农业工程学报2025，Vol.41Issue(10)：166-175,10.DOI:10.11975/j.issn.1002-6819.202502165
基于分数阶微分和无人机高光谱指数优选的油菜产量预测
Rapeseed yield prediction based on fractional-order differentiation and UAV hyperspectral index optimization
摘要
Abstract
Rapeseed is one of the most important raw materials of edible vegetable oil.An accurate and timely yield prediction is crucial to national food and oil security.Unmanned aerial vehicle(UAV)hyperspectral technology can be expected to effectively enhance the data acquisition in traditional satellite remote sensing.A large volume of continuous narrow-band spectral data can also be captured to accurately characterize the physiological and biochemical features of the crops.In this study,the UAV platforms were utilized to capture the hyperspectral images during the flowering stage of the rapeseed.A yield prediction model was constructed using fractional order differentiation(FOD)and multi-band spectral indices.A systematic prediction of the yield was also evaluated on these spectral indices.Firstly,the FOD processing was applied to the hyperspectral data of the rapeseed canopy,and then two-dimensional(2D)and three-dimensional(3D)spectral indices were calculated using different order differential data;Secondly,Pearson correlation coefficient was utilized to examine the correlation between the spectral indices and yield observation.The most sensitive spectral indices were selected for the yield prediction;Finally,the support vector regression was employed to construct the yield prediction model using FOD spectral indices.A systematic investigation was carried out to evaluate the impact of different differential orders and spectral indexes on the prediction accuracy.The results indicate that the FOD processing enhanced the spectral characteristics of the red edge and yellow-green bands during the flowering stage of rapeseed.The potential spectral information was effectively extracted to preserve the original structure of the vegetation spectral curve.The correlation analysis showed that there was a generally low correlation between FOD spectral data and yield at the lower orders.The increase was observed at the higher orders.The excessively high orders(e.g.,2.0)were selected to introduce the noise into the spectral data,which reduced the correlation.Three types of the 3D spectral indices exhibited correlation coefficients of 0.77 with the yield,which were significantly higher than those of the 2D ones.The 2D spectral index with the FOD shared the highest correlation at an order of 1.8,with a correlation coefficient of 0.868,whereas the 3D spectral index shared the highest correlation at an order of 1.6,with a correlation coefficient of 0.887.The estimation of the yield was also carried out with the different spectral indices.Furthermore,the indices derived from the blue,green,and near-infrared bands were the most sensitive to the prediction of the rapeseed yield.The third spectral dimension in the 3D spectral index greatly contributed to the full utilization of the rich information in hyperspectral data.The yield prediction model with the 3D spectral index also outperformed that with the 2D spectral index.The R2 values of the 3D and 2D spectral index ranged from 0.880 to 0.897 and from 0.624 to 0.896,respectively.The high accuracy and robustness were achieved in the yield prediction model using FOD with the multi-dimensional spectral indices.The high-precision early estimation of the yield also provided valuable scientific support to agricultural production.Future research should further explore the impact of rapeseed varieties,growth stages,and environmental conditions on yield prediction with the FOD spectral index.The potential application can also be extended to other crops.Additionally,future studies should explore more to minimize the noise impact in the multi-order differentiation,and then balance the trade-off between spectral resolution,spectral intensity,and noise.The more robust models can provide the data support for rapid,accurate,and early yield prediction.关键词
油菜/产量/高光谱影像/分数阶微分/无人机/光谱指数Key words
rapeseed/yield/hyperspectral image/fractional-order differentiation(FOD)/unmanned aerial vehicle(UAV)/spectral index分类
农业科技引用本文复制引用
马瑜蔓,宋茜,段博,徐宾灿,邹冉,石宇辰,余强毅,史云,陆苗,吴文斌..基于分数阶微分和无人机高光谱指数优选的油菜产量预测[J].农业工程学报,2025,41(10):166-175,10.基金项目
国家重点研发计划项目(2021YFD1600500) （2021YFD1600500）
新疆维吾尔自治区科技项目(2022 LQ02004,2023B02014-2) （2022 LQ02004,2023B02014-2）
北京市自然科学基金项目(6242030) （6242030）
中国农业科学院创新工程(GY2025-22-8) （GY2025-22-8）
