An LSTM Deep Learning Scheme for Prediction of Low Temperatures in Agriculture
- M. Ángel Guillén-Navarro
- Raquel Martínez- España
- Andrés Bueno-Crespo
- Belén Ayuso
- José Luis Moreno
- José M. Cecilia
- Andrés Muñoz (coord.)
- Sofia Ouhbi (coord.)
- Wolfgang Minker (coord.)
- Loubna Echabbi (coord.)
- Miguel Navarro- Cía (coord.)
Editorial: IOS Press
ISBN: 9781614999836
Año de publicación: 2019
Páginas: 130-138
Tipo: Capítulo de Libro
Resumen
Precision agriculture adopts a set of techniques capable of increasingproductivity, yield and efficiency in work related to agriculture, producing a greaterbenefit for farmers. In this study we focus on the problem of predicting weatherconditions, specifically the prediction of low temperatures. Temperature predictionis a major problem in agriculture. Farmers can lose their crops if frost control tech-niques are not activated in time. The threshold for activating such techniques de-pends on the type of crop. A first preliminary study using deep learning is proposedto predict temperature, particularly a Long Short-Term Memory Network (LSTM)is used. The LSTM has been trained using real temperature data provided by an theInternet of Things (IoT) system, deployed in several plots and currently in opera-tion. The results obtained after testing the model created with this neural networkare quite satisfactory obtaining a determination coefficient (R2) of 99% and an av-erage quadratic error of less than 0.8 degrees Celsius. Given the goodness of themodel this can be implemented as an intelligent component of the IoT system, thuscomplementing its functionality.