Estimation of Greenhouse Tomato Foliage Temperature Using DNN and ML Models

Roei Grimberg, Meir Teitel, Shay Ozer, Asher Levi and Avi Levy
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Roei Grimberg: Institute of Agricultural Engineering, Agricultural Research Organisation, Volcani Center, HaMaccabim Road 68, P.O. Box 15159, Rishon LeZion 7505101, Israel
Meir Teitel: Institute of Agricultural Engineering, Agricultural Research Organisation, Volcani Center, HaMaccabim Road 68, P.O. Box 15159, Rishon LeZion 7505101, Israel
Shay Ozer: Institute of Agricultural Engineering, Agricultural Research Organisation, Volcani Center, HaMaccabim Road 68, P.O. Box 15159, Rishon LeZion 7505101, Israel
Asher Levi: Institute of Agricultural Engineering, Agricultural Research Organisation, Volcani Center, HaMaccabim Road 68, P.O. Box 15159, Rishon LeZion 7505101, Israel
Avi Levy: Department of Mechanical Engineering, Ben-Gurion University of the Negev, P.O. Box 653, Beer-Sheva 8410501, Israel

Agriculture, 2022, vol. 12, issue 7, 1-12

Abstract: 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.

Keywords: leaf temperature; remote sensing; deep-learning; machine learning; greenhouse (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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