Greenhouse environment modeling and simulation for microclimate control

Ma, Di; Carpenter, Neal R.; Maki, Hideki; Rehman, Tanzeel U.; Tuinstra, Mitchell R.; Jin, Jian Xun

doi:10.1016/j.compag.2019.04.013

Cited by 76 publications

(47 citation statements)

References 15 publications

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“…Distribución espacial de la temperatura y humedad relativa: En estructuras de agricultura protegida uno de los factores más estudiados es la distribución espacial del microclima. Es de vital importancia analizar variables como; la temperatura y la humedad relativa del ambiente interior, ya que estas variables afectan los procesos de transpiración y fotosíntesis de las plantas, procesos relacionados directamente con el crecimiento y desarrollo de los cultivos y por ende con la productividad final de los mismos (Ma et al, 2019).…”

Section: Discussionunclassified

Comportamiento microclimático diurno, en temporada seca, de tres estructuras para agricultura protegida en el trópico seco

Villagrán

Jaramillo

León-Pacheco

et al. 2020

URJ

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Introducción: En Costa Rica, el uso de estructuras agrícolas protegidas para la producción hortícola se ha incrementado en los últimos años, aunque existe poca información sobre su comportamiento microclimático. Objetivo: Nuestro objetivo fue evaluar el comportamiento de los patrones de flujo de aire y su efecto en la distribución espacial de la temperatura y la humedad relativa dentro de tres tipos de estructuras agrícolas protegidas diseñadas para sistemas agrícolas familiares. Métodos: El estudio se realizó en Guanacaste, Costa Rica, en la época seca de 2019, con un modelo de dinámica de fluidos computacional para el desarrollo de nueve simulaciones de estado estacionario. El modelo 3D se validó experimentalmente mediante la recopilación de información climática en cada uno de los prototipos de estructura. Resultados: Para las tres estructuras, los parámetros de bondad de ajuste entre datos medidos y simulados tuvieron error absoluto medio y error cuadrático medio de 0,21-0,44 °C y 1,65-3,40 % de humedad relativa. Los datos medidos y simulados tuvieron las mismas tendencias; los patrones de flujo de aire dentro de las estructuras dependieron en gran medida de la velocidad y dirección del viento externo. Las condiciones de temperatura y humedad relativa dentro de las tres estructuras tuvieron un comportamiento considerablemente homogéneo. Conclusiones: En las tres pequeñas estructuras agrícolas que probamos, usadas habitualmente en agricultura familiar, no se encontraron diferencias significativas del comportamiento térmico e higrométrico interno en estas condiciones de prueba.

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Section: Discussionunclassified

Comportamiento microclimático diurno, en temporada seca, de tres estructuras para agricultura protegida en el trópico seco

Villagrán

Jaramillo

León-Pacheco

et al. 2020

URJ

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“…Mainly, the temperature and radiation can be considered as two main factors when related to the climate. So, in order to succeed the plant relocation, it should be noted that a plant could spent the same time in each microclimate, more likely during the comparable growth [15]. This research is using the fuzzy logic to produce more dynamic plant watering duration where the temperature and the humidity are suitable with the desired plant.…”

Section: System Realizationmentioning

confidence: 99%

“…Structure of ANFISAs for the description of the ANFIS layer is as follows[15]: a. Layer 1 (fuzzification)…”

mentioning

confidence: 99%

Plant Seeds Growth Prediction on Greenhouse Using Adaptive Neuro Fuzzy Inference System (ANFIS) Method

2020

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Food security is a problem that every country had, especially for poor and developing countries. To improve the food security one of the solutions that can be applied is to collaborate technology and agriculture such as greenhouse. The technology that is applied to greenhouse can produce plants with good quality. Good quality plant can be predicted with prediction on the plant seeds in order to develop the plants production just as we expected. Prediction on plant seeds is using the adaptive neuro fuzzy inference system (ANFIS) model which is a combination of fuzzy and neural network. ANFIS will process the data with high complexity and it will provide the prediction result with high accuracy. Plant seeds prediction is using 65 data which divided into two data, specifically 50 training data and 15 testing data. The prediction provides accurate result and will generate 14/15 x 100% = 93.3333% precision with Mean Absolute Deviation (MAD) is 64.3391 from 15 prediction data about 4.2893, Mean Absolute Percentage Error (MAPE) is 5.3485 from 15 prediction data about 0.35657, Mean Square Deviation (MSD) is 9.159 from 15 prediction data about 0.6106.

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“…Plant imaging models have been developed to predict plants’ physiological features, such as the plant’s biomass [ 1 ], field yield [ 2 ], chlorophyll content [ 3 ], nitrogen content [ 3 ], relative water content [ 4 , 5 ], and disease symptoms [ 6 , 7 ]. In conventional plant phenotyping analyses, most studies rely on the averaged spectrum across all the segmented plant pixels of the whole plant [ 2 , 4 , 8 , 9 , 10 , 11 , 12 ]. Researchers firstly segment the target plant tissue from the image.…”

Section: Introductionmentioning

confidence: 99%

Stress Distribution Analysis on Hyperspectral Corn Leaf Images for Improved Phenotyping Quality

Wang

Zhang

et al. 2020

Sensors

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High-throughput imaging technologies have been developing rapidly for agricultural plant phenotyping purposes. With most of the current crop plant image processing algorithms, the plant canopy pixels are segmented from the images, and the averaged spectrum across the whole canopy is calculated in order to predict the plant’s physiological features. However, the nutrients and stress levels vary significantly across the canopy. For example, it is common to have several times of difference among Soil Plant Analysis Development (SPAD) chlorophyll meter readings of chlorophyll content at different positions on the same leaf. The current plant image processing algorithms cannot provide satisfactory plant measurement quality, as the averaged color cannot characterize the different leaf parts. Meanwhile, the nutrients and stress distribution patterns contain unique features which might provide valuable signals for phenotyping. There is great potential to develop a finer level of image processing algorithm which analyzes the nutrients and stress distributions across the leaf for improved quality of phenotyping measurements. In this paper, a new leaf image processing algorithm based on Random Forest and leaf region rescaling was developed in order to analyze the distribution patterns on the corn leaf. The normalized difference vegetation index (NDVI) was used as an example to demonstrate the improvements of the new algorithm in differentiating between different nitrogen stress levels. With the Random Forest method integrated into the algorithm, the distribution patterns along the corn leaf’s mid-rib direction were successfully modeled and utilized for improved phenotyping quality. The algorithm was tested in a field corn plant phenotyping assay with different genotypes and nitrogen treatments. Compared with the traditional image processing algorithms which average the NDVI (for example) throughout the whole leaf, the new algorithm more clearly differentiates the leaves from different nitrogen treatments and genotypes. We expect that, besides NDVI, the new distribution analysis algorithm could improve the quality of other plant feature measurements in similar ways.

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Greenhouse environment modeling and simulation for microclimate control

Cited by 76 publications

References 15 publications

Comportamiento microclimático diurno, en temporada seca, de tres estructuras para agricultura protegida en el trópico seco

Comportamiento microclimático diurno, en temporada seca, de tres estructuras para agricultura protegida en el trópico seco

Plant Seeds Growth Prediction on Greenhouse Using Adaptive Neuro Fuzzy Inference System (ANFIS) Method

Stress Distribution Analysis on Hyperspectral Corn Leaf Images for Improved Phenotyping Quality

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