This pilot addresses several challenges of smart irrigation and water management of geographically distributed fields. End goal of the pilot is to demonstrate the use of IoT sensor platforms and drones to achieve increased crop yield with optimal water usage with minimum labour effort possible. Farms are distributed over areas of roughly 30 km radius in intensive cropping area. Growing period starts at early September and finishes by late April. Crops are from different ranges such as lettuce, endive, spinach, herbs and baby leaf. These farms are irrigated separately through their proper reservoirs and irrigation systems. There are interconnections between some of the reservoirs. To demonstrate the effectiveness of the methods, we select pilot site from the same area with control field that is using traditional irrigation and monitoring methods. Irrigation system on pilot field is managed using solenoid valves and total water consumption can be monitored via water meters. Facilities used in piloting are:

  • Local weather station;
  • On-line weather services;
  • long life smart soil sensors;
  • Drone gateway;
  • Real time cloud data analysis and decision making support.

Data is collected from weather station, on-line weather prediction services as well as various sensor placed in the field. Sensors are read using drone system acting as a sensor gateway. Autonomous drone operation is demonstrated in order to reduce the labour cost and effort. Overall the envisioned benefits of the SWAMP approach are:

  • Efficient localised irrigation and application of right amount of water to the crop;
  • Work based on soil properties, type of crop, weather and other technical data;
  • Reduce the cost by saving water and labor;
  • Avoid wasting water through percolation;
  • Stop leaching and movement of nutrients from top layers to the subsoil which is the reason for losing soil fertility and contamination of aquifers;
  • Increase the crop yield by irrigating on time and with the right amount of water;
  • Maintain the crop healthier by controlling the level of moisture in soil and leaf surface that reduces the presence of different disease causing-fungi;
  • Increase the shelf life in end product by monitoring the last irrigation which helps to avoid quality deterioration in storage period;
  • Real time decision making based on more variable factors by using intelligent and smart system based on a predictive model and advanced machine learning;
  • Possibility of having access to real time and historical data;

Figure 1 Intercrop irrigation pilot site.

Pilot site is shown in Figure 1. Immediate closeness of the control fields gives ideal circumstances to verify the effectiveness of the deployed methods. There are three growth periods during the project giving an opportunity to do iterative piloting and compare the results of different iterations.