Deep learning and Multiple Sensors Data Acquisition System for Real-Time Decision Analysis in Agriculture Using Unmanned Aerial Vehicle

EasyChair Preprint 2690

Abstract

In precision agriculture, Unmanned Aerial Vehicle (UAV) have the flexibility for data acquisition and analysis in real time from multiple sensors. However, the numbers of sensors and their data analysis from single processing unit still not available. In addition, the UAV operation needs frequent landing due to limited battery charge. Consequently, the real-time application requires the application of Artificial Intelligence (AI) in the decision making process for soil moisture sensing, safety in UAV flying operation starting from takeoff and landing.  Deep learning and multiple sensors data acquisition process could serve in the decision analysis while using an UAV. Therefore, the purpose of this research is to develop real-time data analysis system to enable AI for using UAV in the agricultural operations more robustly with safe landing.

Keyphrases: Artificial Intelligence (AI), UAV, YOLO, multiple sensors

@booklet{EasyChair:2690,
  author    = {Yunyan Xie and Ryozo Noguchi and Tofael Ahamed},
  title     = {Deep learning and Multiple Sensors Data Acquisition System for Real-Time Decision Analysis in Agriculture Using Unmanned Aerial Vehicle},
  howpublished = {EasyChair Preprint 2690},
  year      = {EasyChair, 2020}}