Smart Collective Irrigation

Ikidid, Abdelouafi; Fazziki, Abdelaziz El; Sadgal, Mohammed

doi:10.1145/3444757.3485113

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…In addition, the control unit may receive complementary information, such as environmental parameters and crop conditions, to improve algorithm accuracy using model-based estimations. The strategies to implement the algorithms are mostly based on classical and modern control theories like on-off control [13], PID (proportional-integral-derivative) control [14], and MPC (model predictive control) [15,16]; however, recently artificial intelligent approaches such as fuzzy logic [17][18][19], machine learning [20,21], and multi-agent systems [22,23] have gained the attention of the research community due to the initial promising results in the area of data-driven agriculture. However, most works on closed-loop irrigation consider one crop and a single irrigation area without water constraints.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis between Heuristic and Data-Driven Water Management Control for Precision Agriculture Irrigation

et al. 2023

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Modeling and control theory applied to precision agriculture irrigation systems have been essential to reduce water consumption while growing healthy crops. Specifically, implementing closed-loop control irrigation based on soil moisture measurements is an effective approach for obtaining water savings in this resource-intensive activity. To enhance this strategy, the work presented in this paper proposed a new set of water management strategies for the case in which multiple irrigation areas share a single water supply source and compared them with heuristic approaches commonly used by farmers in practice. The proposed water allocation algorithms are based on techniques used in real-time computing, such as dynamic priority and feedback scheduling. Therefore, the multi-area irrigation system is presented as a resource allocation problem with availability constraints, where water consumption represents the main optimization parameter. The obtained results show that the data-driven water allocation strategies preserve the water savings for closed-loop control systems and avoid crop water stress due to the limited access to irrigation water.

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Section: Introductionmentioning

confidence: 99%

A Comparative Analysis between Heuristic and Data-Driven Water Management Control for Precision Agriculture Irrigation

et al. 2023

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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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A New Approach to Intelligent-Oriented Analysis and Design of Urban Traffic Control: Case of a Traffic Light

Ikidid

Ghazouani

Khanboubi

et al. 2023

Lecture Notes in Networks and Systems

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Smart Collective Irrigation

Cited by 5 publications

References 6 publications

A Comparative Analysis between Heuristic and Data-Driven Water Management Control for Precision Agriculture Irrigation

A Comparative Analysis between Heuristic and Data-Driven Water Management Control for Precision Agriculture Irrigation

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

A New Approach to Intelligent-Oriented Analysis and Design of Urban Traffic Control: Case of a Traffic Light

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