Enhancing Crop Yield Prediction with IoT and Agricultural UAVs: A Comprehensive Review
DOI:
https://doi.org/10.4108/eetiot.10105Keywords:
Artificial Intelligence, Internet of Things, IoT Sensors, Machine Learning, Unmanned Aerial Vehicles, Yield PredictionAbstract
INTRODUCTION: Rapid development in the field of the Internet of Things (IoT) and Unmanned Aerial Vehicles (UAVs) is allowing them to be utilized across multiple sectors like industrial manufacturing, healthcare, defense, etc. In the agricultural industry, IoT and UAVs are also proving themselves as one of the most promising technologies. These technologies have opened the door to numerous innovative opportunities in precision agriculture, particularly in predicting crop yields more accurately and efficiently. Traditional methods for crop yield prediction were based on manual sampling and statistical models, which proved to be time-consuming and less accurate.
OBJECTIVES: This paper mainly contributes to the comprehensive study of IoT and UAVs in crop yield prediction. It highlights how data-driven methods, sensor technologies, and remote sensing enhance decision-making in precision agriculture.
METHODS: The paper discusses traditional practices for crop yield prediction and their limitations. It explains the architecture of IoT and its various layers, including a detailed study and comparison of different IoT sensors, microcontrollers, and communication standards. The paper further focuses on the potential of UAVs for yield prediction, including details of different types of UAV platforms, control strategies, and communication standards. Additionally, the paper explains the benefits and limitations of integrating IoT and UAVs for more accurate crop yield prediction.
RESULTS: The study demonstrated that IoT-enabled monitoring and UAV-based remote sensing improve crop prediction accuracy.
CONCLUSION: Overall, this paper presents the transformative capability of integrating IoT and UAV in modernizing the process of crop yield prediction and other precision agriculture practices. As a future scope, the paper focuses on the use of edge/fog computing, mobile apps, and AI chatbots to enhance the power of IoT and UAVs in crop yield prediction.
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