Estimation of Greenhouse Tomato Foliage Temperature Using DNN and ML Models

• Published in: Agriculture (Basel) Vol. 12; no. 7; p. 1034
• Main Authors: Grimberg, Roei, Teitel, Meir, Ozer, Shay, Levi, Asher, Levy, Avi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 15-07-2022
• Subjects: Atmospheric models; Cameras; deep-learning; Estimation; Foliage; greenhouse; Greenhouses; Humidity; leaf temperature; Learning algorithms; Leaves; Machine learning; Neural networks; Performance evaluation; Photosynthesis; Radiation; Radiation, Background; remote sensing; Seasons; Sensors; Temperature; Tomatoes; Ventilation; Weather; United States > US; Netherlands
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture12071034

Since leaf temperature (LT) is not a trivial measurement, deep-neural networks (DNN) and machine learning (ML) models were evaluated in this study as tools for estimating foliage temperature. Two DNN methods were used. The first DNN used convolutional layers, while the second DNN was based on fully-connected layers and was trained by cross-validation techniques. The machine learning used the K-nearest neighbors (KNN) method for LT estimation. All models used the meteorological and microclimatic parameters (hereafter referred to as features) of the examined greenhouses to determine the average foliage temperature. The models were trained on 75% of the collected data and tested on the remaining 25%. RMS and absolute error were used to evaluate the performance of the different models compared to the LT values measured by a thermal camera. In addition, after finding the correlation of each feature to the leaf temperature, the models were trained based on the high-correlated features only. The machine learning model was superior to DNN when all available features were used and when only high-correlated features were used, resulting in errors of 0.7 °C and 0.8 °C, respectively.