A smart hydroponics farming system using exact inference in Bayesian network

Alipio, Melchizedek I.; Cruz, Allen Earl M. Dela; Doria, Jess David A.; Fruto, Rowena Maria S.

doi:10.1109/gcce.2017.8229470

Cited by 57 publications

(11 citation statements)

References 8 publications

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“…Alipio et al [ 22 ] developed an intelligent hydroponics system used in automation of the crop growth process using inference through Bayesian networks. Huong et al [ 23 ] proposed a generic model using the Markov decision model to create automatic and accurate irrigation.…”

Section: Related Workmentioning

confidence: 99%

“…The type of cultivation is another differential between IndoorPlant and other studies: the proposed model supports different types of cultivation, such as hydroponics, fertigation (semi-hydroponics), and soil cultivation. The other studies focused on only one type of cultivation: Goap et al [ 20 ] applied their system in soil agriculture, Alipio et al [ 22 ] and Sisyanto et al [ 8 ] in hydroponics.…”

Section: Related Workmentioning

confidence: 99%

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IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories

Martini

Helfer

Barbosa

et al. 2021

Sensors

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The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R2, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio; 0.95 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R2), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories

Martini

Helfer

Barbosa

et al. 2021

Sensors

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“…A SmartAgriFood conceptual architecture is proposed in Kaloxylos et al [22], while the authors of [23] introduce internet applications in the agri-food domain; Poppe in [24] proposes the analysis to both the scope and the organization of farm production regulations. Garba [25] develops smart water-sharing methods in semi-arid regions; Hlaing et al [26] introduce plant diseases recognition using statistical models; and, moreover, in Alipio et al [27], there are smart hydroponics systems that exploit inference in Bayesian networks. Marimuthu et al [28] propose and design a Persuasive Technology to encourage smart farming, while also exploiting historical time-series for production quality assurance [29], because nowadays consumers are concerned about food safety assurance related to health and well-being.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

2018

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This work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appropriate ways to exploit data that are continuously recorded and made available can be the right choice to achieve these goals. The agricultural field is only apparently refractory to the digital technology and the “smart farm” model is increasingly widespread by exploiting the Internet of Things (IoT) paradigm applied to environmental and historical information through time-series. The focus of this study is the design and deployment of practical tasks, ranging from crop harvest forecasting to missing or wrong sensors data reconstruction, exploiting and comparing various machine learning techniques to suggest toward which direction to employ efforts and investments. The results show how there are ample margins for innovation while supporting requests and needs coming from companies that wish to employ a sustainable and optimized agriculture industrial business, investing not only in technology, but also in the knowledge and in skilled workforce required to take the best out of it.

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“…In a hydroponics room, the vegetables are grown in nutrient solutions (see Figure 2). The supporting IoT system must thus monitor and control various physical variables, e.g., temperature, humidity, CO2, air flow, lighting, and fertilizer concentration, balance, and pH [36], [37].…”

Section: The Lattice Frameworkmentioning

confidence: 99%

LATTICE: A Framework for Optimizing IoT System Configurations at the Edge

Issarny

Billet

Bouloukakis

et al. 2019

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

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The Internet of Things is expected to contribute to a "smarter world" by connecting the physical to the virtual, i.e., enabling advanced knowledge engineering over the big data gathered about the physical world. However, such a promise comes along with high resource consumption, spanning the network, storage and computational resources, not to mention possible security and privacy threats. As a result, it tends to be admitted that the IoT smartness will not be accommodated at scale by a centralized cloud-based approach. Instead, the deployment of IoT systems needs to leverage a highly distributed system architecture, which optimizes the distribution of the computation-from the edge to the cloud-according to the unique business requirements in terms of financial cost, latency, availability, etc. Toward that goal, this paper introduces the LATTICE framework, which aims at taming the complexity of configuring edge-based IoT systems. LATTICE builds upon ontologies that have proven useful to characterize the constituents of IoT systems in the required domain-specific way. However, LATTICE also revisits the exploitation of ontologies-i.e., the formal description of the real world, spanning the physical and cyber entities-across the development life-cycle of the IoT systems. As a first evidence, this paper introduces an automated approach to the optimization of IoT system configurations at the edge, provided the ontological description of the target IoT system.

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A smart hydroponics farming system using exact inference in Bayesian network

Cited by 57 publications

References 8 publications

IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories

IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories

Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

LATTICE: A Framework for Optimizing IoT System Configurations at the Edge

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