Environmental Control in Greenhouse Based on Phytomonitoring - Leaf Temperature as a Factor Controlling Greenhouse Environments

Roh, Mi-Young; Nam, Y.I.; Cho, M.W.; Yu, In Ho; Choi, Gyeong Lee; Kim, T.Y.

doi:10.17660/actahortic.2007.761.7

Cited by 6 publications

(10 citation statements)

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“…The lower minimum temperature levels reduced or delayed the fruiting process of tomato (Romano and Leonard, 1994) and it might create problems for the vegetative and generative growth (Abak and Guler, 1994). The leaf temperature of 13ºC may cause the reduction in physiological activity of cucumber plants due to low temperature stress (Roh et al, 2007) and it prevent fertilization in tomato by reducing pollen count and viability (Ercan and Vural, 1994) and therefore, decreases fruit set and the yield (Rylski et al, 1994).…”

Section: Yield Contributing Characters and Yieldmentioning

confidence: 99%

Phenological Development and Production Potentials of Tomato under Polyhouse Climate

Parvej

Khan²,

Awal

2010

J Agric Sciences

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Section: Yield Contributing Characters and Yieldmentioning

confidence: 99%

Phenological Development and Production Potentials of Tomato under Polyhouse Climate

Parvej

Khan²,

Awal

2010

J Agric Sciences

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“…El mejoramiento del control ambiental dentro del invernadero, también requiere identificar la influencia de factores climáticos en la fisiología y desarrollo de las plantas cultivadas, puesto que la respuesta de la planta es la parte del proceso más importante en los sistemas de producción (Tantau, 1992;Roh et al, 2007). Dentro de los aspectos fisiológicos más relevantes que deben ser considerados en un sistema de producción en invernadero, se encuentra el entendimiento del proceso de fotosíntesis y los elementos que influyen en ella positiva o negativamente (Evans y Loreto, 2000).…”

Section: Introductionunclassified

“…Improved environmental control inside the greenhouse also requires to identifying the influence of the climatic factors on the physiology and development of the crop, since the response of the plant is the most important process in the production systems (Tantau, 1992;Roh et al, 2007). Among the most relevant physiological aspects that must be considered in a greenhouse production system, is to understand the process of photosynthesis and the elements that influence it positively or negatively (Evans and Loreto, 2000).…”

Section: Introductionmentioning

confidence: 99%

“…The real-time monitoring and continuous plant physiological response to changes in the environment, provides important information to control the greenhouse microclimate more accurately, likewise the use of this technology is very successful as a tool to research (Ton et al, 2001). However, adapting a phyto-monitoring technology to conventional systems in greenhouse production is almost impossible due to the high equipment costs (Roh et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…El monitoreo en tiempo real y continuo de la respuesta fisiológica de la planta a los cambios del ambiente, proporciona información muy importante para controlar el microclima del invernadero de forma precisa; así mismo, el uso de esta tecnología es muy acertado como una herramienta para hacer investigación (Ton et al, 2001). Sin embargo, adaptar una tecnología de fitomonitoreo a sistemas de producción convencional bajo invernadero resulta casi imposible por los elevados costos de los equipos (Roh et al, 2007).…”

Section: Introductionunclassified

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Estimación de la fotosíntesis foliar en jitomate bajo invernadero mediante redes neuronales artificiales

Sállago

Cruz

Garcia

2018

Remexca

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Dentro de la teoría de identificación de sistemas, los modelos de redes neuronales artificiales (ANN) artificial neural networks por sus siglas en inglés, han mostrado gran capacidad para encontrar patrones de relación entre variables de procesos altamente no lineales, así como resolver la limitante de la regresión no lineal, donde no es posible usar variables correlacionadas como entradas. El objetivo de la presente investigación fue modelar la tasa de fotosíntesis foliar de plantas de jitomate, cultivadas bajo invernadero, mediante redes neuronales artificiales, empleando como variables de entrada: temperatura, humedad relativa, déficit de presión de vapor y concentración de dióxido de carbono (CO2) del aire, así como radiación fotosintéticamente activa. El experimento se desarrolló durante 2009 en un invernadero experimental de la Universidad Autónoma de Querétaro, México. Se usó el equipo de fitomonitoreo PTM-48M (Daletown Company, Ltd), para registrar el intercambio de CO2 de las hojas, así como las variables meteorológicas. Para eliminar ruidos de los sensores en las mediciones se aplicó el filtro Savitzky-Golay. Se evaluaron diferentes configuraciones para redes de retropropagación, siendo la de 4 capas con 10 neuronas en la primera capa oculta, 15 en la segunda y 10 más en la tercera, la que generó los mejores índices estadísticos sobre datos de prueba: coeficiente de determinación, R2= 0.9756 y cuadrado medio del error, CME= 0.8532. Tomando las predicciones de la mejor ANN, se realizó una optimización estática, relacionando dos variables climáticas con la tasa de fotosíntesis, mediante gráficas en tercera dimensión, a fin de mostrar estrategias para maximizar la tasa de fotosíntesis.

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A Review on Leaf Temperature Sensor: Measurement Methods and Application

Lü

Wang

Zhang

et al. 2016

Computer and Computing Technologies in Agriculture IX

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Leaf temperature is the guarantee for the plant to carry out the life activities and closely related to plants' healthy growth and crops' planting management. The accurate measurement of leaf temperature is significant to understand the physiological condition, guide farmland irrigation, select variety and forecast production, etc. The development of plant leaf temperature measurement and requirements of application in recent years at home and abroad were briefly summarized and reviewed in the paper. Firstly, the status of application research and achievements of leaf temperature were introduced from the methods of measurement and scientific experiments. Then it analyzed and compared the principle, advantages and disadvantages and measurement of several common methods in detail including thermal resistance measurement, thermocouple measurement, infrared temperature measurement, infrared thermal imaging measurement and the leaf temperature model. At last, some problems urgently needed to be solved and the development direction of the field were presented, which could provide a reference for the further study of the leaf temperature sensor.

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Environmental Control in Greenhouse Based on Phytomonitoring - Leaf Temperature as a Factor Controlling Greenhouse Environments

Cited by 6 publications

References 0 publications

Phenological Development and Production Potentials of Tomato under Polyhouse Climate

Phenological Development and Production Potentials of Tomato under Polyhouse Climate

Estimación de la fotosíntesis foliar en jitomate bajo invernadero mediante redes neuronales artificiales

A Review on Leaf Temperature Sensor: Measurement Methods and Application

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