High-Precision Crop Monitoring Through UAV-Aided Sensor Data Collection

Xu Tao, Evan Damron, Simone Silvestri

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Precision agriculture technologies hold great poten-tial for improving crop monitoring and farming practices. These technologies heavily rely on collecting extensive data through environmental sensors, often scattered over large fields. However, the limited connectivity in rural areas, and the high cost of 4G/5G subscriptions, often hampers their deployment. Unmanned Aerial Vehicles (UAV s) are a valid alternative for sensor data collection across large agricultural fields. However, existing UAV-based solutions often rely on the unnecessary collection of complete information, making them inefficient and more costly. In this paper, we present an innovative framework for collecting agri-cultural sensor data using UAV s. The framework selects a set of hovering points to visit, from which the data of only a subset of sensors is collected. The data from the remaining sensors is inferred using machine learning. We introduce an optimization problem named Hovering Points Selection (HPS) to select the optimal set of hovering points, and we prove it to be NP-Hard. We then propose a polynomial 2 -greedy reinforcement learning heuristic, named DRONE (Determining hoveRing pOints with exploratioN and Exploitation), to solve HPS in polynomial time. To further expedite the inference component of DRONE, we also introduce Fast-DRONE, which relies on information theory for hovering point selection. We evaluate the performance of our proposed framework using both synthetic and real agricultural datasets. Results demonstrate up to three times performance improvements over a recent state-of-the-art approach in several scenarios and under different communication technologies.

Original languageEnglish
Title of host publicationICC 2024 - IEEE International Conference on Communications
EditorsMatthew Valenti, David Reed, Melissa Torres
Pages3506-3511
Number of pages6
ISBN (Electronic)9781728190549
DOIs
StatePublished - 2024
Event59th Annual IEEE International Conference on Communications, ICC 2024 - Denver, United States
Duration: Jun 9 2024Jun 13 2024

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607

Conference

Conference59th Annual IEEE International Conference on Communications, ICC 2024
Country/TerritoryUnited States
CityDenver
Period6/9/246/13/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • Inference and Collection of Agricultural Data
  • Precision crop monitoring
  • Unmanned Aerial Vehicles

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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