Discipline: Technology and Engineering
Subcategory: Civil/Mechanical/Manufacturing Engineering
Agustin Bernier-Vega - Texas A&M University-Kingsville
Precision agriculture is a crucial area of interest due to its potential impact on crop managementdecisions in the United States. Currently, only about 25% of farmers in the United States employany form of precision agriculture. However, the advancement of technologies such as WirelessSensor Networks (WSN)’s and Unmanned Aerial Vehicles (UAV)’s has furthered opportunitiesfor effective remote monitoring. Specifically, this investigation compares two major kinds ofwireless data retrieval: Static data collection and Mobile data collection. The former was achievedwith a commercially available solution, and the latter used a custom UAV-based approach. Thecommercial solution utilizes a mesh-based network of sensor nodes communicating to a fixed datalogger, whereas the custom approach uses a drone as a mobile data collector. The custom solutionwas developed as an affordable alternative using 3D-printed enclosures for the sensor node anddrone node modules. After investigating the advantages of both static and mobile data collection,their viability is discussed and compared to one another according to categories such ascommercial availability, data collection, cost, operation, and scalability.The results showed that while both Mobile and Static data collection methods have strengths andweaknesses, the main differences lie in the affordability and range of application. Mobilecollection via drone has advantages such as a lower cost, large-scale adaptability, and longevity,whereas Static collection systems offer more commercial options, easier maintenance, and fasterdata retrieval. While static collection systems have become more widespread in recent years, thereare few real-world applications of UAVs in agriculture. It is recommended that research shouldcontinue in this area so that it may become more accessible as a solution for farmers andresearchers. In particular, those working in remote and rugged areas.
Funder Acknowledgement(s): This graduate study was supported by Texas A&M University - Kingsville as part of the CREST-SWU using research grant funds from the National Science Foundation (NSF).
Faculty Advisor: Dr. Selahattin Ozcelik, firstname.lastname@example.org
Role: My contributions to the research involved the operation of the commercial HOBOnet data collection system, all relevant analysis of the collected data, all detailed comparison of the Aerial and Mobile systems, and early SOLIDworks drafts of the 3D printed enclosure as well as decision-making and feedback on the design of the custom solution as it was being developed.