Study on the application of MF-DMA model in the diagnosis of leaf nitrogen nutrient

Jianhui Li; Manlan Liu; Chengwen Li; Han Yang; Xuesong Wang

doi:10.1117/12.2674723

23 May 2023 Study on the application of MF-DMA model in the diagnosis of leaf nitrogen nutrient

Jianhui Li, Manlan Liu, Chengwen Li, Han Yang, Xuesong Wang

Proceedings Volume 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022); 126040O (2023) https://doi.org/10.1117/12.2674723
Event: 2nd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 2022, Guangzhou, China

Abstract

By analyzing the texture characteristics of rapeseed leaves, this paper proposes a multifractal detrended moving average analysis (MF-DMA) model to identify different nitrogen application levels. The model adopts several global generalized Hurst exponents and some other related multifractal feature parameters of rape leaf images under different λ values, and uses different feature parameter combinations to identify nitrogen nutrition in basal leaves, middle leaves and top leaves, respectively. The results show that the recognition effect is the best when λ=0, and when λ=0, the basal leaves and middle leaves are more sensitive to nitrogen than the top leaves. Through qualitative identification of three parts of rapeseed samples with moderate nitrogen application and deficient nitrogen application, the results showed that the two classification methods of support vector machine and random forest were the best, and the best identification accuracy was 96.01% and 96.83%, respectively. It shows that the model proposed in this paper has good effectiveness.

Citation Download Citation

Jianhui Li, Manlan Liu, Chengwen Li, Han Yang, and Xuesong Wang "Study on the application of MF-DMA model in the diagnosis of leaf nitrogen nutrient", Proc. SPIE 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 126040O (23 May 2023); https://doi.org/10.1117/12.2674723

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