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Predicting Leaf Nitrogen Content in Cotton with UAV RGB Images

Author

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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
    College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
    These authors contributed equally to this work.)

  • Long Duan

    (College of Information Science and Technology, Shihezi University, Shihezi 832003, China
    These authors contributed equally to this work.)

  • 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)

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.

Suggested Citation

  • Jinmei Kou & Long Duan & Caixia Yin & Lulu Ma & Xiangyu Chen & Pan Gao & Xin Lv, 2022. "Predicting Leaf Nitrogen Content in Cotton with UAV RGB Images," Sustainability, MDPI, vol. 14(15), pages 1-10, July.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9259-:d:874227
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    Citations

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    Cited by:

    1. Peipei Chen & Jianguo Dai & Guoshun Zhang & Wenqing Hou & Zhengyang Mu & Yujuan Cao, 2024. "Diagnosis of Cotton Nitrogen Nutrient Levels Using Ensemble MobileNetV2FC, ResNet101FC, and DenseNet121FC," Agriculture, MDPI, vol. 14(4), pages 1-18, March.
    2. Chunfeng Gao & Xingjie Ji & Qiang He & Zheng Gong & Heguang Sun & Tiantian Wen & Wei Guo, 2023. "Monitoring of Wheat Fusarium Head Blight on Spectral and Textural Analysis of UAV Multispectral Imagery," Agriculture, MDPI, vol. 13(2), pages 1-16, January.
    3. Hajar Hammouch & Suchitra Patil & Sunita Choudhary & Mounim A. El-Yacoubi & Jan Masner & Jana Kholová & Krithika Anbazhagan & Jiří Vaněk & Huafeng Qin & Michal Stočes & Hassan Berbia & Adinarayana Jag, 2024. "Hybrid-AI and Model Ensembling to Exploit UAV-Based RGB Imagery: An Evaluation of Sorghum Crop’s Nitrogen Content," Agriculture, MDPI, vol. 14(10), pages 1-15, September.
    4. Liyuan Zhang & Xiaoying Song & Yaxiao Niu & Huihui Zhang & Aichen Wang & Yaohui Zhu & Xingye Zhu & Liping Chen & Qingzhen Zhu, 2024. "Estimating Winter Wheat Plant Nitrogen Content by Combining Spectral and Texture Features Based on a Low-Cost UAV RGB System throughout the Growing Season," Agriculture, MDPI, vol. 14(3), pages 1-13, March.

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