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Timely monitoring of fruit weight is a paramount concern for the improvement of productivity and quality in strawberry cultivation. Therefore, the present study was conducted to introduce a simple non-destructive technique with machine learning models in measuring fruit weight of strawberries. Nine hundred samples from three strawberry cultivars, i.e., Seolhyang, Maehyang, and Santa (300 samples in each cultivar), in six different ripening stages were randomly collected for determining length, diameter, and weight of each fruit. Pixel numbers of each captured fruit’s image were calculated using image processing techniques. A simple linear-based regression (LR) and a nonlinear regression, i.e., support vector regression (SVR) models were developed by using pixel numbers as input parameter in modeling fruit weight. Findings of the study showed that the LR model performed slightly better than the SVR model in estimating fruit weight. The LR model could explain the relationship between the pixel numbers and fruit weight with a maximum of 96.3% and 89.6% in the training and the testing stages, respectively. This new method is promising non-destructive, time-saving, and cost-effective for regularly monitoring fruit weight. Hereafter, more strawberry samples from various cultivars might need to be examined for the improvement of model performance in estimating fruit weight.

The purpose of this study was to conduct a modeling-based energy performance assessment and validation of an air-to-water heat pump (AWHP) system, in the cooling mode, integrated with a multi-span greenhouse using TRNSYS software. We used the building energy simulation (BES) model to investigate the performance characteristics of the AWHP system for greenhouse cooling. We modelled the components of the AWHP system, including the fan coil unit (FCU), water storage tank, and water circulation pump integrated with the greenhouse model. The proposed model included all the components of the experimental system. We validated the proposed model by comparing the simulation results with those obtained from field experiments. We investigated the cooling energy supply to the multi-span greenhouse, greenhouse internal air temperature, heat pump (HP) output temperature, and coefficient of performance (COP). We evaluated the performance of our model by calculating the Nash–Sutcliffe efficiency (NSE) coefficient of all the validated components. Furthermore, we performed linear regression analyses (R2) to determine the relationship between the different parameters. NSE values of 0.87, 0.81, and 0.93, for the greenhouse internal air temperature, the energy supply to the greenhouse, and the HP output water temperature, respectively, validated the prediction accuracy of the model. Moreover, R2 values of 0.83 and 0.39 indicated that cooling loads are more dependent on ambient solar radiation than ambient air temperature. Furthermore, an R2 value of 0.91 showed a linear relationship between the HP’s energy consumption and ambient air temperature. The average daily COP of the HP system was 2.9. Overall, the simulation results showed acceptable correlation with the experimental results. The high NSE values validated the high predictive power of the model. The proposed validation model can be used to improve the performance of systems by optimizing the control strategies and capacities of the equipment (e.g., the HP, the FCU, and the area of the greenhouse). We have provided detailed information to enable engineers, researchers, and consultants to implement the model for their specific needs.

Intensively grown strawberries in a greenhouse require frequent and precise soil physicochemical constituents for optimal production. Strawberry leaf color analyses are the most effective way to evaluate soil status and protect against excess environmental nutrients and financial setbacks. Meanwhile, precision agriculture (PA) endorsements have been utilized to mimic solutions to these problems. This research aimed to create machine learning models such as multiple linear regression (MLR) and gradient boost regression (GBR) for simulating strawberry leaf color changes related to soil physicochemical components and plant age using RGB (red, green, and blue) mean values. The soil physicochemical properties of the largest varied colored leaves of strawberry were precisely measured by a multifunctional soil sensor from the rooting zones. Simultaneously, 400 strawberry leaflets were detached in each vegetative and reproductive stage, and individual leaves were captured using a digital imaging system. The RGB mean values of colored images were extracted using the image segmentation algorithms of image processing technique. Consequently, MLR and GBR models were developed to predict leaf RGB mean values based on soil physicochemical measurements and plant age. The GBR model vigorously fitted with RGB mean values throughout the growth stage, with R2 and RMSE values of (R = 0.77, 7.16, G = 0.72, 7.37, and B = 0.70, 5.68), respectively. Furthermore, the MLR model performed moderately with R2 and RMSE values of (R = 0.67, 8.59, G = 0.57, 9.12, and B = 0.56, 6.81) when consecutively predicting RGB mean values in strawberry leaves. Eventually, the GBR model performed more effectively than the MLR model with high-performance metrics. In addition, the leaf color model uses visualization technology to measure growth progress, and it performs well in predicting dynamic changes in strawberry leaf color.

In this study, we propose a building energy simulation model of a multi-span greenhouse using a transient system simulation program to simulate greenhouse microenvironments. The proposed model allows daily and seasonal control of screens, roof vents, and heating setpoints according to crop needs. The proposed model was used to investigate the effect of different thermal screens, natural ventilation, and heating setpoint controls on annual and maximum heating loads of a greenhouse. The experiments and winter season weather conditions of greenhouses in Taean Gun (latitude 36.88° N, longitude 126.24° E, elevation 45 m) Chungcheongnam-do, South Korea was used for validation of our model. Nash–Sutcliffe efficiency coefficients of 0.87 and 0.71 showed good correlation between the computed and experimental results; thus, the proposed model is appropriate for performing greenhouse thermal simulations. The results showed that the heating loads of the triple-layered screen were 70% and 40% lower than that of the single-screen and double-screen greenhouses, respectively. Moreover, the maximum heating loads without a screen and for single-, double-, and the triple-layered screens were 0.65, 0.46, 0.41, and 0.34 MJ m−2, respectively. The analysis of different screens showed that Ph-77 (shading screen) combined with Ph-super (thermal screen) had the least heating requirements. The heating setpoint analysis predicted that using the designed day- and nighttime heating control setpoints can result in 3%, 15%, 14%, 15%, and 40% less heating load than when using the fixed value temperature control for November, December, January, February, and March, respectively.