Development of Potato Mass Estimation System Based on Deep Learning

Abstract

Potato is one of the world’s four major food crops as an important resource cultivated in about 150 countries. As precision agriculture has recently attracted increasing attention for its role in improving productivity, interest in yield monitoring is also increasing. Yield monitoring is a precision agriculture technology, and it can help farmhouse business management in the future by contributing to variable fertilization and supply and demand control. The present study was carried out to develop and evaluate a system that uses machine vision and deep learning technologies to estimate potato mass to monitor potato yield. The system performs object classification using the YOLOv5 algorithm to sort out potatoes among various foreign substances, object tracking using the DeepSORT algorithm to track the sorted potatoes, and volume calculation using the lengths of the major axis and minor axis of the tracked potatoes. The results of analyzing the function of the developed yield monitoring system showed an object detection rate of 95.2% and a weight measurement error of 9%, indicating that the computation load must be reduced through algorithm optimization to improve the accuracy and that error correction needs to be performed based on the potato position within the view angle.