Dataset Information

Development of a LoRaWAN IoT Node with Ion-Selective Electrode Soil Nitrate Sensors for Precision Agriculture.

ABSTRACT: Crop productivity is highly dependent on the availability of soluble nitrogen (N), e.g. nitrate, in soil. When N levels are low, fertilisers are applied to replenish the soil's reserves. Typically the timing of these applications is based on paper-based guidance and sensor-based measurements of canopy greenness, which provides an indirect measure of soil N status. However this approach often means that N fertiliser is applied inappropriately or too late, resulting in excess N being lost to the environment, or too little N to meet crop demand. To promote greater N use efficiency and improve agricultural sustainability, we developed an Internet of Things (IoT) approach for the real-time measurement of soil nitrate levels using ion-selective membrane sensors in combination with digital soil moisture probes. The node incorporates state-of-the-art IoT connectivity using a LoRaWAN transceiver. The sensing platform can transfer real-time data via a cloud-connected gateway for processing and storage. In summary, we present a validated soil sensor system for real-time monitoring of soil nitrate concentrations, which can support fertiliser management decisions, improve N use efficiency and reduce N losses to the environment.

SUBMITTER: Bristow N

PROVIDER: S-EPMC9739143 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Development of a LoRaWAN IoT Node with Ion-Selective Electrode Soil Nitrate Sensors for Precision Agriculture.

Bristow Noel N Rengaraj Saravanan S Chadwick David R DR Kettle Jeff J Jones Davey L DL

Sensors (Basel, Switzerland) 20221123 23

Crop productivity is highly dependent on the availability of soluble nitrogen (N), e.g. nitrate, in soil. When N levels are low, fertilisers are applied to replenish the soil's reserves. Typically the timing of these applications is based on paper-based guidance and sensor-based measurements of canopy greenness, which provides an indirect measure of soil N status. However this approach often means that N fertiliser is applied inappropriately or too late, resulting in excess N being lost to the e ...[more]

PMID: 36501798

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Project description:For optimizing production yield while limiting negative environmental impact, sustainable agriculture benefits from real-time, on-the-spot chemical analysis of soil at low cost. Colorimetric paper sensors are ideal candidates, however, their automated readout and analysis in the field is needed. Using mobile technology for paper sensor readout could, in principle, enable the application of machine-learning models for transforming colorimetric data into threshold-based classes that represent chemical concentration. Such a classification method could provide a basis for soil management decisions where high-resolution lab analysis is not required or available. In tropical regions, where reliable soil data is difficult to acquire, this approach would be particularly useful. Here, we report a mobile chemical analysis system based on colorimetric paper sensors that operates under tropical field conditions. A standard smartphone equipped with a dedicated software application automatically classifies the paper sensor results into three classes-low, medium, or high soil pH-which provides a basis for soil correction. The classification task is performed by a machine-learning model which was trained on the colorimetric pH indicators deployed on the paper sensor. By mapping topsoil pH on a test site with an area of 9 hectares, the mobile system was benchmarked in the field against standard soil lab analysis. The mobile system has correctly classified soil pH in 97% of test cases, while reducing the analysis turnaround time from days (soil lab) to minutes (mobile). By performing on-the-spot analyses using the mobile system in the field, a 9-fold increase of spatial resolution reveals pH-variations not detectable in the standard compound mapping mode of lab analysis. We discuss how the mobile analysis can support smallholder farmers and enable sustainable agriculture practices by avoiding excessive soil correction. The system can be extended to perform multi-parameter chemical tests of soil nutrients for applications in environmental monitoring at marginal manufacturing cost.

Dataset Information

Development of a LoRaWAN IoT Node with Ion-Selective Electrode Soil Nitrate Sensors for Precision Agriculture.

Publications

Development of a LoRaWAN IoT Node with Ion-Selective Electrode Soil Nitrate Sensors for Precision Agriculture.

Similar Datasets

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