defect size is compared to USDA hollow heart classification charts for acceptance or rejection. An automatic, non-destructive system to identify hollow heart has the potential to improve quality. Two methods have been developed to collect data for such a system: acoustic signal capture and visual/vibration signal capture. Data is collected and stored for one potato at a time. The procedure includes the collection of weight, proportional size, and volume, as well as the generation of an acoustic sound signal through a drop test and a motion signal captured through a vision system. To simulate hollow heart, potatoes are cored and retested by producing a new set of data. Each potato is manually cut and inspected for true hollow heart. The generated data includes over 1000 samples, each comprising proportional volume, weight, proportional size, motion, and acoustic data. Such a dataset does not exist in the current literature and can serve for the development of machine learning algorithms to detect hollow heart nondestructively. In this paper, the data is also analyzed in terms of its statistical properties, as applied for possible feature engineering in machine learning.

Keywords: artificial hollow heart; acoustic sound data; motion data (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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