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Machine learning-based crop recognition from aerial remote sensing imagery

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Abstract

Timely and accurate acquisition of crop distribution and planting area information is important for making agricultural planning and management decisions. This study employed aerial imagery as a data source and machine learning as a classification tool to statically and dynamically identify crops over an agricultural cropping area. Comparative analysis of pixel-based and object-based classifications was performed and classification results were further refined based on three types of object features (layer spectral, geometry, and texture). Static recognition using layer spectral features had the highest accuracy of 75.4% in object-based classification, and dynamic recognition had the highest accuracy of 88.0% in object-based classification based on layer spectral and geometry features. Dynamic identification could not only attenuate the effects of variations on planting dates and plant growth conditions on the results, but also amplify the differences between different features. Object-based classification produced better results than pixel-based classification, and the three feature sets (layer spectral alone, layer spectral and geometry, and all three) resulted in only small differences in accuracy in object-based classification. Dynamic recognition combined with object-based classification using layer spectral and geometry features could effectively improve crop classification accuracy with high resolution aerial imagery. The methodologies and results from this study should provide practical guidance for crop identification and other agricultural mapping applications.

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Acknowledgements

This work was supported by the National Key Research and Development Program (No. 2020YFD1100204), and the Provincial Key Basic Research Project (No. 2019AB002). The authors wish to thank Lee Denham and Fred Gomez at USDA-ARS in College Station, Texas for the collection of the airborne images. Mention of a commercial product is solely for the purpose of providing specific information and should not be construed as a product endorsement by the authors or the institutions with which the authors are affiliated.

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Authors and Affiliations

  1. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China

    Yanqin Tian, Wenjiang Huang, Jia Tang, Xingrong Li & Qing Zhang

  2. USDA-Agricultural Research Service, Aerial Application Technology Research Unit, College Station, TX, 77845, USA

    Chenghai Yang

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  1. Yanqin Tian
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  2. Chenghai Yang
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  3. Wenjiang Huang
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  4. Jia Tang
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Correspondence to Chenghai Yang or Qing Zhang.

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Tian, Y., Yang, C., Huang, W. et al. Machine learning-based crop recognition from aerial remote sensing imagery. Front. Earth Sci. 15, 54–69 (2021). https://doi.org/10.1007/s11707-020-0861-x

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