Journal Article

Estimation of Winter Wheat SPAD Values Based on UAV Multispectral Remote Sensing

Yin, Quan, Zhang, Yuting, Wang, Jianjun, Ahmad, Irshad, Wang, Weiling, Li, Weilong, Huo, Zhongyang, Zhou, Guisheng

2023

Overview

Unmanned aerial vehicle (UAV) multispectral imagery has been applied in the remote sensing of wheat SPAD (Soil and Plant Analyzer Development) values. However, existing research has yet to consider the influence of different growth stages and UAV flight altitudes on the accuracy of SPAD estimation. This study aims to optimize UAV flight strategies and incorporate multiple feature selection techniques and machine learning algorithms to enhance the accuracy of the SPAD value estimation of different wheat varieties across growth stages. This study sets two flight altitudes (20 and 40 m). Multispectral images were collected for four winter wheat varieties during the green-up and jointing stages. Three feature selection methods (Pearson, recursive feature elimination (RFE), and correlation-based feature selection (CFS)) and four machine learning regression models (elastic net, random forest (RF), backpropagation neural network (BPNN), and extreme gradient boosting (XGBoost)) were combined to construct SPAD value estimation models for individual growth stages as well as across growth stages. The CFS-RF (40 m) model achieved satisfactory results (green-up stage: R2 = 0.7270, RPD = 2.0672, RMSE = 1.1835, RRMSE = 0.0259; jointing stage: R2 = 0.8092, RPD = 2.3698, RMSE = 2.3650, RRMSE = 0.0487). For cross-growth stage modeling, the optimal prediction results for SPAD values were achieved at a flight altitude of 40 m using the Pearson-XGBoost model (R2 = 0.8069, RPD = 2.3135, RMSE = 2.0911, RRMSE = 0.0442). These demonstrate that the flight altitude of UAVs significantly impacts the estimation accuracy, and the flight altitude of 40 m (with a spatial resolution of 2.12 cm) achieves better SPAD value estimation than that of 20 m (with a spatial resolution of 1.06 cm). This study also showed that the optimal combination of feature selection methods and machine learning algorithms can more accurately estimate winter wheat SPAD values. In addition, this study includes multiple winter wheat varieties, enhancing the generalizability of the research results and facilitating future real-time and rapid monitoring of winter wheat growth.

• Publisher: MDPI AG
• Subject: Accuracy / Agricultural production / Algorithms / Altitude / Artificial intelligence / Artificial neural networks / Back propagation networks / Chlorophyll / Comparative analysis / Crops / Drone aircraft / Efficiency / Experiments / Feature selection / Fertilizers / Flight / Flight altitude / Growth / Growth stage / Identification and classification / Jointing / Learning algorithms / Machine learning / Measurement / multispectral / multispectral imagery / Multispectral photography / Neural networks / Nitrogen / Optimization / Potassium / prediction / Regression analysis / Regression models / Remote sensing / soil / Soil analysis / SPAD value / Spatial discrimination / Spatial resolution / Triticum aestivum / unmanned aerial vehicle (UAV) / Unmanned aerial vehicles / Vegetation / Wheat / Winter wheat