Irrigation Control Methods for Wireless Sensor Network

Kohanbash, David; Valada, Abhinav; Kantor, George

doi:10.13031/2013.42113

Cited by 9 publications

(7 citation statements)

References 4 publications

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“…The nR5-DC nodes were exclusively used in this study. Irrigation control is achieved by interfacing the nR5 nodes with the Sensorweb software (Kohanbash et al, 2012(Kohanbash et al, , 2013.…”

Section: Sensor Network Hardware and Softwarementioning

confidence: 99%

Costs and Benefits of Implementing Sensor-controlled Irrigation in a Commercial Pot-in-Pot Container Nursery

Belayneh

Lea‐Cox

Lichtenberg

2013

hortte

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Irrigation scheduling in ornamental plant production is complex due to the large number of species grown by individual growers and the need to consider plant, environment, and substrate conditions to make correct irrigation decisions on a daily or more frequent basis. The engineering team in our project has developed a smart wireless sensor node that is capable of integrating outputs from a range of soil moisture and environmental sensors to schedule irrigation events. In addition, an advanced monitoring and control software enables growers to manage irrigation based on set-point or model-based protocols, which are then independently executed by the nodes, enhancing or replacing human decision making. During 2012, we implemented a sensor-controlled vs. grower-controlled irrigation study at a pot-in-pot nursery in Tennessee. Sensor networks were installed in two separate production blocks of 3-year-old dogwood (Cornus florida ‘Cherokee Brave’) and 2-year-old red maple (Acer rubrum ‘Autumn Blaze’) trees grown in 15- and 30-gal containers, respectively. One row of trees in each block was irrigated based on the average reading of soil moisture sensors inserted in individual trees using micropulse irrigation, i.e., sensor controlled. Trees in the adjacent row and the rest of the block were independently irrigated by the grower using standard practices, i.e., grower controlled. Sensor volumetric water content (VWC) readings and irrigation volumes were logged by nodes on a 15-min basis and were relayed to a base station on the farm. For the study period between Mar. 2012 and Nov. 2012, average daily water applied by the grower-controlled irrigation to the dogwood block was 0.92 gal/tree, compared with 0.34 gal/tree applied using sensor-controlled irrigation; for red maple, these values were 1.72 gal/tree and 1.13 gal/tree, respectively. No significant differences in tree caliper or quality were noted between the two irrigation treatments in either species over the year. The cost of water for this particular operation was negligible consisting only of pumping costs, as water is drawn from a perennial stream with excellent water quality. Consequently, a conservative return on investment for a wireless sensor network capable of covering the entire operation was 37.5%, corresponding to a payback period of 2.7 years, associated almost entirely from a reduction in irrigation management time. Pricing in a nominal cost for water of $326 per acre-foot ($1 per 1000 gal) increased annualized net savings 9-fold, reducing the payback period to less than 4 months. This analysis did not factor in additional economic benefits such as reductions in production time, losses due to disease, or increased plant quality, which have been associated with the use of sensor-based irrigation control in other studies.

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Section: Sensor Network Hardware and Softwarementioning

confidence: 99%

Costs and Benefits of Implementing Sensor-controlled Irrigation in a Commercial Pot-in-Pot Container Nursery

Belayneh

Lea‐Cox

Lichtenberg

2013

hortte

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“…Irrigation control using this WSN system is versatile and has multiple modes (Kohanbash et al, 2012a). Users can manually send manual irrigation commands, configure irrigation schedules, configure local set points, and configure irrigation based on grower tools as outlined in Table 2.…”

Section: Wsn Software Developmentmentioning

confidence: 99%

“…This ''global control'' allows data from other nodes or grower tools to be used for controlling irrigation. Global control brings precision irrigation to an entirely new level by allowing plant physiological grower tool models to run in real time and determine plant water requirements (Kohanbash et al, 2012a).…”

mentioning

confidence: 99%

Wireless Sensor Network Design for Monitoring and Irrigation Control: User-centric Hardware and Software Development

Kohanbash

Kantor

Martin³

et al. 2013

hortte

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Wireless sensor networks (WSNs) are increasingly becoming a critical tool for growers and researchers. We describe how the technology has advanced, starting with a commercially available WSN node and pushing the technology to make the data more meaningful, actionable and to add advanced irrigation control functionality. User features such as spatial views, custom charts, real-time data access, remote access, irrigation control, alerts, and plant models help create an advanced WSN system that is user centric. Growers and researchers were involved in the design process by directly communicating with the design engineers, and continuously using and testing new features, resulting in a user-centric design and experience. The results of this research are being rolled into a new line of commercial products and is continuously evolving based on user feedback and interaction.

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“…In this context, self-driving systems for agricultural machineries have been a great breakthrough towards the accomplishment of the objectives mentioned above. Indeed, once endowed the proper equipment, these robotic vehicles can harvest [1], spray [2], seed [3] and irrigate [4] as well as collect data for inventory management [5].…”

Section: Introductionmentioning

confidence: 99%

Deep Semantic Segmentation at the Edge for Autonomous Navigation in Vineyard Rows

Aghi,

Cerrato,

Mazzia

et al. 2021

Preprint

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Precision agriculture is a fast-growing field that aims at introducing affordable and effective automation into agricultural processes. Nowadays, algorithmic solutions for navigation in vineyards require expensive sensors and high computational workloads that preclude large-scale applicability of autonomous robotic platforms in real business case scenarios. From this perspective, our novel proposed control leverages the latest advancement in machine perception and edge AI techniques to achieve highly affordable and reliable navigation inside vineyard rows with low computational and power consumption. Indeed, using a custom-trained segmentation network and a low-range RGB-D camera, we are able to take advantage of the semantic information of the environment to produce smooth trajectories and stable control in different vineyards scenarios. Moreover, the segmentation maps generated by the control algorithm itself could be directly exploited as filters for a vegetative assessment of the crop status. Extensive experimentations and evaluations against real-world data and simulated environments demonstrated the effectiveness and intrinsic robustness of our methodology.

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Irrigation Control Methods for Wireless Sensor Network

Cited by 9 publications

References 4 publications

Costs and Benefits of Implementing Sensor-controlled Irrigation in a Commercial Pot-in-Pot Container Nursery

Costs and Benefits of Implementing Sensor-controlled Irrigation in a Commercial Pot-in-Pot Container Nursery

Wireless Sensor Network Design for Monitoring and Irrigation Control: User-centric Hardware and Software Development

Deep Semantic Segmentation at the Edge for Autonomous Navigation in Vineyard Rows

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