Host: The Japan Society of Mechanical Engineers
Name : [in Japanese]
Date : June 05, 2019 - June 08, 2019
This paper describes the development of a harvest time prediction system based on color information of tomato. For stable supply of agricultural crops, it is important that prediction of harvest time and amount of harvest. Generally, the harvest time of agricultural crops has various factors such as air temperature, soil temperature, humidity, and so on. It is difficult to measure all of these factors for each crops. In this research, we proposed a harvest time prediction system based on Long Short-Term Memory (LSTM). The LSTM predicts the time series data of tomato's color information. Regarding the accuracy of color prediction and how many input data is needed, we comparing the single layered LSTM with double layered LSTM in this paper. The double layered LSTM achieved the more accuracy prediction with less input data than single layered LSTM.
