Classification of Maize Growth Stages Based on Phenotypic Traits and UAV Remote Sensing

作者：Yihan Yao, Jibo Yue, Yang Liu, Hao Yang, Haikuan Feng, Jianing Shen, Jingyu Hu, Qian Liu · 发表于：Agriculture · 年份：2024 · DOI：10.3390/agriculture14071175 · 被引用次数：18 · 研究领域：Remote Sensing in Agriculture、Smart Agriculture and AI、Remote Sensing and LiDAR Applications

Maize, an important cereal crop and crucial industrial material, is widely used in various fields, including food, feed, and industry. Maize is also a highly adaptable crop, capable of thriving under various climatic and soil conditions. Against the backdrop of intensified climate change, studying the classification of maize growth stages can aid in adjusting planting strategies to enhance yield and quality. Accurate classification of the growth stages of maize breeding materials is important for enhancing yield and quality in breeding endeavors. Traditional remote sensing-based crop growth stage classifications mainly rely on time series vegetation index (VI) analyses; however, VIs are prone to saturation under high-coverage conditions. Maize phenotypic traits at different growth stages may improve the accuracy of crop growth stage classifications. Therefore, we developed a method for classifying maize growth stages during the vegetative growth phase by combining maize phenotypic traits with different classification algorithms. First, we tested various VIs, texture features (TFs), and combinations of VI and TF as input features to estimate the leaf chlorophyll content (LCC), leaf area index (LAI), and fractional vegetation cover (FVC). We determined the optimal feature inputs and estimation methods and completed crop height (CH) extraction. Then, we tested different combinations of maize phenotypic traits as input variables to determine their accuracy in classifying growth s...