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This paper presents the electrical performance of organic photovoltaic modules (OPVs) on top of a polyethylene covered greenhouse high tunnel in a Mediterranean climate. Modules from a previous study were kept on the tunnel and monitored together with new modules with improved connectors installed on the greenhouse roof and on frames adjacent to the greenhouse. Measured module power conversion efficiencies ranged from 1% to 3%. The typical combined output of the modules across the tunnel roof were 105Wh on a sunny day and 81Wh on a cloudy day. Module burn-in period was about 15 days, losing around 36% of its initial efficiency. Ts80 lifetimes ranged from 7 days to 94 days. Tunnel integration was shown to accelerate module degradation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]