scholarly journals Design of a Data Management Reference Architecture for Sustainable Agriculture

2021 ◽  
Vol 13 (13) ◽  
pp. 7309
Author(s):  
Görkem Giray ◽  
Cagatay Catal
Keyword(s):  
Sustainable Agriculture ◽  
Data Management ◽  
Precision Agriculture ◽  
Domain Modeling ◽  
Reference Architecture ◽  
Design Data ◽  
High Productivity ◽  
Smart Farming ◽  
Operational Systems ◽  
Efficient Data

Effective and efficient data management is crucial for smart farming and precision agriculture. To realize operational efficiency, full automation, and high productivity in agricultural systems, different kinds of data are collected from operational systems using different sensors, stored in different systems, and processed using advanced techniques, such as machine learning and deep learning. Due to the complexity of data management operations, a data management reference architecture is required. While there are different initiatives to design data management reference architectures, a data management reference architecture for sustainable agriculture is missing. In this study, we follow domain scoping, domain modeling, and reference architecture design stages to design the reference architecture for sustainable agriculture. Four case studies were performed to demonstrate the applicability of the reference architecture. This study shows that the proposed data management reference architecture is practical and effective for sustainable agriculture.

Climate Smart Sustainable Agriculture

2021 ◽  
pp. 46-59
Author(s):  
Luis Loures ◽  
Paulo Ferreira ◽  
Ana Loures ◽  
Vera Barradas
Keyword(s):  
Climate Change ◽  
Sustainable Agriculture ◽  
Environmental Health ◽  
Precision Agriculture ◽  
Agricultural Sector ◽  
Conservation Agriculture ◽  
Agricultural Ecosystems ◽  
Smart Farming ◽  
The World ◽  
Careful Management

Careful management of agricultural ecosystems is considered a vital procedure to ensure both environmental health and the sustainability of this sector, particularly when, besides all the argumentative used by farmers, there are no globally accepted sustainable management solutions for agriculture. This scenario poses several challenges for the agricultural sector all over the world, especially on an increasing climate change situation, in which extreme weather phenomena tend to be gradually more severe, as is the case of floods and draughts. Still, considering that the last decades were marked by great developments in agricultural management systems as is the case of precision agriculture, hi-tech-agriculture, organic farming, conservation agriculture, sustainable agriculture, smart farming, among others, it is crucial to assess specific case studies, in which the application of predetermined sustainable farming principles and/or procedures contributed to increase their resilience to climate change on a sustainable manner.

scholarly journals Precision Agriculture: A Remote Sensing Monitoring System Architecture †

Information ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 348 ◽  
Author(s):  
Anna Triantafyllou ◽  
Panagiotis Sarigiannidis ◽  
Stamatia Bibi
Keyword(s):  
Precision Agriculture ◽  
Agricultural Practices ◽  
Monitoring Systems ◽  
Reference Architecture ◽  
Application Layer ◽  
Sensor Layer ◽  
Architecture Model ◽  
Real World Application ◽  
Smart Farming ◽  
The Internet Of Things

Smart Farming is a development that emphasizes on the use of modern technologies in the cyber-physical field management cycle. Technologies such as the Internet of Things (IoT) and Cloud Computing have accelerated the digital transformation of the conventional agricultural practices promising increased production rate and product quality. The adoption of smart farming though is hampered because of the lack of models providing guidance to practitioners regarding the necessary components that constitute IoT-based monitoring systems. To guide the process of designing and implementing Smart farming monitoring systems, in this paper we propose a generic reference architecture model, taking also into consideration a very important non-functional requirement, the energy consumption restriction. Moreover, we present and discuss the technologies that incorporate the seven layers of the architecture model that are the Sensor Layer, the Link Layer, the Encapsulation Layer, the Middleware Layer, the Configuration Layer, the Management Layer and the Application Layer. Furthermore, the proposed Reference Architecture model is exemplified in a real-world application for surveying Saffron agriculture in Kozani, Greece.

Design data management in a distributed hardware environment

2002 ◽  
Author(s):  
G.W. Sloof ◽  
P. Bingley ◽  
P. Dewilde ◽  
T.G.R. van Leuken ◽  
P. van der Wolf
Keyword(s):  
Data Management ◽  
Design Data

A survey on energy-efficient data management

2011 ◽  
Vol 40 (2) ◽  
pp. 17-23 ◽  
Author(s):  
Jun Wang ◽  
Ling Feng ◽  
Wenwei Xue ◽  
Zhanjiang Song
Keyword(s):  
Data Management ◽  
Energy Efficient ◽  
Efficient Data

scholarly journals A Case Study about the Use of Precision Agriculture Technology Applied to a Zn Biofortification Workflow for Grapevine Vitis vinifera cv Moscatel

2021 ◽  
Vol 3 (1) ◽  
pp. 2
Author(s):  
Diana Daccak ◽  
Inês Carmo Luís ◽  
Ana Coelho Marques ◽  
Ana Rita F. Coelho ◽  
Cláudia Campos Pessoa ◽  
...  
Keyword(s):  
Vitis Vinifera ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Foliar Application ◽  
Fold Increase ◽  
Production Cycle ◽  
Smart Farming ◽  
Reduced Surface

As the human population is growing worldwide, the food demand is sharply increasing. Following this assumption, strategies to enhance the food production are being explored, namely, smart farming, for monitoring crops during the production cycle. In this study, a vineyard of Vitis vinifera cv. Moscatel located in Palmela (N 38°35′47.113′′ O 8°40′46.651) was submitted to a Zn biofortification workflow, through foliar application of zinc oxide (ZnO) or zinc sulfate (ZnSO4) (at a concentration of 60% and 90%—900 g·ha−1 and 1350 g·ha−1, respectively). The field morphology and vigor of the vineyard was performed through Unmanned Aerial Vehicles (UAVs) images (assessed with altimetric measurement sensors), synchronized by GPS. Drainage capacity and slopes showed one-third of the field with reduced surface drainage and a maximum variation of 0.80 m between the extremes (almost flat), respectively. The NDVI (Normalized Difference Vegetation Index) values reflected a greater vigor in treated grapes with treatment SZn90 showing a higher value. These data were interpolated with mineral content, monitored with atomic absorption analysis (showing a 1.3-fold increase for the biofortification index). It was concluded that the used technologies furnishes specific target information in real time about the crops production.

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