Automated Precision Irrigation System Using Machine Learning and IoT

Bhoi, Ashutosh; Nayak, Rajendra Prasad; Bhoi, Sourav Kumar; Sethi, Srinivas

doi:10.1007/978-981-33-6081-5_24

Cited by 6 publications

(6 citation statements)

References 13 publications

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“…Although growers see potential in using the Internet of Things, remote sensing, and machine learning (Agriculture 4.0) for having better decision-making processes, particularly in irrigation, substantially less work has been undertaken on water management activities ( Benos et al, 2021 ). Within the studies focused on water management, machine learning techniques have been applied to estimate groundwater reservoirs, soil moisture ( Paloscia et al, 2013 ; Coopersmith et al, 2016 ; Prasad et al, 2018 ; Singh et al, 2019 ; Babaeian et al, 2021 ; Greifeneder et al, 2021 ; Grillakis et al, 2021 ; Orth, 2021 ; Sungmin and Rene, 2021 ), evapotranspiration ( Ponraj and Vigneswaran, 2020 ), and provide irrigation control ( González-Briones et al, 2019 ; Kondaveti et al, 2019 ; Murthy et al, 2019 ; Akshay and Ramesh, 2020 ; Campoverde et al, 2021 ; Ikidid et al, 2021 ; Perea et al, 2021 ; Bhoi et al, 2021a ), among other applications ( Liakos et al, 2018 ; Cardoso et al, 2020 ; Perea et al, 2021 ; Bhoi et al, 2021b ). The machine learning techniques applied in these studies are shown in Table 1 , following the classification suggested in ( Liakos et al, 2018 ) and considering two additional categories: Multi-Agent System (MAS) and Genetic Algorithm.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…Recently, smart irrigation system (also referred as precision irrigation system) is considered as a key solution to save the limited water resources as well as to improve crop productivity and quality [1][2][3][4][5][6][7][8][9][10][11]. Indeed, the application of Internet of things (IoT) allows prediction of olive tree's needs (according to the sensed environmental parameters from the soil or the climate), and provides optimal decisions to the farmers about possible things to do, in real time [6].…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, the application of Internet of things (IoT) allows prediction of olive tree's needs (according to the sensed environmental parameters from the soil or the climate), and provides optimal decisions to the farmers about possible things to do, in real time [6]. Although several IoT based irrigation systems have been introduced in literature [1][2][3][4][5][6][7][8][9][10][11], there are several challenges that still need to be tackled [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Authors in [8] employ the MQTT (Message Queuing Telemetry Transport) protocol. In [9] Bhoi et al used machine learning (ML) and internet of things to deploy a smart irrigation system in Raipur, India. In order to save water usage with minimum human assistance, several ML-based regression and classification models were used in [9].…”

Section: Introductionmentioning

confidence: 99%

“…In [9] Bhoi et al used machine learning (ML) and internet of things to deploy a smart irrigation system in Raipur, India. In order to save water usage with minimum human assistance, several ML-based regression and classification models were used in [9]. Authors had integrated the forecasted weather data in order to ensure the robustness of their system.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Secure Irrigation System for Olive Orchards Using Internet of Things

Massaoudi¹,

Berguiga²,

Harchay³

2022

Computers, Materials &Amp; Continua

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Smart irrigation system, also referred as precision irrigation system, is an attractive solution to save the limited water resources as well as to improve crop productivity and quality. In this work, by using Internet of things (IoT), we aim to design a smart irrigation system for olive groves. In such IoT system, a huge number of low-power and low-complexity devices (sensors, actuators) are interconnected. Thus, a great challenge is to satisfy the increasing demands in terms of spectral efficiency. Moreover, securing the IoT system is also a critical challenge, since several types of cybersecurity threats may pose. In this paper, we address these issues through the application of the massive multiple-input multiple-output (M-MIMO) technology. Indeed, M-MIMO is a key technology of the fifth generation (5G) networks and has the potential to improve spectral efficiency as well as the physical layer security. Specifically, by exploiting the available M-MIMO channel degrees of freedom, we propose a physical layer security scheme based on artificial noise (AN) to prevent eavesdropping. Numerical results demonstrate that our proposed scheme outperforms traditional ones in terms of spectral efficiency and secrecy rate.

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An smart irrigation system based on IoT to enhance the accuracy using novel K-nearest neighbor compared with random forest algorithm to increase accuracy rates on agricultural fields

Reddy,

Rahiman

2024

AIP Conference Proceedings

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Automated Precision Irrigation System Using Machine Learning and IoT

Cited by 6 publications

References 13 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

Secure Irrigation System for Olive Orchards Using Internet of Things

An smart irrigation system based on IoT to enhance the accuracy using novel K-nearest neighbor compared with random forest algorithm to increase accuracy rates on agricultural fields

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