Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter

Global warming is one metric of climate change which is defined as an increase in the average global temperature. Residential buildings contribute significantly to the pollution that causes climate change. It is essential to have a comprehensive understanding of the functions of highly energy efficient buildings in view of projected climate projections, the quality of their heating systems, and the impact on human health and well-being. Thus, in this study, the effects of six input variables which are Overall Height, Glazing Area, Wall Area, Relative Compactness, Roof Area, and Glazing Area Distribution on one output variable, namely Heating Load (HL) of residential buildings was investigated using Multiple Linear Regression and Artificial Neural Network (MLR-ANN) approaches. Two-layer hyperbolic tangent-identity transfer functions with 6-3-1 configurations were employed as it was found as the best neural network model. A dataset of 768 residential buildings was used for secondary data. The Mean Square Error (MSE), determination coefficients R2, as well as the percentage of normalized importance analysis were used to assess the statistical prediction capabilities of the MLR-ANN model. Based on the current findings, Wall Area is the most contributing factor towards HL, followed by Relative Compactness, Roof Area, Overall Height, Glazing Area, and Glazing Area Distribution. It can be suggested that HVAC (heating, ventilation, and air conditioning) systems should be implemented in residential buildings to reduce energy use. Natural ventilation is encouraged in buildings through vernacular design, and radiant heating and cooling systems as it is very effective and efficient way of providing thermal comfort within a structure.

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“…( 1)) in the first layer and the purelin transfer function (Eq. ( 2)) in the second layer as in the study by López-Aguilar et al, [35]. In Eq.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 88%

Heating Load of Residential Buildings Using Multiple Linear Regression Artificial Neural Network

Ibrahim

Kamaruddin

Nasir

2022

ARFMTS

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“…Therefore, the accuracy of prediction of LAI and LUE is important parameter in this model. On the other hand, in recent years, there has been a lot of interest in using image analysis techniques and machine learning models to obtain physiological and biological information [7,31,32], and we suggest that these techniques can be used to improve the accuracy of prediction of LAI, LUE, and then yield.…”

Section: Discussionmentioning

confidence: 99%

“…The explanatory model consists of a quantitative description of the mechanisms and processes [4]. Several models have been developed to predict yield and dry matter (DM) production [4][5][6][7][8][9][10], especially, functional-structural plant models have been used these days widely [11], and these models have been improved in various methods [12][13][14][15]. On the other hand, a mechanical photosynthesis-based yield prediction model for cucumbers that simulated yield and fruit size as well as improved plant management, has been reported [16].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Dry Matter Production and Yield Prediction in Greenhouse Cucumber without Destructive Measurements

Maeda¹,

Ahn²

2021

Agriculture

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In this study, we aimed to estimate dry matter (DM) production and fresh fruit yield in “Fresco-dash” (FD) and “Project X” (PX) cucumber cultivars using an empirical model developed for tomatoes. First, we cultivated the two cucumber cultivars under a hydroponic system for about six months. Also, parameters related to DM production such as light use efficiency (LUE), light extinction coefficient (k), DM distribution of fruits (DMD), and fruit dry matter content (DMC) were measured via destructive measurements. The k, DMD and DMC values were 0.99 and 0.93, 46.0 and 45.2, 3.84 and 3.78 in “Fresco Dash” and “Project X”, respectively. Second, we cultivated cucumbers to estimate DM production and fruit fresh yield using the model without destructive measurement for about eight months and validated the model’s effectiveness. The predicted DM fell within the range of the observed DM ± standard error at 51 and 132 d after transplantation (DAT) in PX as well as 51 (DAT) in FD. The predicted and observed DM at 163 DAT were 2.08 and 1.82 kg m−2, 2.09 and 1.87 kg m−2 in “Fresco Dash” and “Project X”, respectively. The predicted and observed fruit yield at 200 DAT were 30.3 and 31.7, 30.5 and 29.1 in “Fresco Dash” and “Project X”, respectively, which were 4.4% lower than the observed fruit yield in FD and 4.9% higher than that in PX. These results suggest that the model applies to cucumbers in predicting dry matter production and fresh fruit yield.

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“…Therefore, deep learning may be useful for more accurate predicted results. In fact, some models have been developed to predict yield and dry matter production in tomato (Lin and Hill, 2008;Ehret et al, 2011;López-Aguilar et al, 2020) by machine learning, and a product that predicts disease (Plantect, Bosch, Japan) has also been developed. On the other hand, it is extremely difficult to obtain accurate data to create a prediction model using deep learning.…”

Section: Future Researchmentioning

confidence: 99%

A Review of Japanese Greenhouse Cucumber Research from the Perspective of Yield Components

Maeda

Ahn

2021

Hortic J

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Japanese cucumbers are unique in terms of their production methods, such as steamed cultivation in which the greenhouse is closed to increase the temperature and humidity. Moreover, Japan has strict standards for fruit size. Therefore, most research on greenhouse cucumbers in Japan has focused on pests, diseases and fruit quality, and few studies have focused on increasing yields. Therefore, we aimed to contribute to a yield improvement in Japanese greenhouse cucumber production by considering environmental factors and training methods based on the yield components. Here, we discuss different training systems, pinching and lowering methods, and the effects of environmental factors such as temperature, humidity, CO 2 concentration, irrigation, and nutrition on yield and yield components. Moreover, this paper also proposes future areas of research for Japanese greenhouse cucumbers.

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Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter

Cited by 12 publications

References 71 publications

Heating Load of Residential Buildings Using Multiple Linear Regression Artificial Neural Network

Heating Load of Residential Buildings Using Multiple Linear Regression Artificial Neural Network

Estimation of Dry Matter Production and Yield Prediction in Greenhouse Cucumber without Destructive Measurements

A Review of Japanese Greenhouse Cucumber Research from the Perspective of Yield Components

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