Using Multi-Sensor Data Fusion Techniques and Machine Learning Algorithms for Improving UAV-Based Yield Prediction of Oilseed Rape

Using Multi-Sensor Data Fusion Techniques and Machine Learning Algorithms for Improving UAV-Based Yield Prediction of Oilseed Rape

Accurate and timely prediction of oilseed rape yield is crucial in precision agriculture and field remote sensing. We explored the feasibility and potential for predicting oilseed rape yield through the utilization of a UAV-based platform equipped with RGB and multispectral cameras. Genetic algorith...

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Bibliographic Details
Main Authors: Hongyan Zhu, Shikai Liang, Chengzhi Lin, Yong He, Jun-Li Xu
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Drones
Subjects:
data fusion
machine learning algorithms
oilseed rape
remote sensing
unmanned aerial vehicle (UAV)
yield prediction
Online Access:https://www.mdpi.com/2504-446X/8/11/642
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