MicroCEA: Developing a Personal Urban Smart Farming Device

Stevens, Joseph D; Shaikh, Talal

doi:10.1109/icsgsc.2018.8541311

Cited by 18 publications

(12 citation statements)

References 6 publications

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“…The system complexity and expertise required for the adoption of hydroponics are a barrier to its wider adoption and use in an appliancelike manner [1][2][3][4]. To simplify the management of the system complexities, micro indoor smart hydroponics (MISH) systems use Internet of Things (IoT) technologies [4,11,12]. IoT technologies enable sensors and actuators to connect to a networked computing system, allowing software systems to monitor and assist with hydroponic management.…”

Section: Introductionmentioning

confidence: 99%

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Development and Testing of an IoT Spectroscopic Nutrient Monitoring System for Use in Micro Indoor Smart Hydroponics

et al. 2023

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Nutrient monitoring in Micro Indoor Smart Hydroponics (MISH) relies on measuring electrical conductivity or total dissolved solids to determine the amount of nutrients in a hydroponic solution. Neither method can distinguish concentrations of individual nutrients. This study presents the development and testing of a novel spectroscopic sensor system to monitor nitrogen changes in nutrient solutions for MISH systems. The design phase determined that using an inexpensive AS7265x Internet of Thing (IoT) sensor in a transflective spectroscopic application could effectively detect small fluctuations in nitrogen concentraation. Next, a novel transflective sensor apparatus was designed and constructed for use in a MISH system experiment, growing lettuce over 30 days. Two solution tanks of different sizes, 80 L and 40 L, were used in the deployment of the system. Samples from each tank were analyzed for nitrogen concentration in a laboratory, and multilinear regression was used to predict the nitrogen concentrations using the AS7265x 18 spectral channels recorded in the sensor system. Significant results were found for both tanks with an R2 of 0.904 and 0.911 for the 80 and 40 L tanks, respectively. However, while the use of all wavelengths produced an accurate model, none of the individual wavelengths were indicative on their own. These findings indicate that the novel system presented in this study successfully and accurately monitors changes in nitrogen concentrations for MISH systems, using low cost IoT sensors.

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Section: Introductionmentioning

confidence: 99%

“…The proposed system, hereby referred to as NutriSpec, was built with a commodity IoT spectroscopic sensor and hardware. The NutriSpec system uses IoT hardware and software architectures that are well-established for MISH systems [4,12,32,33]. The system was demonstrated in a real-world setting via a 28-day lettuce-growing experiment.…”

Section: Introductionmentioning

confidence: 99%

Development and Testing of an IoT Spectroscopic Nutrient Monitoring System for Use in Micro Indoor Smart Hydroponics

et al. 2023

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“…Similar to our approach, Stevens and Shaikh [10] created their MicroCEA as an adapted version of the MIT Personal Food Computer, and Boanos et al [11] also made several changes to the MIT Personal Food Computer to create a cost-effective automated food computer to overcome some of the shortcomings of the MIT Personal Food Computer. Even if little data is provided, Boanos et al [11] showed indications of differences in growth conditions between blue and red light conditions on cilantro grown inside such a food computer.…”

Section: Related Workmentioning

confidence: 99%

GrowBot: An Educational Robotic System for Growing Food

et al. 2022

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We present the GrowBot as an educational robotic system to facilitate hands-on experimentation with the control of environmental conditions for food plant growth. The GrowBot is a tabletop-sized greenhouse automated with sensors and actuators to become a robotic system for the control of plant’s growth. The GrowBot includes sensors for humidity, CO2, temperature, water level, RGB camera images, and actuators to control the grow conditions, including full spectrum lights, IR lights, and UV lights, nutrients pump, water pump, air pump, air change pump, and fan. Inspired by educational robotics, we developed user-friendly graphical programming of the GrowBots on several means: a touch display, a micro:bit, and a remote webserver interface. This allows school pupils to easily program the GrowBots to different growth conditions for the natural plants in terms of temperature, humidity, day light cycle, wavelength of LED light, nutrient rate, etc. The GrowBot system also allows the user to monitor the environmental conditions, such as CO2 monitoring for photosynthesis understanding, on both the touch display and the remote web–interface. An experiment with nine GrowBots shows that the different parameters can be controlled, that this can control the growth of the food plants, and that control to make an environmental condition with blue light results in higher and larger plants than red light. Further, the pilot experimentation in school settings indicates that the comprehensive system design method results in a deployable system, which can become well adopted in the educational domain.

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“…They focus on collecting parameters related to the dairy farms and the weather. Stevens et al [ 21 ] present MICROCEA, an architecture developed to automate the growth of plants in urban indoor residential areas. The purpose is to monitor sensor data, such as light, temperature, and humidity, and let users program events to automate the process.…”

Section: Bibliography Reviewmentioning

confidence: 99%

An Internet of Things Platform Based on Microservices and Cloud Paradigms for Livestock

Mateo

Pagès‐Bernaus

Plà-Aragonés

et al. 2021

Sensors

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With the growing adoption of the Internet of Things (IoT) technology in the agricultural sector, smart devices are becoming more prevalent. The availability of new, timely, and precise data offers a great opportunity to develop advanced analytical models. Therefore, the platform used to deliver new developments to the final user is a key enabler for adopting IoT technology. This work presents a generic design of a software platform based on the cloud and implemented using microservices to facilitate the use of predictive or prescriptive analytics under different IoT scenarios. Several technologies are combined to comply with the essential features—scalability, portability, interoperability, and usability—that the platform must consider to assist decision-making in agricultural 4.0 contexts. The platform is prepared to integrate new sensor devices, perform data operations, integrate several data sources, transfer complex statistical model developments seamlessly, and provide a user-friendly graphical interface. The proposed software architecture is implemented with open-source technologies and validated in a smart farming scenario. The growth of a batch of pigs at the fattening stage is estimated from the data provided by a level sensor installed in the silo that stores the feed from which the animals are fed. With this application, we demonstrate how farmers can monitor the weight distribution and receive alarms when high deviations happen.

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MicroCEA: Developing a Personal Urban Smart Farming Device

Cited by 18 publications

References 6 publications

Development and Testing of an IoT Spectroscopic Nutrient Monitoring System for Use in Micro Indoor Smart Hydroponics

Development and Testing of an IoT Spectroscopic Nutrient Monitoring System for Use in Micro Indoor Smart Hydroponics

GrowBot: An Educational Robotic System for Growing Food

An Internet of Things Platform Based on Microservices and Cloud Paradigms for Livestock

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