Tomato Growth State Map for the Automation of Monitoring and Harvesting
Takuya Fujinaga, Shinsuke Yasukawa, Kazuo Ishii
Abstract
To realize smart agriculture, we engaged in its systematization, from monitoring to harvesting tomato fruits using robots. In this paper, we explain a method of generating a map of the tomato growth states to monitor the various stages of tomato fruits and decide a harvesting strategy for the robots. The tomato growth state map visualizes the relationship between the maturity stage, harvest time, and yield. We propose a generation method of the tomato growth state map, a recognition method of tomato fruits, and an estimation method of the growth states (maturity stages and harvest times). For tomato fruit recognition, we demonstrate that a simple machine learning method using a limited learning dataset and the optical properties of tomato fruits on infrared images exceeds more complex convolutional neural network, although the results depend on how the training dataset is created. For the estimation of the growth states, we conducted a survey of experienced farmers to quantify the maturity stages into six classifications and harvest times into three terms. The growth states were estimated based on the survey results. To verify the tomato growth state map, we conducted experiments in an actual tomato greenhouse and herein report the results.