关键词: 烟叶成熟度, SPAD值, 机器学习, 图像特征, 即时预测

Abstract: As a key link in the process of tobacco harvest, maturity measurement should be immediate, scientific and accurate. There are some problems in the current implementation of tobacco maturity recognition. For example, the measurement instruments are expensive and cumbersome to operate, which cannot be promoted for use in the field; the embedded image processing algorithms in phone cameras interfere with the effective features of images and the complex weather environment in the field affects the consistency of image acquisition; existing recognition algorithms ignore botanical domain information, which affects model accuracy and universality. Accordingly, this paper proposes a low-cost and effective method to predict the SPAD value and the maturity of fresh tobacco leaves in the field immediately, which ensures the quality of subsequent modulation of tobacco leaves by improving the prediction accuracy. Firstly, this paper develops a portable shooting device to achieve high-quality image acquisition in the field and proposes a segmentation method adapted to tobacco images in the field taking the CX-26 as the research object. After that, this paper extracts the feature data of the image target area and builds a SPAD value prediction model and maturity identification model in turn using the XGBoost algorithm. Then, a model integration idea for two models is proposed, which is able to use the strong correlation between SPAD value and maturity to improve maturity identification accuracy by predicting SPAD values. The method performs well in all evaluation indexes, where the mean absolute error reaches 0.470 3 for SPAD value prediction and macro F1-Score reaches 95.27% for maturity identification. Finally, this paper develops an APP to realize the transmission of tobacco images and prediction results between the portable shooting device and the model to achieve fast, objective and accurate immediate prediction of tobacco maturity in the field. The results can provide effective technical support for the accurate harvest of crops in the field.

Key words: maturity of tobacco leaves, SPAD value, machine learning, image features, immediate prediction