Petri nets based procedure of hardware/software codesign of an urban agriculture monitoring system

Yazgac, Bilgi Gorkem; Durmuş, Halil; Kırcı, Mürvet; Güneş, Ece Olcay; Karli, Hakan Burak

doi:10.1109/agro-geoinformatics.2019.8820255

Cited by 1 publication

(1 citation statement)

References 4 publications

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“…The idea promotes the planting and cultivating of crops within cities [8,9]. Moreover, it also involves complex systems that consider indoor food production inside factories with an artificial environment suitable for cultivation [10], which applies the discipline of a controlled environment agriculture (CEA) [11,12].…”

Section: Introductionmentioning

confidence: 99%

Automation and Control for Adaptive Management System of Urban Agriculture Using Computational Intelligence

Dadios¹,

Vicerra²,

Lauguico³

et al. 2022

Automation and Control - Theories and Applications

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It has been predicted by the United Nations that the world population will increase to 9.8 billion in 2050. This causes agricultural development areas to be transformed into urban areas. This urbanization and increase in population density cause food insecurity. Urban agriculture using precision farming becomes a feasible solution to meet the growing demand for food and space. An adaptive management system (AMS) is necessary for such farm to provide an artificial environment suitable to produce cultivars effectively. This research proposes the development of a computational intelligence-based urban farm automation and control system utilizing machine learning and fuzzy logic system models. A quality assessment is employed for adjusting the environmental parameters with respect to the cultivars’ requirements. The system is composed of sensors for data acquisition and actuators for model-dictated responses to stimuli. Data logging was done wirelessly through a router that would collect and monitor data through a cloud-based dashboard. The model intended for training from the acquired data undergo statistical comparative analysis and least computational cost analysis to optimize the performance. The system performance was evaluated by monitoring the conditions of the sensors and actuators. Experiment results showed that the proposed system is accurate, robust, and reliable.

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