Multi-item Prediction Using LSTM with Single Data for Plant Growth

Abstract

In recent years, food problems have arisen due to population changes. To solve this problem, Advanced technologies such as robots and artificial intelligence are increasingly being used to improve the efficiency of agriculture. In particular, plant factories are attracting attention because they have a high affinity for advanced technologies and can be produced regardless of the cultivation location and climate. However, production in plant factories exhibits of higher management costs and lower profitability than traditional cultivation methods. It is thought that this problem can be solved by predicting plant growth and notifying the farm manager. In this research, we will use data that can be measured at plant factories to create a machine learning model which predicts, both the size and weight of an agricultural product from a single piece of data. As a result, we were able to predict multiple items using a relatively lightweight model. The overall error was small, with an average error rate of about 15%. Although the average error rate for weight was about 30%, we were able to create a model that behaves close to the actual measured values.