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Alfalfa (Medicago sativa L.) crop vigor and yield characterization using high-resolution aerial multispectral and thermal infrared imaging technique
Computers and Electronics in Agriculture ( IF 8.3 ) Pub Date : 2021-02-15 , DOI: 10.1016/j.compag.2021.105999
Abhilash K. Chandel , Lav R. Khot , Long-Xi Yu

Alfalfa (Medicago sativa L.) is an important forage crop grown worldwide for animal feed, green manure, and as a land cover. However, very few approaches exist for timely field scale mapping of crop status, yield and quality attributes for management of inputs, harvest and storage resources, budgeting, crop insurance, etc. This study aims to apply high-resolution aerial multispectral and thermal infrared remote sensing (7 cm/pixel) to characterize above crop attributers. Imaged were two crop cutting cycles in 2018 season. Eight crop vigor index (VI) and a Crop Water Stress Index (CWSI) features were derived from collected imagery data. Modified Non-Linear Index (MNLI), Modified Simple Ratio (MSR) and CWSI reliably evaluated the spatial variations in crop vigor and stress traits (Coefficient of variation [CV] in the ranges of 24–69%). Yield was then predicted with indices as predictor variables through nine simple linear regression (LRs, variable: one image feature per model), seven multiple linear regression (MLRs, variables: one VI and CWSI per model), a stepwise linear regression (SLR), a partial least square regression (PLSR) and a least absolute shrinkage and selection operator (LASSO) models. The SLR, PLSR and LASSO initially used all image features for model training. Amongst simple models, MLR-4 (Variables: MNLI and CWSI) performed the best (Root mean square error [RMSE] = 0.45 kg, R2 = 0.64) and LR-5 (Variable: MNLI) was the second-best model (RMSE = 0.51 kg, R2 = 0.54). The complex SLR, PLSR and LASSO models predicted yield with similar accuracy as MLR-4 (RMSE in the ranges of 0.45–0.46 kg, R2 in the ranges of 0.63–0.64). MNLI (canopy vigor) and CWSI (stress) were significant and sufficient for effective alfalfa crop status and yield prediction for their non-saturation and non-linearity features. Overall, high-resolution aerial remote sensing in the visible-NIR and thermal infrared domain showed potential for site-specific crop monitoring.



中文翻译:

苜蓿(紫花苜蓿大号)作物活力和产量的表征利用高分辨率的空中多光谱和热红外成像技术

苜蓿(苜蓿L.)是世界范围内重要的饲料作物,可用于饲料,绿肥和土地覆盖。但是,很少有方法可以对作物状态,产量和质量属性进行及时的田间规模制图,以管理投入,收获和储存资源,预算,作物保险等。本研究旨在应用高分辨率的空中多光谱和热红外遥测仪感应(7厘米/像素)来表征以上作物属性。成像的是2018季的两个作物切割周期。从收集的图像数据中得出了八个作物活力指数(VI)和一个作物水分胁迫指数(CWSI)特征。改良的非线性指数(MNLI),改良的简单比率(MSR)和CWSI可靠地评估了作物活力和胁迫性状的空间变化(变异系数[CV]在24-69%的范围内)。然后通过九个简单线性回归(LR,变量:每个模型一个图像特征),七个多元线性回归(MLR,变量:每个模型一个VI和CWSI),逐步线性回归(SLR),以指标作为预测变量来预测产量。 ,偏最小二乘回归(PLSR)和最小绝对收缩与选择算子(LASSO)模型。SLR,PLSR和LASSO最初使用所有图像特征进行模型训练。在简单模型中,MLR-4(变量:MNLI和CWSI)表现最佳(均方根误差[RMSE] = 0.45 kg,偏最小二乘回归(PLSR)和最小绝对收缩与选择算子(LASSO)模型。SLR,PLSR和LASSO最初使用所有图像特征进行模型训练。在简单模型中,MLR-4(变量:MNLI和CWSI)表现最佳(均方根误差[RMSE] = 0.45 kg,偏最小二乘回归(PLSR)和最小绝对收缩与选择算子(LASSO)模型。SLR,PLSR和LASSO最初使用所有图像特征进行模型训练。在简单模型中,MLR-4(变量:MNLI和CWSI)表现最佳(均方根误差[RMSE] = 0.45 kg,R 2  = 0.64)和LR-5(变量:MNLI)是第二好的模型(RMSE = 0.51 kg,R 2  = 0.54)。复杂的SLR,PLSR和LASSO模型预测的产量与MLR-4相似(RMSE在0.45-0.46 kg范围内,R 2在0.63-0.64范围内)。MNLI(冠层活力)和CWSI(胁迫)由于其非饱和和非线性特征,对于有效的苜蓿作物状况和产量预测具有重要意义和充分意义。总体而言,可见光-近红外和热红外领域的高分辨率航空遥感显示了针对特定地点的作物监测的潜力。

更新日期:2021-02-16
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