A dynamic model for temperature prediction in glass greenhouse

Nguyen-Xuan, Sam; Nhat, Nguyen Luong

doi:10.1109/nics48868.2019.9023791

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…Como parte de la agricultura inteligente el uso de los drones con cámaras es una alternativa viable para el monitoreo de cultivos [14]. Otra aplicación muy estudiada por este país es el uso de sistemas de sensores que recopilan los datos de humedad del suelo para optimizar el riego [27], [33], [40]. Por otro lado, Indonesia en el 2019 y Brasil en 2020 lograron la mayor cantidad de producción científica.…”

Section: Resultsunclassified

Proposed Open Systems Model and Intelligent Agriculture in Coffee Farms within the Harvesting Process

Ordonez-Avila¹,

Martínez-Rangel²

2022

Proceedings of the 2nd LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development (LEIRD 202

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The countries that produce the most coffee worldwide have an economy based on agribusiness and it is noteworthy that some of them apply various methods based on Industry 4.0 in their processes. However, in coffee production these technologies have a very limited application. The use of open systems makes it possible to talk about intelligent agriculture, which implements various technologies to improve some processes. This research work identifies the different technologies of Industry 4.0 that could be used in agriculture by the leading countries in coffee production as proposed in this research work. The development of the theoretical and methodological framework allows identifying three general steps in the management of information in this area: generation, grouping and visualization. After the theoretical review, the main trends of these countries with signs of intelligent agriculture are presented as results, given that they apply Industry 4.0 technologies. To visualize the impact that these technologies can have on coffee farms, the open systems model of Katz & Kan is proposed. The application of this model will allow the integration of Industry 4.0 within coffee farms. Finally, it is concluded that the most used technology is the internet of things and sensors, which many of the authors used together, while robotics is the least implemented. By implementing these technologies, the authors showed benefits in crop monitoring, irrigation systems and management systems.

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Section: Resultsunclassified

Proposed Open Systems Model and Intelligent Agriculture in Coffee Farms within the Harvesting Process

Ordonez-Avila¹,

Martínez-Rangel²

2022

Proceedings of the 2nd LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development (LEIRD 202

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“…After that, Equations ( 3)- (7) were applied, and the beam, diffuse, and reflected radiations were determined for all tilted surfaces of various orientations for the days and hours to be studied.…”

Section: The Principles Of Modeling the Greenhouse Heat Transfermentioning

confidence: 99%

“…Many models have been used over time to calculate the heat transfer of a greenhouse. Static models [4,5] have lower precision, but are easy to control, while dynamic models have higher precision, though it is more difficult to find calculation tools for them-withthese being usually specific to buildings [6][7][8][9]. TRNSYS, EnergyPlus, and other dynamic simulation software have also been used to model greenhouses.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Greenhouse Design with Miniature Models and IoT (Internet of Things) Technology—A Real-Time Monitoring Approach

Udrea,

Gheorghe,

Dogeanu

2024

Sensors

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The market for smart greenhouses has been valued at USD 1.77 billion in 2022 and is expected to grow to 3.39 billion by 2030. In order to make this more efficient, with the help of Internet of Things (IoT) technology, it is desired to eliminate the problem of traditional agriculture, which has poor monitoring and accuracy control of the parameters of a culture. Climate control decisions in a greenhouse are made based on parameter monitoring systems, which can be remotely controlled. Instead of this adjustment of the measured parameters, it would be preferable from the point of view of energy consumption that they should be calculated at optimal values from the design phase of the greenhouse. For this reason, it would be better to perform an energy simulation of the greenhouse first. For the study carried out in this work, a small greenhouse (mini-greenhouse) was built. It was equipped with an IoT sensor system, which measured indoor climate parameters and could send data to the cloud for future recording and processing. A simplified mathematical model of the heat balance was established, and the measured internal parameters of the mini-greenhouse were compared with those obtained from the simulation. After validating the mathematical model of the mini-greenhouse, this paper aimed to find the optimal position for placing a normal-sized greenhouse. For this, several possible locations and orientations of the greenhouse were compared by running the mathematical model, with which the most unfavorable positions could be eliminated. Then, some considerably cheaper “mini-greenhouses” were made and placed in the locations with the desired orientations. Using sensor systems and technologies similar to those presented in this work, the parameters from all mini-greenhouses can be monitored in real time. This real-time monitoring allows for the simultaneous analysis of all greenhouses, without the disadvantages of data collection directly in the field, with all data being recorded in the cloud and other IoT-specific advantages being made use of. In the end, we can choose the optimal solution for the location of a real-size greenhouse.

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“…Simple regression algorithms -include linear, Extreme Gradient Boosting (XGBoost), and Stacked regressors-has been utelised to estimate the crop yield [228][229][230]. Decision tree, K-nearest neighbors (KNN), random forest, Naïve Bayes Classifier, and support vector machine (SVM) are simple classifiers used to predict the most suitable crop for a region [210,[231][232][233][234][235][236]. The simplicity of those algorithms enable presenting relationship between data with out demanding extensive computation power, hence can be employed at the edge.…”

Section: E Data Analysis 1) Machine Learning Techniquesmentioning

confidence: 99%

Technologies Driving the Shift to Smart Farming: A Review

Elbeheiry

Balog

2023

IEEE Sensors J.

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As today's agriculture industry is facing numerous challenges, including climate changes, encroachment of the urban environment and lack of qualified farmers, there is a need for new practices to ensure sustainable agriculture and food supply. Consequently, there is an emphasis on upgrading the farming practices by shifting towards Smart Farming (SF) -utilizing advanced information and communication technologies to improve the quantity and quality of the crop with minimal labor interference. SF has gained lots of interest in recent years utilizing a variety of technological innovations in the field, which imposes a challenge on farmers and technology integrators to identify the suitable technologies and best practices for a particular application. This paper provides a survey of the most recent SF scientific literature to identify common practices toward technology integration, challenges, and solutions. The survey was conducted on 588 papers published on the IEEE database following Cochrane methods to ensure appropriate analysis and interpretation of results. The papers' contributions were analyzed to identify necessary technologies that constitute SF, and consequently, research themes were identified. The identified themes are sensors, communication, big data, actuators and machines, and data analysis. Besides presenting an in-depth analysis of each identified theme, the paper discusses integrating more than one technology in systems to achieve independency. The most common SF systems are remote monitoring, autonomous, and intelligent decision-making systems.

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A dynamic model for temperature prediction in glass greenhouse

Cited by 4 publications

References 6 publications

Proposed Open Systems Model and Intelligent Agriculture in Coffee Farms within the Harvesting Process

Proposed Open Systems Model and Intelligent Agriculture in Coffee Farms within the Harvesting Process

Optimizing Greenhouse Design with Miniature Models and IoT (Internet of Things) Technology—A Real-Time Monitoring Approach

Technologies Driving the Shift to Smart Farming: A Review

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