Potato Data Set

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, 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 producing a new set data. Each potato is manually cut and inspected for true hollow heart. The generated data includes over 1,000 samples, each comprised of volume, weight, size, motion, and acoustic data. Such data can serve for the development of machine learning algorithms to detect hollow heart non-destructively. In this paper, the data is also analyzed in terms of its statistical properties, as applied for possible feature engineering in machine learning.