IOT based crop-field monitoring and irrigation automation

Rajalakshmi, P.; Mahalakshmi, S.

doi:10.1109/isco.2016.7726900

Cited by 176 publications

(56 citation statements)

References 11 publications

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“…Authors in [15] for example use a GPS based remotely controlled robot to monitor real time field data, and perform irrigation tasks respectively, like watering, securing or fertilizing the field. Likewise, in [16], the irrigation system collects soil, moisture, temperature, humidity and light measures through sensors, and transfers them to a web server, which enables the corresponding actuators according to its preset optimal measures. Various other works tackle the problem in similar fashion, with slightly different technologies.…”

Section: A Overview Of Smart Irrigation Systemsmentioning

confidence: 99%

A Constraint-based Approach to Deal with Self-Adaptation: The Case of Smart Irrigation Systems

Achtaich¹,

Souissi²,

Salinesi³

et al. 2019

IJACSA

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Smart irrigation is a specific application of the IoT, where devices composed of sensors and actuators, collect environmental data, like soil humidity, air temperature and brightness, in order to lunch or plan irrigation cycles. These systems function according to a configuration that dictates the way in which every component should operate. Static configurations are limited, as they only represent a set of fixed requirements. However, in domains such as the IoT, technology is continuously evolving, and various users, sometimes with various needs, interact with the system. This leads to dynamic requirements, which are fulfilled by dynamic configurations. This purpose uses the case of an irrigation system to illustrate such requirements, and proposes a constrained-based approach to design self-adaptive smart irrigation systems.

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Section: A Overview Of Smart Irrigation Systemsmentioning

confidence: 99%

A Constraint-based Approach to Deal with Self-Adaptation: The Case of Smart Irrigation Systems

Achtaich¹,

Souissi²,

Salinesi³

et al. 2019

IJACSA

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“…According to Figure 5, U 1.8 are the values of the N = 8 sensors and a L , a N and a C are the mean u values of each one of the three classes DC, NC and CC accordingly. These values are calculated based on Equations (8) and (9), where a th is the fuzzy process Athres value:…”

Section: Irrigation Systems Algorithmsmentioning

confidence: 99%

Proposed Fuzzy-NN Algorithm with LoRaCommunication Protocol for Clustered Irrigation Systems

et al. 2017

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Abstract:Modern irrigation systems utilize sensors and actuators, interconnected together as a single entity. In such entities, A.I. algorithms are implemented, which are responsible for the irrigation process. In this paper, the authors present an irrigation Open Watering System (OWS) architecture that spatially clusters the irrigation process into autonomous irrigation sections. Authors' OWS implementation includes a Neuro-Fuzzy decision algorithm called FITRA, which originates from the Greek word for seed. In this paper, the FITRA algorithm is described in detail, as are experimentation results that indicate significant water conservations from the use of the FITRA algorithm. Furthermore, the authors propose a new communication protocol over LoRa radio as an alternative low-energy and long-range OWS clusters communication mechanism. The experimental scenarios confirm that the FITRA algorithm provides more efficient irrigation on clustered areas than existing non-clustered, time scheduled or threshold adaptive algorithms. This is due to the FITRA algorithm's frequent monitoring of environmental conditions, fuzzy and neural network adaptation as well as adherence to past irrigation preferences.

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“…They choose NRF24L01 because of the low power consumption. NRF24L01 requires 1.9-3.6V and uses 2.4GHz [23]. On the other hand Srishti Rawal proposes an automated irrigation system which monitors and maintains the desired soil moisture content via automatic watering.…”

Section: Related Workmentioning

confidence: 99%

A Smart IoT Fuzzy Irrigation System

Kokkonis¹,

Kontogiannis²,

Tomtsis³

2017

IOSR JEN

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This paper proposes a novel fuzzy computational algorithm for IoT smart irrigation systems. It describes all the possible sensors, actuators and microcontrollers that could be used in the irrigation systems. Based on these sensors a novel system architecture is presented. The irrigation system continuously monitoring air temperature and humidity, and ground moisture. The ground humidity sensors are interspersed all over the field. The measurements are send in a microcontroller that applies a fuzzy computational algorithm and decides whether to open a servo valve or not. All the data collected from the microcontroller are sent to a cloud database for statistical information and processing.

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IOT based crop-field monitoring and irrigation automation

Cited by 176 publications

References 11 publications

A Constraint-based Approach to Deal with Self-Adaptation: The Case of Smart Irrigation Systems

A Constraint-based Approach to Deal with Self-Adaptation: The Case of Smart Irrigation Systems

Proposed Fuzzy-NN Algorithm with LoRaCommunication Protocol for Clustered Irrigation Systems

A Smart IoT Fuzzy Irrigation System

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