Adaboost-Based Machine Learning Improved the Modeling Robust and Estimation Accuracy of Pear Leaf Nitrogen Concentration by In-Field VIS-NIR Spectroscopy

Sensors

Wei Xue¹  Xiaojun Shi²  Jie Wang²  Yangchun Xu³  Caixia Dong³ 

[1] College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China;College of Resources and Environment, Southwest University, Chongqing 400716, China;College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China;

关键词: mixed cultivars;    VIS-NIR spectroscopy;    Adaboost;    support vector regression;    back-propagation neural networks;   

DOI  :  10.3390/s21186260

来源: DOAJ

【 摘 要 】

Different cultivars of pear trees are often planted in one orchard to enhance yield for its gametophytic self-incompatibility. Therefore, an accurate and robust modelling method is needed for the non-destructive determination of leaf nitrogen (N) concentration in pear orchards with mixed cultivars. This study proposes a new technique based on in-field visible-near infrared (VIS-NIR) spectroscopy and the Adaboost algorithm initiated with machine learning methods. The performance was evaluated by estimating leaf N concentration for a total of 1285 samples from different cultivars, growth regions, and tree ages and compared with traditional techniques, including vegetation indices, partial least squares regression, singular support vector regression (SVR) and neural networks (NN). The results demonstrated that the leaf reflectance responded to the leaf nitrogen concentration were more sensitive to the types of cultivars than to the different growing regions and tree ages. Moreover, the AdaBoost.RT-BP had the best accuracy in both the training (R² = 0.96, root mean relative error (RMSE) = 1.03 g kg⁻¹) and the test datasets (R² = 0.91, RMSE = 1.29 g kg⁻¹), and was the most robust in repeated experiments. This study provides a new insight for monitoring the status of pear trees by the in-field VIS-NIR spectroscopy for better N managements in heterogeneous pear orchards.

【 授权许可】

Unknown