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:10.20944/preprints202010.0429.v1

Cited by 2 publications

(1 citation statement)

References 31 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 cloudbased 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: Site Locations and Characteristicsmentioning

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: Site Locations and Characteristicsmentioning

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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Integrated Service Architecture to Promote the Circular Economy in Agriculture 4.0

Varella,

Oliveira Neto,

Stefani

et al. 2024

Sustainability

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Innovation has been the transforming tool of precision agriculture as a response to population growth and the demand for more food with quality, less waste, food security, and sustainable management of environmental resources. The challenges are to increase the productivity of cultivated areas, both for current and future areas, to manage the use of potable water, scarce in many regions, to keep the soil fertile, and to reduce waste through reuse, optimization, resource sharing, and operational and strategic management based on accurate information of planting, harvesting, and management of environmental conditions, which are also objectives of the Circular Economy. Therefore, using Industry 4.0 technologies in agriculture becomes fundamental to facing such challenges. This paper presents a systematic literature review on Industry 4.0 technologies adopted in agriculture for sustainable development, considering environmental, economic, and social benefits. The research pointed to the use of IoT in irrigation control systems by sending automatic commands, monitoring soil and weather conditions, in the use of machinery with some automation features and in cloud data storage systems, and with the use of Big Data analytical tools, with access by mobile devices, these uses contribute to operational and strategic decision making in the management of planting and harvesting. However, the literature review did not find a technological architecture for Integrated Services in Agriculture 4.0. Thus, this paper proposes a Service Architecture that enables the promotion of a Circular Economy in Agriculture 4.0. The contribution of this article to the theory is in the expansion of knowledge of the use of technologies in Agriculture 4.0. In terms of practice, this article provides an Integrated Service Architecture so that new products can be developed for Agriculture 4.0 and thus contribute to society in reducing food insecurity, generating environmental, economic, and social benefits, and promoting the Circular Economy in Agriculture 4.0.

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

Cited by 2 publications

References 31 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

Integrated Service Architecture to Promote the Circular Economy in Agriculture 4.0

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