This project develops a systematized approach to strawberry feature detection. Evaluating the appearance of a strawberry is necessary to assess the quality of fruit and determine characteristics of different varieties for commercial and breeding purposes. The current method uses visual observations, a highly subjective approach that also relies on experiential knowledge. A systematized, analytical approach to strawberry feature detection would reduce both time and labor. In this project, several key features of a strawberry, such as color, shape, and size, are extracted using machine vision technology and image processing tools in MATLAB. These results can be used to create an algorithmic predictor of strawberry variety characteristics for strawberry producers and breeders.

Additional Resources

• Breeders toolbox. (2011). Genome Database for Rosaceae. Retrieved from https://app.bioinfo.wsu.edu
• HSV to RGB color conversion. RapidTables. Retrieved from https://www.rapidtables.com/convert/color/hsv-to-rgb.html
• Martin, D. (2017, January 30). A Practical Guide to Machine Vision Lighting. National Instruments. Retrieved from http://www.ni.com/white-paper/6901/en/
• Strawberries. (2015). Ag Marketing Resource Center. Retrieved from https://www.agmrc.org/commodities-products/fruits/strawberries/
• Yamamoto, K., Ninomiya, S., Kumra, Y., Hashimoto, A., Yoshioka, Y., & Kameoka, T. (2015). Strawberry cultivar identification and quality evaluation on the basis of multiple fruit appearance features. Computers and Electronics in Agriculture, 110(1), 233-240.
• Xu, L., & Zhao, Y. (2010). Automated strawberry grading system based on image processing. Computers and Electronics in Agriculture, 71, S32-S39.