IoT-Based Smart Agriculture and Poultry Farms for Environmental Sustainability and Development

Naranjo, Paola G. Vinueza; Nascimento-Silva, Hieda A.; Rumipamba-Zambrano, Rubén; Gomes, Igor dos Santos; Rivas, David; Patil, Navinkumar J.

doi:10.1007/978-3-030-75123-4_17

Cited by 4 publications

(1 citation statement)

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“…Much research effort has also addressed the use of TL in smart agriculture applications showing the promising advantages of the approach in enhancing the agriculture process. These efforts present the concept, tools, advantages and application of TL in smart agriculture [32][33][34][35][36][37]. In our work, we focus on implementing ML on the no-OS chips that run on ultra-low performance capabilities.…”

Section: Transfer Learningmentioning

confidence: 99%

Tiny machine learning on the edge: A framework for transfer learning empowered unmanned aerial vehicle assisted smart farming

Hayajneh,

Aldalahmeh,

Alasali

et al. 2023

IET Smart Cities

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Emerging technologies are continually redefining the paradigms of smart farming and opening up avenues for more precise and informed farming practices. A tiny machine learning (TinyML)‐based framework is proposed for unmanned aerial vehicle (UAV)‐assisted smart farming applications. The practical deployment of such a framework on the UAV and bespoke internet of things (IoT) sensors which measure soil moisture and ambient environmental conditions is demonstrated. The key objective of this framework is to harness TinyML for implementing transfer learning (TL) using deep neural networks (DNNs) and long short‐term memory (LSTM) ML models. As a case study, this framework is employed to predict soil moisture content for smart agriculture applications, guiding optimal water utilisation for crops through time‐series forecasting models. To the best of authors’ knowledge, a framework which leverages UAV‐assisted TL for the edge internet of things using TinyML has not been investigated previously. The TL‐based framework employs a pre‐trained data model on different but similar applications and data domains. Not only do the authors demonstrate the practical deployment of the proposed framework but they also quantify its performance through real‐world deployment. This is accomplished by designing a custom sensor board for soil and environmental sensing which uses an ESP32 microcontroller unit. The inference metrics (i.e. inference time and accuracy) are measured for different ML model architectures on edge devices as well as other performance metrics (i.e. mean square error and coefficient of determination [R2]), while emphasising the need for balancing accuracy and processing complexity. In summary, the results show the practical feasibility of using drones to deliver TL for DNN and LSTM models to ultra‐low performance edge IoT devices for soil humidity prediction. But in general, this work also lays the foundation for further research into other applications of TinyML usage in many different aspects of smart farming.

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