Evaluation of time-series libraries for temperature prediction in smart greenhouses

Nowadays, human overpopulation is stressing our ecosystems in different ways, being agriculture a critical example as different predictions point towards food shortages in the near future. In such context, smart farming is becoming key to optimize natural resources so that different crops are grown efficiently, consuming as few resources as possible. In particular, greenhouses have shown to be an effective approach to producing a high volume of vegetables/fruits in a reduced space and within a short time span. Hence, optimizing greenhouse functioning results in less water and nutrient consumption, less energy use, faster growth, and better product quality. In this paper, we take a step in this direction by studying the best approach to forecast greenhouse temperature based on univariate time-series analysis. In particular, several widely used time-series libraries such as Prophet by Facebook, Greykite by LinkedIn and TPOT are studied to figure out which performs better for this particular scenario. Results show that the maximum prediction error ranges from 1.5 to 3 degrees Celsius, and, in general terms, Greykite is found to be the best performing library for this particular environment.