Estimation of Relative Chlorophyll Content in Spring Wheat Based on Multi-Temporal UAV Remote Sensing
نویسندگان
چکیده
Relative chlorophyll content (SPAD) is an important index for characterizing the nitrogen nutrient status of plants. Continuous, rapid, nondestructive, and accurate estimation SPAD values in wheat after heading stage can positively impact subsequent fertilization management strategies, which regulate grain filling yield quality formation. In this study, leaf relative spring was conducted at experimental base Wuyuan County, Inner Mongolia 2021. Multispectral images different application levels 7, 14, 21, 28 days were acquired by DJI P4M UAV. A total 26 multispectral vegetation indices constructed, measured on ground obtained simultaneously using a handheld meter. Four machine learning algorithms, including deep neural networks (DNN), partial least squares (PLS), random forest (RF), Adaptive Boosting (Ada) used to construct value models time from growth stages. The model’s progress evaluated coefficient determination (R2), root mean square error (RMSE), absolute (MAPE). results showed that optimal periods independent reproductive stages different, with PLS as model 7 14 heading, RF 21 Ada d heading. highest accuracy achieved estimating (training set R2 = 0.767, RMSE 3.205, MAPE 0.060, 0.878, 2.405, 0.045 test set). combined analysis concluded selecting multiple input variables significantly improve estimation, provides new technical support rapid monitoring wheat.
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ژورنال
عنوان ژورنال: Agronomy
سال: 2023
ISSN: ['2156-3276', '0065-4663']
DOI: https://doi.org/10.3390/agronomy13010211