scholarly journals Entropy Weight Ensemble Framework for Yield Prediction of Winter Wheat Under Different Water Stress Treatments Using Unmanned Aerial Vehicle-Based Multispectral and Thermal Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Shuaipeng Fei ◽  
Muhammad Adeel Hassan ◽  
Yuntao Ma ◽  
Meiyan Shu ◽  
Qian Cheng ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Winter Wheat ◽  
Mean Square Error ◽  
Unmanned Aerial Vehicle ◽  
Grain Filling ◽  
Growth Stages ◽  
Yield Prediction ◽  
Mean Square ◽  
Entropy Weight ◽  
Aerial Vehicle

Crop breeding programs generally perform early field assessments of candidate selection based on primary traits such as grain yield (GY). The traditional methods of yield assessment are costly, inefficient, and considered a bottleneck in modern precision agriculture. Recent advances in an unmanned aerial vehicle (UAV) and development of sensors have opened a new avenue for data acquisition cost-effectively and rapidly. We evaluated UAV-based multispectral and thermal images for in-season GY prediction using 30 winter wheat genotypes under 3 water treatments. For this, multispectral vegetation indices (VIs) and normalized relative canopy temperature (NRCT) were calculated and selected by the gray relational analysis (GRA) at each growth stage, i.e., jointing, booting, heading, flowering, grain filling, and maturity to reduce the data dimension. The elastic net regression (ENR) was developed by using selected features as input variables for yield prediction, whereas the entropy weight fusion (EWF) method was used to combine the predicted GY values from multiple growth stages. In our results, the fusion of dual-sensor data showed high yield prediction accuracy [coefficient of determination (R2) = 0.527–0.667] compared to using a single multispectral sensor (R2 = 0.130–0.461). Results showed that the grain filling stage was the optimal stage to predict GY with R2 = 0.667, root mean square error (RMSE) = 0.881 t ha–1, relative root-mean-square error (RRMSE) = 15.2%, and mean absolute error (MAE) = 0.721 t ha–1. The EWF model outperformed at all the individual growth stages with R2 varying from 0.677 to 0.729. The best prediction result (R2 = 0.729, RMSE = 0.831 t ha–1, RRMSE = 14.3%, and MAE = 0.684 t ha–1) was achieved through combining the predicted values of all growth stages. This study suggests that the fusion of UAV-based multispectral and thermal IR data within an ENR-EWF framework can provide a precise and robust prediction of wheat yield.

The effects of CO2 database on a localized AquaCrop model construction based on the field experiment

2021 ◽  
Author(s):  
fawen li ◽  
chunya song ◽  
hua li
Keyword(s):  
Root Mean Square Error ◽  
Winter Wheat ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Mean Square Error ◽  
North China Plain ◽  
North China ◽  
Mean Square ◽  
Simulation Results

Abstract To examine whether the use of default CO2 database affected the simulation results, this paper built the AquaCrop models of winter wheat based on the measured CO2 database and the default CO2 database, respectively. The models were calibrated with data (2017–2018) and validated with the data (2018–2019) in the North China Plain. The residual coefficient method (CRM), root mean square error (RMSE), normalized root mean square error (NRMSE) and determination coefficient (R2) were used to test the model performance. The results showed that the accuracy of simulation under the two CO2 database were both good. Compared with the default CO2 database, the simulation accuracy under the measured CO2 database had higher accuracy. In order to verify the model further, the simulated values of evapotranspiration, soil water content and measured values were compared and analyzed. The results showed that there were some errors between the measured evapotranspiration and the values of simulation in the filling and waxing period of winter wheat. In general, the simulation values of evapotranspiration were consistent with the measured value at different irrigation levels. The simulated values ​​of the soil water content at the three levels of irrigation were all higher than the measured values, but the simulated results basically reflected the dynamic changes of soil water content throughout the growth period. The model adjustment value of WP*(the normalized water productivity) were a difference under the two CO2 databases, which is one of the reasons for the difference in the simulation results. The results show that in the absence of measured CO2 data, the default CO2 database can be used, which has little influence on the model construction, and the accuracy of the model constructed meets the actual demand. The research results can provide a basis for the establishment of crop models in North China Plain.

scholarly journals Potential of Multivariate Statistical Technique Based on the Effective Spectra Bands to Estimate the Plant Water Content of Wheat Under Different Irrigation Regimes

2021 ◽  
Vol 12 ◽  
Author(s):  
Hui Sun ◽  
Meichen Feng ◽  
Lujie Xiao ◽  
Wude Yang ◽  
Guangwei Ding ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Winter Wheat ◽  
Water Content ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Coefficient Of Determination ◽  
Plant Water ◽  
Mean Square ◽  
Proximal Sensing ◽  
Sensing Data

Real-time, nondestructive, and accurate estimation of plant water status is important to the precision irrigation of winter wheat. The objective of this study was to develop a method to estimate plant water content (PWC) by using canopy spectral proximal sensing data. Two experiments under different water stresses were conducted in 2014–2015 and 2015–2016. The PWC and canopy reflectance of winter wheat were collected at different growth stages (the jointing, booting, heading, flowering, and filling stages in 2015 and the jointing, booting, flowering, and filling stages in 2016). The performance of different spectral transformation approaches was further compared. Based on the optimal pretreatment, partial least squares regression (PLSR) and four combination methods [i.e., PLSR-stepwise regression (SR), PLSR-successive projections algorithm (SPA), PLSR-random frog (RF), and PLSR-uninformative variables elimination (UVE)] were used to extract the sensitive bands of PWC. The results showed that all transformed spectra were closely correlated to PWC. The PLSR models based on the first derivative transformation method exhibited the best performance (coefficient of determination in calibration, R2C = 0.96; root mean square error in calibration, RMSEC = 20.49%; ratio of performance to interquartile distance in calibration, RPIQC = 9.19; and coefficient of determination in validation, R2V = 0.86; root mean square error in validation, RMSEV = 46.27%; ratio of performance to interquartile distance in validation, RPIQV = 4.34). Among the combination models, the PLSR model established with the sensitive bands from PLSR-RF demonstrated a good performance for calibration and validation (R2C = 0.99, RMSEC = 11.53%, and RPIQC = 16.34; and R2V = 0.84, RMSEV = 44.40%, and RPIQV = 4.52, respectively). This study provides a theoretical basis and a reference for estimating PWC of winter wheat by using canopy spectral proximal sensing data.

scholarly journals PENGARUH SEBARAN DATA GROUND CONTROL POINT (GCP) DALAM PENGOLAHAN DATA FOTO UDARA PADA AREA IN-PIT MAPPING MENGGUNAKAN DRONE QUADCOPTER DJI PHANTOM 4 RTK BERBASIS BASE GPS METODE REAL TIME KINEMATIC (RTK)

2020 ◽  
Vol 1 (1) ◽  
pp. 191-200
Author(s):  
Ryan Nugraha ◽  
Sigit Putrasakti
Keyword(s):  
Root Mean Square Error ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Control Point ◽  
Ground Control ◽  
Horizontal Position ◽  
Mean Square ◽  
Ground Control Point ◽  
Real Time Kinematic ◽  
Aerial Vehicle

ABSTRAKTeknik pengambilan foto udara yang saat ini sedang berkembang, tidak bisa dipungkiri lagi bahwa teknologi Unmanned Aerial Vehicle (UAV), khususnya drone merupakan salah satu teknologi yang sangat efektif dan efisien dalam melakukan kegiatan mapping (pemetaan). Kegiatan mapping menggunakan drone ini juga tidak luput dari industri pertambangan, khususnya tambang batu bara yang saat ini mulai popular menggunakan salah satu teknologi yang modern ini. Salah satu jenis UAV yang digunakan PT Arutmin Indonesia adalah drone quadcotper DJI Phantom 4 RTK yang berbasis base GPS metode Real Time Kinematic (RTK). Kegiatan mapping menggunakan drone diperlukan beberapa titik ikat atau kontrol di permukaan tanah yang disebar di area mapping yang dikenal dengan Ground Control Point (GCP). GCP berfungsi sebagai titik ikat atau kontrol di permukaan tanah. Sebaiknya GCP disebar merata di permukaan tanah area mapping yang areanya bebas dari obstacles, dan tidak mengganggu kegiatan penambangan agar hasil dari pengolahan data diharapkan menghasilkan data orthophoto dan kontur topografi yang presisi dan akurat. Kegiatan mapping yang dilakukan PT Arutmin Indonesia ini dilakukan di area in pit dump dengan sebaran enam data GCP yang disebar di ujung-ujung dan tengah batasan area mapping. GCP yang tidak di sebar merata di area mapping akan menghasilkan data orthophoto dan kontur topografi yang tidak presisi dan akurat. Ini disebabkan adanya area mapping yang tidak terikat/terkontrol oleh GCP. Area mapping yang tidak tercover GCP, dominan orthophoto yang dihasilkan tidak sesuai dengan aktual kondisi in pit dump. Orthophoto in pit dump ini, keadaan bench dump akan terlihat tidak lurus atau terpisah atau tidak menyambung karena posisi horizontal yang dihasilkan tidak presisi dan akurat. Begitu juga dengan data topografi, apabila area mapping tidak tercover GCP, akan menimbulkan variance +/- 5-10 m pada posisi horizontal (easting dan northing) dan 3-5 m pada posisi vertical (elevation). Dengan demikian data GCP yang disebar merata di area mapping merupakan salah satu parameter untuk menghasilkan data orthophoto dan kontur yang presisi dan akurat. GCP yang disebar merata di area mapping akan memberikan pengaruh terhadap ketelitian rektifikasi yang ditunjukkan melalui nilai Root Mean Square Error (RMSE) ketelitian jarak dan posisi (koordinat). Kata Kunci: GCP, mapping, in pit dump, rektifikasi   ABSTRACT The technique of taking aerial photographs is currently developing, it is undeniable that the technology of Unmanned Aerial Vehicle (UAV), especially drones, is one of the technologies that is very effective and efficient in conducting mapping activities. Mapping activities using drones are also not spared from the mining industry, especially coal mining which is currently gaining popularity using one of these modern technologies. One type of UAV used by PT Arutmin Indonesia is the DJI Phantom 4 RTK quadcotper drone based on the GPS Real Time Kinematic (RTK) method. Mapping activities using drones require a number of grounding points or controls that are spread out in a mapping area known as a Ground Control Point (GCP). GC Work as a bonding point or control at ground level. GCP should be distributed evenly on unobstructed mapping surface, and there is no mining activity so that the results of data processing are expected to produce precise and accurate orthophoto and topographic contour data. The mapping activity carried out by PT Arutmin Indonesia was carried out in an area in the pit dump with the distribution of six GCP data distributed at the edges and the mapping of the middle area. GCP that is not spread evenly in the mapping area will produce orthophoto data and topographic contours that are not precise and accurate. This represents the existence of an area mapping that is not approved / controlled by GCP. Mapping the area that is not covered by GCP, the dominant orthophoto produced is not in accordance with the actual conditions in the pit dump. Orthophoto in this pit dump, the state of the dump bench will look not straight or separate or not connect because the resulting horizontal position is not precise and accurate. Likewise with topographic data, mapping the rejected area is not covered by GCP, will cause variance +/- 5-10 m in the horizontal position (east and north) and 3-5 m vertical position (elevation). Thus GCP data distributed evenly in the mapping area is one of the parameters to produce precise and accurate orthophoto and contour data. GCP that is spread evenly in the mapping area will give effect to the accuracy of rectification studied through the value of Root Mean Square Error (RMSE) accuracy of distance and position (coordinates). Keywords: GCP, mapping, in pit dump, rectification

scholarly journals Estimating Leaf Nitrogen Content in Corn Based on Information Fusion of Multiple-Sensor Imagery from UAV

2021 ◽  
Vol 13 (3) ◽  
pp. 340
Author(s):  
Xingang Xu ◽  
Lingling Fan ◽  
Zhenhai Li ◽  
Yang Meng ◽  
Haikuan Feng ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Nitrogen Content ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Leaf Nitrogen ◽  
Growth Stages ◽  
Leaf Nitrogen Content ◽  
Multispectral Images ◽  
Mean Square ◽  
Rgb Images

With the rapid development of unmanned aerial vehicle (UAV) and sensor technology, UAVs that can simultaneously carry different sensors have been increasingly used to monitor nitrogen status in crops due to their flexibility and adaptability. This study aimed to explore how to use the image information combined from two different sensors mounted on an UAV to evaluate leaf nitrogen content (LNC) in corn. Field experiments with corn were conducted using different nitrogen rates and cultivars at the National Precision Agriculture Research and Demonstration Base in China in 2017. Digital RGB and multispectral images were obtained synchronously by UAV in the V12, R1, and R3 growth stages of corn, respectively. A novel family of modified vegetation indices, named coverage adjusted spectral indices (CASIs (CASI =VI/1+FVcover, where VI denotes the reference vegetation index and FVcover refers to the fraction of vegetation coverage), has been introduced to estimate LNC in corn. Thereby, typical VIs were extracted from multispectral images, which have the advantage of relatively higher spectral resolution, and FVcover was calculated by RGB images that feature higher spatial resolution. Then, the PLS (partial least squares) method was employed to investigate the relationships between LNC and the optimal set of CASIs or VIs selected by the RFA (random frog algorithm) in different corn growth stages. The analysis results indicated that whether removing soil noise or not, CASIs guaranteed a better estimation of LNC than VIs for all of the three growth stages of corn, and the usage of CASIs in the R1 stage yielded the best R2 value of 0.59, with a RMSE (root mean square error) of 22.02% and NRMSE (normalized root mean square error) of 8.37%. It was concluded that CASIs, based on the fusion of information acquired synchronously from both lower resolution multispectral and higher resolution RGB images, have a good potential for crop nitrogen monitoring by UAV. Furthermore, they could also serve as a useful way for assessing other physical and chemical parameters in further applications for crops.

scholarly journals THE CAPABILITIES OF UNMANNED AERIAL VEHICLE FOR SLOPE CLASSIFICATION

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 451-459
Author(s):  
N. M. Mokhtar ◽  
N. Darwin ◽  
M. F. M. Ariff ◽  
Z. Majid ◽  
K. M. Idris
Keyword(s):  
Root Mean Square Error ◽  
Unmanned Aerial Vehicle ◽  
Aerial Image ◽  
Mean Square ◽  
Suitability Analysis ◽  
Ground Survey ◽  
Digital Elevation ◽  
Check Points ◽  
Aerial Vehicle ◽  
Elevation Model

Abstract. Slope classification mapping is an important component of land suitability analysis for preventing landslides. This study aim to investigate the capabilities and application of Unmanned Aerial Vehicle (UAV) platform for slope classification. The objectives of this study such as investigating the capabilities of UAV for slope classification, generating Digital Elevation Model (DEM) and orthophoto from the image acquired and assessing the accuracy of DEM and orthophoto produced for slope classification. In this study, the aerial image was acquired using UAV at 60 m and 40 m altitude will then generates the DEM and orthophoto used to produce the slope map and classify the slope. The UAV data was validated with the check points observed from ground survey using GPS to obtain the Root Mean Square Error (RMSE) values. The RMSE value for UAV derived DEM at 60 m altitude is ±0.234 m and ±0.604 m for X and Y respectively. The average RMSE is ±0.279 m. The average RMSE value obtained from LiDAR derived DEM in previous research is ±0.616 m. The RMSE value for UAV derived DEM at 40 m altitude is ±0.596 m and ±0.405 for X and Y respectively. The average RMSE is ±0.334 m. The average RMSE value obtained from LiDAR derived DEM in previous research is ±0.450 m. In conclusion, it shows that the RMSE value obtained from UAV derived DEM is smaller than the RMSE value obtained from LiDAR derived DEM. Hence, UAV is capable for the generation of slope map and slope classification.

scholarly journals Plant phenomics & precision agriculture simulation of winter wheat growth by the assimilation of unmanned aerial vehicle imagery into the WOFOST model

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0246874
Author(s):  
Tianle Yang ◽  
Weijun Zhang ◽  
Tong Zhou ◽  
Wei Wu ◽  
Tao Liu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Leaf Area ◽  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Model Simulation ◽  
Growth Stages ◽  
Wheat Growth ◽  
Area Index ◽  
Crop Models ◽  
Aerial Vehicle

The aim of this study is to optimize the simulation result of the WOFOST model and explore the possibility of assimilating unmanned aerial vehicle (UAV) imagery into this model. Field images of wheat during its key growth stages are acquired with a UAV, and the corresponding leaf area index (LAI), biomass, and final yield are experimentally measured. LAI data is retrieved from the UAV imagery and assimilated into a localized WOFOST model using least squares optimization. Sensitive parameters, i.e., specific leaf area (SLATB0, SLATB0.5, SLATB2) and maximum CO2 assimilation rate (AMAXTB1, AMAXTB1.3) are adjusted to minimize the discrepancy between the LAI obtained from the model simulation and inversion of the UAV data. The results show that the assimilated model provides a better estimation of the growth and development of winter wheat in the study area. The R2, RMSE, and NRMSE of winter wheat LAI simulated with the assimilated WOFOST model are 0.8812, 0.49, and 23.5% respectively. The R2, RMSE, and NRMSE of the simulated yield are 0.9489, 327.06 kg·hm−2, and 6.5%. The accuracy in model simulation of winter wheat growth is improved, which demonstrates the feasibility of integrating UAV data into crop models.

scholarly journals Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1630
Author(s):  
Yaohui Zhu ◽  
Guijun Yang ◽  
Hao Yang ◽  
Fa Zhao ◽  
Shaoyu Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Minimum Temperature ◽  
Price Regulation ◽  
Flowering Period ◽  
Mean Square ◽  
Prevention Measures ◽  
Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

scholarly journals Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1020
Author(s):  
Yanqi Dong ◽  
Guangpeng Fan ◽  
Zhiwu Zhou ◽  
Jincheng Liu ◽  
Yongguo Wang ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Low Cost ◽  
Reference Value ◽  
Tree Height ◽  
Point Clouds ◽  
Tree Structure ◽  
Mean Square ◽  
Tree Model

The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

scholarly journals Rapid Biochemical Methane Potential Evaluation of Anaerobic Co-Digestion Feedstocks Based on Near Infrared Spectroscopy and Chemometrics

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1460
Author(s):  
Jinming Liu ◽  
Changhao Zeng ◽  
Na Wang ◽  
Jianfei Shi ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Variable Selection ◽  
Root Mean Square Error ◽  
Least Squares ◽  
Mean Square Error ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Mean Square ◽  
Biochemical Methane Potential ◽  
Methane Potential

Biochemical methane potential (BMP) of anaerobic co-digestion (co-AD) feedstocks is an essential basis for optimizing ratios of materials. Given the time-consuming shortage of conventional BMP tests, a rapid estimated method was proposed for BMP of co-AD—with straw and feces as feedstocks—based on near infrared spectroscopy (NIRS) combined with chemometrics. Partial least squares with several variable selection algorithms were used for establishing calibration models. Variable selection methods were constructed by the genetic simulated annealing algorithm (GSA) combined with interval partial least squares (iPLS), synergy iPLS, backward iPLS, and competitive adaptive reweighted sampling (CARS), respectively. By comparing the modeling performances of characteristic wavelengths selected by different algorithms, it was found that the model constructed using 57 characteristic wavelengths selected by CARS-GSA had the best prediction accuracy. For the validation set, the determination coefficient, root mean square error and relative root mean square error of the CARS-GSA model were 0.984, 6.293 and 2.600, respectively. The result shows that the NIRS regression model—constructed with characteristic wavelengths, selected by CARS-GSA—can meet actual detection requirements. Based on a large number of samples collected, the method proposed in this study can realize the rapid and accurate determination of the BMP for co-AD raw materials in biogas engineering.

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