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The SWAMP project published a paper in the 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor 2020) reporting an experience aimed at evaluating the Sensitivity of the agro-hydrological model CRITERIA-1D to the Leaf Area Index parameter for precision irrigation management. The CRITERIA-1D model is currently running into the SWAMP platform for estimating crop water requirements in the Italian pilot. The leaf area index is an input data of the model and is used to characterize the plant status and to represent its developing stages. To assess the importance of the LAI parameter on the estimation of the plant water needs, the model was set-up to mimic the agro-environmental conditions of the selected pear orchard. Two scenarios were defined: in the first reference scenario literature LAI values were used (S_ref); in the second scenario measured LAI values (S_LAI), collected with the AccuPAR LP80 ceptometer, were replaced to reproduce the real filed conditions. During the summer 2019, four sampling campaigns were conducted in the pear orchard and LAI measurements were collected in 16 points in a regular grid; for every point six measurements were taken around the plant.

Results of the simulations with the model are presented in the two figures below. As it is shown, for the modified scenario (S_LAI) LAI values were lower than those of the reference scenario (S_ref). This is accompanied by a decrease of the plant’s potential transpiration (left graph) resulting in less water demand for the modified scenario (S_LAI).

Looking at the irrigation water requirements, a lower number of irrigation events compared to the reference scenario have been quantified. Consequently, a lower total irrigation water need is also estimated, as it is possible to see in the right figure.

As general conclusion, the results confirmed the sensitivity of water need estimation CRITERIA-1D model output to the LAI values. Following this analysis, the model into the SWAMP platform has been updated using LAI ground-based measurements, to better represent the actual field condition as this parameter affects the management of irrigation water and its consequent saving.

The SWAMP project published a paper in the 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor 2020) reporting the process for obtaining a linear equation for the calibration of soil moisture sensor measurements. Calibration Equation and Field Test of a Capacitive Soil Moisture Sensor recognizes that many IoT solutions rely on the use of soil moisture sensors to gather data, in real- time, from the plant’s root zone. This study presents a linear calibration curve, obtained by the thermogravimetric method, for a capacitive soil moisture sensor for a silty clay soil.

The curve obtained has an adjusted R² of 0.86 and a residual standard error of 2.8%. The soil sensor was tested in the field during the end of February revealing that the sensor is capable of indicating changes in soil moisture within the error obtained from the calibration procedure and that it can be used to indicate changes by rain or irrigation procedures.

During the experimental procedure, the incorrect soil compaction might lead to measurement errors, which can compromise the final calibration model. One evidence relies on data from sample 1, in which an outlier was observed between sensors values of 18,000 and 22,000, and water content values of 20% and 30%.

The SWAMP project published a paper in the 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor 2020) reporting a mixed-integer linear optimization solution for water distribution for irrigation. Smart Water Management in Agriculture: a Proposal for an Optimal Scheduling Formulation of a Gravity Water Distribution System proposes an improved approach to the irrigation scheduling problem, reducing water wastage while satisfying farmers’ demands and crops’ water needs. For water distribution system managed with on-demand distribution approach, the efficiency of irrigation relies on the ability of the network manager (i.e., gatekeeper) to guarantee a proper service, consisting in: the irrigation scheduling, the definition of the volume of water passing through the channels at a given time, and the operations on gates and sluices to make the water reach the farms.

We propose an improved mixed-integer linear optimization formulation that adds the possibility to store water in the channels and takes seepage into account. This new formulation is able to better represent the physical behavior of the water flow in the channels network, also avoiding the presence of flooding. The proposed optimization solution is embedded within a wider monitoring framework with the intent to fully exploit the availability of a complex network of models, repositories and sensors installed in the field.

The resulting problem is solved by one of the most used optimization solvers (IBM ILOG CPLEX) and tested on a synthetic benchmark. Furthermore, we validate the results on a digital copy of the network that performs a hydraulic simulation of the irrigation system. The scheduling is accepted if the water introduced in the system can satisfy farmers’ requests with the considered timing and does not produce flooding.

The SWAMP project published a paper in the 2020 IEEE International Workshop on Metrology for Agriculture and Forestry ( MetroAgriFor 2020 ) describing the Farmer Application for mobile devices. The Farmer App provides a user-friendly interface to the smart water services running the SWAMP platform.

The services are accessed through the SWAMP API, RESTful API also described in the paper (see schematicin the figure below).

The Farmer App allows the monitoring of the water status of the fields, including current measurements and historical data in the form of charts (figure below, on the left). A satellite view of the fields is also available, where the farmer can see farm divisions, probe locations and more (figure below, on the right). The farmer can also visualize and adjust irrigation plans generated by the SWAMP platform. The irrigation plan describes the timing and amount of water of irrigation events for all fields (and its divisions), giving the farmer control over the water cycle in their farm.

The SWAMP project published a paper in the 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor 2020) reporting an experience with various IoT technologies for the measurement of smart agriculture. IoT-based Measurement for Smart Agriculture presents an IoT technology set applied to the acquisition of agricultural data using open source solutions such as FIWARE and LoRaWAN, which allow extensive customization and integration with advanced weather forecasting, Machine Learning, and real-time dashboard services. The results obtained by the combination of different tools and platforms in pilots located in Brazil and Europe reveal a high versatility of the IoT technology applied to smart agriculture.

The SWAMP data collection occurs on three fronts. Firstly, through soil probes that use LoRaWAN. Secondly, in the weather station that uses RS-232 serial communication. Finally, in the data collection from weather forecast services in different providers according to the region. For the soil probe and the weather station, the Mist node, composed by a Raspberry Pi and a LoRa shield, collects field data.

The charts below show examples of time series of the air temperature collected by a Weather Station (left) and by two weather forecast services (right) for the Guaspari pilot.

The chart below shows the soil moisture obtained by the multi-depth soil sensor probe revealing a sample of the data availability problems faced in a real world operation, that increased considerably during the development of the project.

The SWAMP project published a paper in the 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor 2020) reporting the soil sensor probe developed by the SWAMP Project. Enhancing Soil Measurements with a Multi-Depth Sensor for IoT-based Smart Irrigation reporting a multi-depth and multi-parameter probe for soil data collection utilized to on-farm research by the SWAMP project. The probe is based on LoRaWAN communication and has sensors for soil moisture, temperature, and electrical conductivity.

The sensor core module (left in the picture below) is based on the ESP32 system on a chip, a power supply block, and the interfaces to sensors, secondary acquisition boards and a Radio Communication module. The communication module accommodates the wireless solution according to the network needs (LoRaWAN, Zigbee, Narrow Band, among others). The sensing module is composed of directly connected sensors and additional acquisition boards, which will convert the analogical signal from sensors into digital format. The picture in the right presents the SWAMP probe with three sensing units (left) at 20, 40, and 60 cm depths, and one sensing unit in detail with the acquisition board exposed

The chart below shows 25 data packets sent by the probe. The raw data shows the moisture levels varying during the period of data collection. The peak between the tenth and the sixteenth packet presents the behavior of the sensor during an irrigation session.

The validation of the solution was made in real deployments in two pilots for smart irrigation. The data obtained by the probes is sent to a cloud platform, where a user can see and analyze the data. The probe electronics can also be used in other applications of smart agriculture.

The SWAMP project published a paper in the 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor 2020) reporting a data filtering mechanism for optimizing data transmission between fog and cloud in an IoT system. A Nearest Neighbors based Data Filter for Fog Computing in IoT Smart Agriculture proposes an approach to collect and store data in a smart agriculture environment and two different methods filtering data in the fog. We designed an experiment for each filtering method, using a real dataset containing temperature and humidity values. In both experiments, the fog filters the data using the k-Nearest-Neighbors (kNN) algorithm, which classifies data into categories according to their value ranges.

The dataflow is presented in the picture below. Sensors collect data from a farm environment and send it to the fog. The data arrives in the fog through a network server, transferring the raw data to the data storage module and the data analytics module. The data analytics module analyses the raw data to make decisions, then, it stores the decisions in fog memory and sends the decisions to actuators to irrigate a crop. The data storage module is responsible for storing data in memory. It can provide past decisions and filtered data to the analytics module. Furthermore, the data storage module provides the raw data periodically to the fog data filter.

We divided the classification process into two rounds: for the round 1, we classified the data collected between 00:00 and 06:00 and for the round 2, between 06:01 and 12:00 (both on the first day of August 2013). The chart below shows a correlogram for each round comprising four graphic plots. The correlogram includes two density plots and two dispersion plots, highlighting the category density according to temperature and humidity. In some density curves, we observed that classes overlap, although it does not prevent data classification from occurring because we deal with two attributes

The SWAMP project played an important role in the IEEE MetroAgriFor 2020 (2020 IEEE International Workshop on Metrology for Agriculture and Forestry), having members serving as conference organizers, chairs of two special sessions, and publication of 10 research papers.

SWAMP Papers presented at MetroAgriFor 2020

• IoT-based Measurement for Smart Agriculture
• Understanding the tradeoffs of LoRaWAN for IoT-based Smart Irrigation
• Enhancing Soil Measurements with a Multi-Depth Sensor for IoT-based Smart Irrigation
• The SWAMP Farmer App for IoT-based Smart Water Status Monitoring and Irrigation Control
• Water spray detection for smart irrigation systems with Mask R-CNN and UAV footage (Best Paper Award!)
• Sensitivity of the agro-hydrological model CRITERIA -1D to the Leaf Area Index parameter
• Calibration equation and field test of a capacitive soil moisture sensor
• Smart Water Management in Agriculture: a Proposal for an Optimal Scheduling Formulation of a Gravity Water Distribution System
• A Nearest Neighbors based Data Filter for Fog Computing in IoT Smart Agriculture
• Enabling Context Aware Tuning of Low Power Sensors for Smart Agriculture

Conference Organizers: TPC Co-Chair and Publication Chair

Special Session Chairs

We have the pleasure to release the first SWAMP Newsletter, to spread the word about the concepts, efforts, practical experiences, and lessons learned during the development of the project. As a multi-partner, multi-national, and bi-continental research collaboration, SWAMP has been collecting valuable experiences regarding the smart management of water resources, particularly for irrigation purposes in agriculture. Agriculture is the biggest consumer of freshwater in the world, amounting up to 70%, which makes a case for smart water management to guarantee water and food security to the world’s population.
In the SWAMP Newsletter #1, we expect to convey valuable information to different interested stakeholders regarding impact creation measures of communication, dissemination, and exploitation. Notably, this issue is featuring:
• Summary of SWAMP Communication Activities
• Summary of SWAMP Dissemination Activities
• Summary of SWAMP Exploitation Activities
• SWAMP Interest Group – Call for Members
• News from the SWAMP Pilots
• Summary of recent research papers
If you are willing to obtain additional information regarding the SWAMP Project’s findings, please do not hesitate to contact us by any means, including the official project email at contact@swamp-project.org. Also, if you want to be a member of the SWAMP Interest Group, which promotes the exploitation of SWAMP project results firsthand, please sign up here.

Sincerely,

Juha-Pekka Soininen & Carlos Kamienski
(on behalf of the SWAMP partners)