Predicting Leaf Nitrogen Content in Cotton with UAV RGB Images

Jinmei Kou, Long Duan, Caixia Yin, Lulu Ma, Xiangyu Chen, Pan Gao and Xin Lv
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Jinmei Kou: The Key Laboratory of Oasis Eco-Agriculture, Xinjiang Production and Construction Group, College of Agriculture, Shihezi University, Shihezi 832003, China
Long Duan: College of Information Science and Technology, Shihezi University, Shihezi 832003, China
Caixia Yin: The Key Laboratory of Oasis Eco-Agriculture, Xinjiang Production and Construction Group, College of Agriculture, Shihezi University, Shihezi 832003, China
Lulu Ma: The Key Laboratory of Oasis Eco-Agriculture, Xinjiang Production and Construction Group, College of Agriculture, Shihezi University, Shihezi 832003, China
Xiangyu Chen: The Key Laboratory of Oasis Eco-Agriculture, Xinjiang Production and Construction Group, College of Agriculture, Shihezi University, Shihezi 832003, China
Pan Gao: College of Information Science and Technology, Shihezi University, Shihezi 832003, China
Xin Lv: The Key Laboratory of Oasis Eco-Agriculture, Xinjiang Production and Construction Group, College of Agriculture, Shihezi University, Shihezi 832003, China

Sustainability, 2022, vol. 14, issue 15, 1-10

Abstract: Rapid and accurate prediction of crop nitrogen content is of great significance for guiding precise fertilization. In this study, an unmanned aerial vehicle (UAV) digital camera was used to collect cotton canopy RGB images at 20 m height, and two cotton varieties and six nitrogen gradients were used to predict nitrogen content in the cotton canopy. After image-preprocessing, 46 hand features were extracted, and deep features were extracted by convolutional neural network (CNN). Partial least squares and Pearson were used for feature dimensionality reduction, respectively. Linear regression, support vector machine, and one-dimensional CNN regression models were constructed with manual features as input, and the deep features were used as inputs to construct a two-dimensional CNN regression model to achieve accurate prediction of cotton canopy nitrogen. It was verified that the manual feature and deep feature models constructed from UAV RGB images had good prediction effects. R 2 = 0.80 and RMSE = 1.67 g kg −1 of the Xinluzao 45 optimal model, and R 2 = 0.42 and RMSE = 3.13 g kg −1 of the Xinluzao 53 optimal model. The results show that the UAV RGB image and machine learning technology can be used to predict the nitrogen content of large-scale cotton, but due to insufficient data samples, the accuracy and stability of the prediction model still need to be improved.

Keywords: UAV-RGB image; image analysis; leaf nitrogen content; cotton; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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