[en] Timely and accurate mapping of leaf area index (LAI) in vineyards plays an important role for management choices in precision agricultural practices. However, only a little work has been done to extract the LAI of pergola-trained vineyards using higher spatial resolution remote sensing data. The main objective of this study was to evaluate the ability of unmanned aerial vehicle (UAV) imageries to estimate the LAI of pergola-trained vineyards using shallow and deep machine learning (ML) methods. Field trials were conducted in different growth seasons in 2021 by collecting 465 LAI samples. Firstly, this study trained five classical shallow ML models and an ensemble learning model by using different spectral and textural indices calculated from UAV imageries, and the most correlated or useful features for LAI estimations in different growth stages were differentiated. Then, due to the classical ML approaches need the arduous computation of multiple indices and feature selection procedures, another ResNet-based convolutional neural network (CNN) model was constructed which can be directly fed by cropped images. Furthermore, this study introduced a new image data augmentation method which is applicable to regression problems. Results indicated that the textural indices performed better than spectral indices, while the combination of them can improve estimation results, and the ensemble learning method showed the best among classical ML models. By choosing the optimal input image size, the CNN model we constructed estimated the LAI most effectively without extracting and selecting the features manually. The proposed image data augmentation method can generate new training images with new labels by mosaicking the original ones, and the CNN model showed improved performance after using this method compared to those using only the original images, or augmented by rotation and flipping methods. This data augmentation method can be applied to other regression models to extract growth parameters of crops using remote sensing data, and we conclude that the UAV imagery and deep learning methods are promising in LAI estimations of pergola-trained vineyards.
Disciplines :
Agriculture & agronomy
Author, co-author :
Ilniyaz, Osman; State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China ; School of Resource and Environmental Sciences, Wuhan University, Wuhan, China ; Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi, China
Du, Qingyun; School of Resource and Environmental Sciences, Wuhan University, Wuhan, China ; Key Laboratory of GIS, Ministry of Education, Wuhan University, Wuhan, China ; Key Laboratory of Digital Mapping and Land Information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, Wuhan University, Wuhan, China ; Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, China
Shen, Huanfeng; School of Resource and Environmental Sciences, Wuhan University, Wuhan, China
He, Wenwen; School of Resource and Environmental Sciences, Wuhan University, Wuhan, China
Feng, Luwei; School of Resource and Environmental Sciences, Wuhan University, Wuhan, China
Azadi, Hossein ; Université de Liège - ULiège > TERRA Research Centre > Modélisation et développement ; State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China ; Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi, China ; Department of Geography, Ghent University, Ghent, Belgium ; Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czech Republic
Kurban, Alishir; State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China ; Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi, China
Chen, Xi; Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi, China
Language :
English
Title :
Leaf area index estimation of pergola-trained vineyards in arid regions using classical and deep learning methods based on UAV-based RGB images
This work was jointly funded by the National Natural Science Foundation of China (Grant No. 32071655; 42230708) and by Chinese Academy of Sciences President's International Fellowship Initiative (2021VCA0004; 2020VCA0015).We would like to thank Rosul Memet who works in the Science and Technology Bureau of Turpan for lending the UAV for field campaigns and for providing other related information about vineyards. We would also like to thank Abliz Ilniyaz, Erkinjan Abliz, Sulayman Abdul, Dawut Hoshur, and Ghoji Mengnik for their help in field measurements.
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