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Estimation of Greenhouse Tomato Foliage Temperature Using DNN and ML Models
Year:
2022
Source of publication :
Agriculture
Authors :
Lavi, Asher
;
.
Ozer, Shay
;
.
Teitel, Meir
;
.
Volume :
Co-Authors:

Roei Grimberg

Meir Teitel

Shay Ozer

Asher Levi

Avi Levy

 

Facilitators :
From page:
0
To page:
0
(
Total pages:
1
)
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.

Note:
Related Files :
Deep learning
greenhouse
Leaf temperature
Machine learning
remote sensing
Show More
Related Content
More details
DOI :
10.3390/agriculture12071034
Article number:
0
Affiliations:
Database:
Scopus
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
62422
Last updated date:
02/11/2022 14:00
Creation date:
02/11/2022 13:32
You may also be interested in
Scientific Publication
Estimation of Greenhouse Tomato Foliage Temperature Using DNN and ML Models

Roei Grimberg

Meir Teitel

Shay Ozer

Asher Levi

Avi Levy

 

Estimation of Greenhouse Tomato Foliage Temperature Using DNN and ML Models

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.

Scientific Publication
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