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The Classification of Farming Progress in Rice–Wheat Rotation Fields Based on UAV RGB Images and the Regional Mean Model

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  • Xiaoxin Song

    (Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, China
    Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China)

  • Fei Wu

    (Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, China
    Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China)

  • Xiaotong Lu

    (Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, China
    Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China)

  • Tianle Yang

    (Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, China
    Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China)

  • Chengxin Ju

    (Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, China
    Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China)

  • Chengming Sun

    (Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, China
    Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China)

  • Tao Liu

    (Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, China
    Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China)

Abstract

Extraction of farming progress information in rice–wheat rotation regions is an important topic in smart field research. In this study, a new method for the classification of farming progress types using unmanned aerial vehicle (UAV) RGB images and the proposed regional mean (RM) model is presented. First, RGB information was extracted from the images to create and select the optimal color indices. After index classification, we compared the brightness reflection of the corresponding grayscale map, the classification interval, and the standard deviation of each farming progress type. These comparisons showed that the optimal classification color indices were the normalized red–blue difference index (NRBDI), the normalized green–blue difference index (NGBDI), and the modified red–blue difference index (MRBDI). Second, the RM model was built according to the whole-field farming progress classification requirements to achieve the final classification. We verified the model accuracy, and the Kappa coefficients obtained by combining the NRBDI, NGBDI, and MRBDI with the RM model were 0.86, 0.82, and 0.88, respectively. The proposed method was then applied to predict UAV RGB images of unharvested wheat, harvested wheat, and tilled and irrigated fields. The results were compared with those obtained with traditional machine learning methods, that is, the support vector machine, maximum likelihood classification, and random forest methods. The NRBDI, NGBDI, and MRBDI were combined with the RM model to monitor farming progress of ground truth ROIs, and the Kappa coefficients obtained were 0.9134, 0.8738, and 0.9179, respectively, while traditional machine learning methods all produced a Kappa coefficient less than 0.7. The results indicate a significantly higher accuracy of the proposed method than those of the traditional machine learning classification methods for the identification of farming progress type. The proposed work provides an important reference for the application of UAV to the field classification of progress types.

Suggested Citation

  • Xiaoxin Song & Fei Wu & Xiaotong Lu & Tianle Yang & Chengxin Ju & Chengming Sun & Tao Liu, 2022. "The Classification of Farming Progress in Rice–Wheat Rotation Fields Based on UAV RGB Images and the Regional Mean Model," Agriculture, MDPI, vol. 12(2), pages 1-16, January.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:2:p:124-:d:727226
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    References listed on IDEAS

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    1. Tan, Shuhao & Heerink, Nico & Kruseman, Gideon & Qu, Futian, 2008. "Do fragmented landholdings have higher production costs? Evidence from rice farmers in Northeastern Jiangxi province, P.R. China," China Economic Review, Elsevier, vol. 19(3), pages 347-358, September.
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    Cited by:

    1. Wenjing Zhu & Zhankang Feng & Shiyuan Dai & Pingping Zhang & Xinhua Wei, 2022. "Using UAV Multispectral Remote Sensing with Appropriate Spatial Resolution and Machine Learning to Monitor Wheat Scab," Agriculture, MDPI, vol. 12(11), pages 1-16, October.

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