Cloud-based multi-sensor remote data acquisition system for precision agriculture (CSR-DAQ)

Abstract

<p>Many of the current agriculture systems have deployed analog/digital sensors to measure crop monitoring, weather forecast, and environmental sensor data. The significant problems of the current agriculture system are — 1) the inability to combine the collected sensor data into useful information for farmers to make the right decision to optimize the crop produce; 2) legacy infrastructure and manual data collection; 3) lack of scalability and incompatibility due to the vendor-dependent sensors and legacy data loggers. With the advent of the Internet of Things (IoT), the ad-hoc and traditional agriculture systems adopt precision agriculture methods to improve the quality and quantity of harvest. To realize such precision agriculture methods in Smart farming, we require a platform that collects the sensor data, processes it into information and helps in visualizing the results. The existing custom-made prototype solutions and the industry-grade data acquisition systems are expensive and have limited functionalities to realize the precision agriculture methods.</p> <p>In this thesis, we propose an architecture and testbed-based implementation for a cost-effective active data acquisition system that can autonomously collect, transmit, and process the raw data. The proposed architecture includes four modules - Nodes, Aggregators, Cloud-based Database, and Client-side applications. The functionalities of these modules are — 1) Node collects sensor data at specified intervals and transmits the sensor data streams to the aggregator; 2) Aggregator executes a data serializer for converting the sensor data streams, buffer for local storage, and data transmitter for sending them to the cloud-based database system; 3) Cloud-based Database is hosted on Amazon Relational Database Services (RDS) and uses Postgres SQL to facilitate multiple reads, write, and no overwrite functionality; and 4) Client-side applications include web pages, mobile apps, and services that communicate the cloud-based database system for the field sensor data.</p> <p>The test-bed was set up at the Iowa State University greenhouse environment to read controlled environmental data. Collected data from a commercial sensor validated the measurements as a benchmark tool. The end-to-end test setup and obtained results were congruent with the design specifications and satisfied the user requirements.</p> <p>Analog sensors with the proper specifications are compatible with the proposed hardware to read environmental data without additional modifications. Field test implementation also successfully validated the design with real-time data collection.</p> <p>The results with the VWC from measured sensors have 98\% $R^2$ values on performing linear regression. Battery optimization was also found to allow the Data-logger to work for an entire harvest season. Thus, CSR-DAQ solves the need for smart systems for small-scale farmers by providing them active data acquisition units at cost-effective budgets and allows them to make a decision or automate certain parts of farming such as irrigation and fertilizer control.</p>