Evaluation of synthetic data generation for intelligent climate control in greenhouses
Author/s
Morales García, Juan; Bueno Crespo, Andrés; Terroso Sáenz, Fernando; Arcas Túnez, Francisco; Martínez España, Raquel; [et al.]Date
2023-07-28Discipline/s
Ingeniería, Industria y ConstrucciónSubject/s
Deep LearningSynthetic time series data generation
Generative Adversarial Networks
Time series forecasting
Abstract
We are witnessing the digitalization era, where artificial intelligence (AI)/machine learning (ML) models are mandatory to transform this data deluge into actionable information. However, these models require large, high-quality datasets to predict high reliability/accuracy. Even with the maturity of Internet of Things (IoT) systems, there are still numerous scenarios where there is not enough quantity and quality of data to successfully develop AI/ML-based applications that can meet market expectations. One such scenario is precision agriculture, where operational data generation is costly and unreliable due to the extreme and remote conditions of numerous crops. In this paper, we investigated the generation of synthetic data as a method to improve predictions of AI/ML models in precision agriculture. We used generative adversarial networks (GANs) to generate synthetic temperature data for a greenhouse located in Murcia (Spain). The results reveal that the use of synthetic data signif...