Iot and Generative AI for Enhanced Data-Driven Agriculture
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| Publicado en: | ProQuest Dissertations and Theses (2025) |
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| Acceso en línea: | Citation/Abstract Full Text - PDF Full text outside of ProQuest |
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| 100 | 1 | |a Bailey, Joshua Karl | |
| 245 | 1 | |a Iot and Generative AI for Enhanced Data-Driven Agriculture | |
| 260 | |b ProQuest Dissertations & Theses |c 2025 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a The adoption of IoT in agriculture faces significant challenges due to inadequate field-level connectivity. LoRaWAN has emerged as a leading solution, offering long-range, low-power communication with scalable deployment options. Cloud-based LoRaWAN gateway implementations, including Wi-Fi, cellular, and hybrid approaches, are presented to leverage existing farm infrastructure to reduce costs. An open-source software stack was designed to process and store sensor data efficiently, ensuring scalability and resilience. Case studies to demonstrate successful deployments in a commercial apple orchard and a research farm highlight real-world application. A cost evaluation framework is also provided, revealing minimal costs for the hardware implementations, software for analysis, and data storage.While IoT improves site-specific, real-time farm data collection, Generative AI has potential to transform farm data analysis by reducing knowledge barriers, enhancing digital solutions, and interfacing with multiple data sources to enable intuitive interactions. By leveraging Generative AI tools, farm managers could efficiently extract insights from both dissociated (requiring import) and integrated (directly connected) data sources. Dissociated data demonstrations showcased Generative AI capabilities in analyzing machinery maintenance records from a CSV file, financial statements in an Excel file paired with a university extension resource PDF, and yield data paired with soil type spatial data in CSV files. A framework for integrated data was also provided, with demonstrations utilizing public weather data, a private field records database, and an SQL database containing IoT sensor data, each accessed via in-built and custom APIs. Generative AI did generate correct responses to relatively complex analyses, but care in prompting is required. Further developments and research in Generative AI are required to enhance its reliability for management decisions, and some degree of custom coding remains necessary for integrated data. | |
| 653 | |a Agriculture | ||
| 653 | |a Management decisions | ||
| 653 | |a Open source software | ||
| 653 | |a Internet of Things | ||
| 653 | |a Decision making | ||
| 653 | |a Chatbots | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2025) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3235007353/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3235007353/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u https://figshare.com/articles/thesis/IoT_and_Generative_AI_for_Enhanced_Data-Driven_Agriculture/28899107 |