李岚涛,任涛,汪善勤,明金,刘秋霞,鲁剑巍.基于角果期高光谱的冬油菜产量预测模型研究[J].农业机械学报,2017,48(3):221-229.
LI Lantao,REN Tao,WANG Shanqin,MING Jin,LIU Qiuxia,LU Jianwei.Prediction Models of Winter Oilseed Rape Yield Based on Hyperspectral Data at Pod-filling Stage[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(3):221-229.
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基于角果期高光谱的冬油菜产量预测模型研究   [下载全文]
Prediction Models of Winter Oilseed Rape Yield Based on Hyperspectral Data at Pod-filling Stage   [Download Pdf][in English]
投稿时间:2016-07-11  
DOI:10.6041/j.issn.1000-1298.2017.03.028
中文关键词:  冬油菜  角果期  产量  预测模型  高光谱  偏最小二乘回归
基金项目:国家自然科学基金项目(31471941)和国家油菜产业体系建设专项(CARS-13)
作者单位
李岚涛 华中农业大学 
任涛 华中农业大学 
汪善勤 农业部长江中下游耕地保育重点实验室 
明金 农业部长江中下游耕地保育重点实验室 
刘秋霞 华中农业大学 
鲁剑巍 华中农业大学 
中文摘要:以连续3a田间氮肥水平试验为基础,研究基于高光谱估产的可行性,明确最佳光谱监测方式和有效波段,降低光谱分析维数,提高产量估测时效性。2013—2016年分别于湖北省武穴市和沙洋县进行大田试验,通过测试角果期冠层光谱反射率、产量构成因子(单株角果数、每角粒数和千粒质量)和成熟期产量,利用偏最小二乘回归(PLS)分别对油菜原初光谱(RSR)和一阶微分光谱(FDR)与其产量及构成因子间构建定量分析模型并筛选有效波段。结果表明,基于全波段的FDR-PLS模型预测精度显著优于R-PLS,其最佳监测指标是冬油菜产量和角果数,验证集决定系数(R2)分别为0.90和0.91,均方根误差(RMSE)分别为379kg/hm2和66个/株,相对分析误差(RPD)分别为3.11和3.12。基于各波段变量重要性投影(VIP)值,确定冬油菜产量有效波段分别为628、753、882、935、1061、1224nm;角果数有效波段分别为628、758、935、1063、1457、1600nm。此后,再次构建基于上述有效波段的冬油菜产量和角果数监测模型,决定系数分别为0.91和0.87,均方根误差分别为504kg/hm2和82个/株,相对分析误差分别为2.34和2.52,估算精度较为理想。
LI Lantao  REN Tao  WANG Shanqin  MING Jin  LIU Qiuxia  LU Jianwei
Huazhong Agcicultural University,Huazhong Agcicultural University,Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtse River)
, Ministry of Agriculture,Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtse River)
, Ministry of Agriculture,Huazhong Agcicultural University and Huazhong Agcicultural University
Key Words:winter oilseed rape  pod-filling stage  yield  prediction model  hyperspectral  partial least square (PLS)
Abstract:Hyperspectral remote sensing can provide a non-destructive and effective approach for assessing the yield and yield components of oilseed rape timely. A quantitative technique was developed to estimate oilseed rape yield accurately depending on ground-based canopy reflectance spectra. Field experiments were conducted over three growing seasons at different sites (Wuxue and Shayang) in Hubei Province, China. The key parameters, including canopy hyperspectral reflectance during pod-filling period, seed yield and yield components (pod numbers per plant, seed numbers per pod and 1000 seed weight) were monitored. A partial least square (PLS) regression analysis was employed to perform the relationship between raw spectral reflectance (RSR), the first derivative reflectance (FDR) and seed yield and yield components. According to the calibration dataset, the best results were obtained with the FDR-PLS model for the prediction of yield and pod number, which yielded the highest coefficient of determination (R2cal) of 0.96 and 0.98, and the lowest root mean square error (RMSEcal) of 158kg/hm2 and 17 pods/plant, respectively. The tests using the independent validation dataset also showed that the FDR-PLS model could well forecast yield and pod number of winter oilseed rape, with values of R2val of 0.90 and 0.91, RMSEval of 379kg/hm2 and 66 pods/plant, and RPD of 3.11 and 3.12, respectively. The variable importance in projection (VIP) scores resulted from the PLS regression analysis were used to determine the effective wavelengths and reduce the dimensionality of the spectral reflectance data. The newly-developed FDR-PLS model using the effective wavelengths (628nm, 753nm, 882nm, 935nm, 1061nm and 1224nm) performed well in yield prediction with R2val of 0.91, RMSE val of 504kg/hm2 and RPDval of 2.34;Similar results were also obtained for pod number prediction with R2val of 0.87, RMSEval of 82 pods/plant and RPDval of 2.52 using the effective wavelengths (628nm, 758nm, 935nm, 1063nm, 1457nm and 1600nm). Consequently, the yield of winter oilseed rape could be reliably estimated with the in situ developed FDR-PLS method.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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