Image recognition of flue-cured tobacco harvest maturity based on XGBoost algorithm
[Objective]This study aims to achieve intelligent and accurate identification of tobacco harvest maturity.[Methods]Taking Yunyan 87 as the test material,we extracted image features using OpenCV and GLCM,and constructed XGBoost algorithm model so as to realize the maturity recognition of fresh tobacco leaves.[Results]① In the image features of fresh tobacco leaves,the components R(red),G(green),B(blue),and ASM(Angular Second Moment)showed a significant rising trend with the increase of maturity,while other image features did not change significantly;② After stepwise screening of F-score,AUC value(Area Under the Receiver Operating Characteristic Curve),and accuracy rate,five feature parameters including R1(mean value of R component),G1(mean value of G component),B1(mean value of B component),S2(variance of S component),and B2(variance of B component)were selected.The XGBoost algorithm model established based on these features achieved an accuracy rate of 95.85％in identifying tobacco leaf maturity,which is 0.41％higher than the model with 22-dimensional feature parameters and 2.72％higher than the BP neural network model.[Conclusion]The XGBoost algorithm based on machine vision can accurately and efficiently identify the maturity of fresh tobacco leaves.
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