IRRISENS: An IoT Platform Based on Microservices Applied in Commercial-Scale Crops Working in a Multi-Cloud Environment

Maia, Rodrigo Filev; Ballester, Carlos; Baniya, Arbind Agrahari; Hornbuckle, John

doi:10.3390/s20247163

Cited by 14 publications

(8 citation statements)

References 45 publications

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“…In all sites, cotton was furrow irrigated employing a bank-less channel irrigation system ( Grabham, 2012 ). Irrigation was scheduled based on the farm consultants’ decision except for site A during the 2020/21 season, where irrigation was triggered based on soil matric potential thresholds and recommendations obtained from the cloud-based IRRISENS platform ( Filev Maia et al, 2020 ). At this site, the grower was notified by text message when soil matric potential at 0.20 m depth at any of the three bays monitored was lower than −40 kPa to be able to order water to the irrigation company and organize the irrigation event 48 h in advance.…”

Section: Methodsmentioning

confidence: 99%

Machine learning approach to estimate soil matric potential in the plant root zone based on remote sensing data

2022

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There is an increasing interest in using the Internet of Things (IoT) in the agriculture sector to acquire soil- and crop-related parameters that provide helpful information to manage farms more efficiently. One example of this technology is using IoT soil moisture sensors for scheduling irrigation. Soil moisture sensors are usually deployed in nodes. A more significant number of sensors/nodes is recommended in larger fields, such as those found in broadacre agriculture, to better account for soil heterogeneity. However, this comes at a higher and often limiting cost for farmers (purchase, labour costs from installation and removal, and maintenance). Methodologies that enable maintaining the monitoring capability/intensity with a reduced number of in-field sensors would be valuable for the sector and of great interest. In this study, sensor data analysis conducted across two irrigation seasons in three cotton fields from two cotton-growing areas of Australia, identified a relationship between soil matric potential and cumulative satellite-derived crop evapotranspiration (ETcn) between irrigation events. A second-degree function represents this relationship, which is affected by the crop development stage, rainfall, irrigation events and the transition between saturated and non-saturated soil. Two machine learning models [a Dense Multilayer Perceptron (DMP) and Support Vector Regression (SVR) algorithms] were studied to explore these second-degree function properties and assess whether the models were capable of learning the pattern of the soil matric potential-ETcn relation to estimate soil moisture from satellite-derived ETc measurements. The algorithms performance evaluation in predicting soil matric potential applied the k-fold method in each farm individually and combining data from all fields and seasons. The latter approach made it possible to avoid the influence of farm consultants’ decisions regarding when to irrigate the crop in the training process. Both algorithms accurately estimated soil matric potential for individual (up to 90% of predicted values within ±10 kPa) and combined datasets (73% of predicted values within ±10 kPa). The technique presented here can accurately monitor soil matric potential in the root zone of cotton plants with reduced in-field sensor equipment and offers promising applications for its use in irrigation-decision systems.

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Section: Methodsmentioning

confidence: 99%

Machine learning approach to estimate soil matric potential in the plant root zone based on remote sensing data

2022

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“…The communication protocol is analyzed and studied in the literature [ 15 ]. Based on the specific needs, combined with artificial intelligence technology to solve the problems of cross-platform difficulty and fierce competition, an extremely reliable, high-performance, and scalable intelligent internet of things cloud platform is designed [ 16 ]. The platform can withstand massive concurrency, so as to realize cross-platform (high portability) operation, and can perform many functions such as predicting user behavior and intelligent device operation [ 17 ].…”

Section: Related Workmentioning

confidence: 99%

System Design and Simulation for Square Dance Movement Monitoring Based on Machine Learning

Lei

2022

Computational Intelligence and Neuroscience

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Since the reform and opening, China’s economy has grown rapidly, and people’s living standards have improved significantly. As one of the most effective ways to implement national fitness, square dance has gradually become the main lifestyle of urban communities, an important part of China’s sports construction, and an important indicator to reflect the fitness of the masses and the construction of a well-off society in an all-round way. On the other hand, with the rapid development of internet of things technology, many people can use intelligent bracelets based on machine learning technology to realize motion detection. This technology is also applicable in square dance, which is of great significance to exercise and protect health. This paper first reviews the research status of the internet of things communication protocol and cloud platform, then introduces and analyzes the MQTT communication protocol and Netty high-performance network framework, and studies the integration technology of the internet of things and machine learning. Then, according to the characteristics of the internet of things, a scheme to realize data preprocessing is proposed. The value to be completed is calculated based on the correlation of other attributes corresponding to the k-nearest neighbor model (KNN) and the regression model. Finally, the machine learning algorithm is used to train the results of the three models to obtain the final filling value. The whole scheme design allows the machine learning algorithm to obtain relatively high-quality data in the internal environment. This paper designs a sports monitoring data system for square dance by combining machine learning and internet of things technology, so as to promote national fitness.

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“…Integral to the success of the IoT is the extent to which it leverages applications based on algorithms, enhanced automation and greater trust through authenticated and encrypted data. The distributed nature of the problem and the opportunity to engage multiple stakeholders can add value across farming systems and built around small scale connected and shared services, IT developers typically refer to these as micro-services (Maia et al, 2020). The foundation for these services requires an interconnected IoT framework that is divided not by stakeholder ownership but by what each service delivers (Devi et al, 2019;Santana et al, 2021).…”

Section: Technology Developments Required To Deliver Fenceless Livestock Farmingmentioning

confidence: 99%

“…Animal location information can be used to determine livestock movement and livestock landscape preferences (Swain et al, 2011). Integrated micro-services will use baseline generic algorithms but these will be refined within the system to take account of individual animal parameters (Devi et al, 2019;Taneja et al, 2019;Maia et al, 2020;Santana et al, 2021). In addition to livestock monitoring external data will be added to the system to provide a further context for management decisions.…”

Section: The Application Of Fenceless Farming-defining a New Paradigmmentioning

confidence: 99%

“…As stated earlier the application of fenceless farming systems will require sensor technologies that can continuously monitor the state of the animal. These sensors will also be able to deliver a range of micro-services via communication infrastructure that can be used to manage the complexity of the system (Taneja et al, 2019;Maia et al, 2020). These services will be built on insight related to animal health and welfare, genetics and environmental (including but not limited to the feed-base) drivers.…”

Section: Renewing a Social Contract-fenceless Farming Putting Community At The Heart Of Food Securitymentioning

confidence: 99%

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Back to Nature With Fenceless Farms—Technology Opportunities to Reconnect People and Food

Swain

Charters

2021

Front. Sustain. Food Syst.

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The development and application of the fence was one of the earliest forms of agricultural technology in action. Managing the supply of animal protein required hunter gatherer communities to be able to domesticate and contain wild animals. Over the ages the fence has become ingrained in the very fabric of society and created a culture of control and ownership. Garett Hardin's article titled “The Tragedy of the Commons” suggested that shared land, typified by access to a fenceless common resource, was doomed to failure due to a human instinct for mistrust and exploitation. Perhaps the fence has created an ingrained societal cultural response. While natural ecosystems do have physical boundaries, these are based on natural environmental zones. Landscapes are more porous and resilience is built up through animal's being able to respond to dynamic changes. This paper explores the opportunity for remote monitoring technologies to create open fenceless landscapes and how this might be integrated into the growing need for humans to access animal protein.

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IRRISENS: An IoT Platform Based on Microservices Applied in Commercial-Scale Crops Working in a Multi-Cloud Environment

Cited by 14 publications

References 45 publications

Machine learning approach to estimate soil matric potential in the plant root zone based on remote sensing data

Machine learning approach to estimate soil matric potential in the plant root zone based on remote sensing data

System Design and Simulation for Square Dance Movement Monitoring Based on Machine Learning

Back to Nature With Fenceless Farms—Technology Opportunities to Reconnect People and Food

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