Estimation of total nitrogen in young Aquilaria sinensis based on multi image features

YUAN Ying, WANG Xuefeng, WANG Tian, CHEN Feifei, HUANG Chuanteng, LIN Ling, DONG Xiaona

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (3) : 19-28.

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南京林业大学学报（自然科学版）
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JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (3) : 19-28. DOI: 10.12302/j.issn.1000-2006.202107017

Estimation of total nitrogen in young Aquilaria sinensis based on multi image features

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Abstract

【Objective】Multi-image features of young Aquilaria sinensis were extracted using computer vision technology to estimate the total nitrogen content of leaves, providing a new method for rapid and nondestructive measurement of the nitrogen nutritional status of A. sinensis. 【Method】In this study, the best histogram entropy method (KSW entropy method) and morphological processing based on the HIS (Hue-intensity-saturation) color space were used to segment an image of young A. sinensis, and the color, shape and textural features of the image were extracted. Subsequently, the partial least squares (PLS) method was used to reduce the multi-image feature dimensions, and the principal components of the image feature variables were generated. Finally, the Elman neural network (ElmanNN), optimized using the BAS algorithm, was used to estimate the total nitrogen content of young A. sinensis, and the validation results of the model were compared with those of other commonly used models. 【Result】Research showed the following: (1) focusing on the visible image of A. sinensis, the segmentation algorithm based on HIS color space was better than that based on RGB and Lab color space. (2) The PLS algorithm extracted six principal components from the image features, which reduced the dimension of the image features quickly, and effectively eliminated the multicollinearity among the feature variables. (3) The PLS-BAS-ElmanNN model proposed in this study could achieve the adaptive selection of model parameters, and had higher estimation accuracy; for instance, the R2 was 0.740 7 and the root mean square error was only 1.265 3 g/kg. The estimation accuracy of it was slightly higher than that of the PLSR and PLS-GAM models. 【Conclusion】In this study, we proposed an image processing method for young A. sinensis and constructed a PLS-BAS-ElmanNN estimation model that can stably process high-dimensional image data. This provides a new idea for monitoring the nitrogen nutrition status of young A. sinensis and has a very important practical significance for the accurate cultivation of A. sinensis.

Key words

Aquilaria sinensis / total nitrogen / computer vision / BAS-Elman / PLS

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YUAN Ying , WANG Xuefeng , WANG Tian , et al . Estimation of total nitrogen in young Aquilaria sinensis based on multi image features[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2023, 47(3): 19-28 https://doi.org/10.12302/j.issn.1000-2006.202107017

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