Improved Canopy Characterization with Laser Scanning Sensor for Greenhouse Spray Applications

2021 ◽  
Vol 64 (6) ◽  
pp. 2125-2136
Author(s):  
Uchit Nair ◽  
Peter P. Ling ◽  
Heping Zhu
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Sensor Data ◽  
Processing Algorithm ◽  
Individual Plant ◽  
Laser Sensor ◽  
List Type ◽  
Mean Square ◽  
Processing Variable

HighlightsAn algorithm was developed to process laser sensor data to make more accurate measurements of canopy dimensions.The algorithm isolated individual canopies, removed distortion, and estimated the occluded portions of the dataset.The algorithm reduced measured error by 46% in terms of root mean square error (RMSE).The RMSE was higher for sensor heights below and above a calculated optimal sensor height.Abstract. Laser-guided intelligent spray technology for greenhouse applications requires sensors that can accurately measure plant dimensions. This study proposed a new method to overcome current limitations by introducing a processing algorithm that manipulates the noisy dataset and determines the optimal sensor height to produce better measurements of the canopy width. The processing algorithm involves a combination of registration, clustering, and mirroring. Registration aligns multiple scans of the same scene to improve resolution. Clustering isolates individual plant canopies from the dataset to enable further processing. Mirroring is used to resolve the problems of distortion and occlusion and predict missing information in the dataset. The performance of the processing algorithm was evaluated by calculating the root mean square error (RMSE) in the canopy width measurements. Its results were compared with the measurements reported in earlier research, where there was limited processing of the laser sensor data. The processing algorithm reduced RMSE values by 46% compared to the earlier research, and the largest improvements were seen for objects placed beyond 1.5 m from the sensor. The sensor height was observed to be inversely proportional to the RMSE values. The average RMSE of the processing algorithm was 25 mm, compared to 47 mm in the earlier research when the laser sensor was at a height of 1 m. Another experimental setup was used to test the limits of the relationship between sensor height and algorithm performance while using objects that were more representative of plant canopy shapes. The accuracy of the processing algorithm decreased when the sensor height was either above or below the optimal sensor height, which was derived from calculations made in earlier research. The processing algorithm has potential to improve spray efficiencies. Keywords: Automation, Clustering, LiDAR, Point cloud data processing, Variable-rate spray.

scholarly journals Evaluation methods for low-cost particulate matter sensors

2021 ◽  
Author(s):  
Jeffrey K. Bean
Keyword(s):  
Quality Assurance ◽  
Particulate Matter ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Prediction Interval ◽  
Low Cost ◽  
Sensor Data ◽  
Rural Site ◽  
Mean Square

Abstract. Understanding and improving the quality of data generated from low-cost sensors is a crucial step in using these sensors to fill gaps in air quality measurement and understanding. This paper shows results from a 10-month long campaign that included side-by-side measurements and comparison between EPA-approved reference instruments and low-cost particulate matter sensors in Bartlesville, Oklahoma. At this rural site in the Midwestern United States the instruments typically encountered only low (under 20 µg/m3) concentrations of particulate matter, however higher concentrations (50–400 µg/m3) were observed on three different days during what were likely agricultural burning events. This study focused on methods for understanding and improving data quality for low-cost particulate matter sensors. The data offered insights on how averaging time, choice of reference instrument, and the observation of higher pollutant concentrations can all impact performance indicators (R2 and root mean square error) for an evaluation. The influence of these factors should be considered when comparing one sensor to another or when determining whether a sensor can produce data that fits a specific need. Though R2 and root mean square error remain the dominant metrics in sensor evaluations, an alternative approach using a prediction interval may offer more consistency between evaluations and a more direct interpretation of sensor data following an evaluation. Ongoing quality assurance for sensor data is needed to ensure data continues to meet expectations. Observations of trends in linear regression parameters and sensor bias were used to analyze calibration and other quality assurance techniques.

scholarly journals Evaluation methods for low-cost particulate matter sensors

2021 ◽  
Vol 14 (11) ◽  
pp. 7369-7379
Author(s):  
Jeffrey K. Bean
Keyword(s):  
United States ◽  
Quality Assurance ◽  
Particulate Matter ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Low Cost ◽  
Sensor Data ◽  
Rural Site ◽  
Mean Square

Abstract. Understanding and improving the quality of data generated from low-cost sensors represent a crucial step in using these sensors to fill gaps in air quality measurement and understanding. This paper shows results from a 10-month-long campaign that included side-by-side measurements and comparison between reference instruments approved by the United States Environmental Protection Agency (EPA) and low-cost particulate matter sensors in Bartlesville, Oklahoma. At this rural site in the Midwestern United States the instruments typically encountered only low (under 20 µg m−3) concentrations of particulate matter; however, higher concentrations (50–400 µg m−3) were observed on 3 different days during what were likely agricultural burning events. This study focused on methods for understanding and improving data quality for low-cost particulate matter sensors. The data offered insights on how averaging time, choice of reference instrument, and the observation of higher pollutant concentrations can all impact performance indicators (R2 and root mean square error) for an evaluation. The influence of these factors should be considered when comparing one sensor to another or when determining whether a sensor can produce data that fit a specific need. Though R2 and root mean square error remain the dominant metrics in sensor evaluations, an alternative approach using a prediction interval may offer more consistency between evaluations and a more direct interpretation of sensor data following an evaluation. Ongoing quality assurance for sensor data is needed to ensure that data continue to meet expectations. Observations of trends in linear regression parameters and sensor bias were used to analyze calibration and other quality assurance techniques.

scholarly journals Automatic detection of social rhythms in bipolar disorder

2016 ◽  
Vol 23 (3) ◽  
pp. 538-543 ◽  
Author(s):  
Saeed Abdullah ◽  
Mark Matthews ◽  
Ellen Frank ◽  
Gavin Doherty ◽  
Geri Gay ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
High Accuracy ◽  
Sensor Data ◽  
Mean Square ◽  
Improve Performance ◽  
The Social ◽  
Social Rhythm

Objective To evaluate the feasibility of automatically assessing the Social Rhythm Metric (SRM), a clinically-validated marker of stability and rhythmicity for individuals with bipolar disorder (BD), using passively-sensed data from smartphones. Methods Seven patients with BD used smartphones for 4 weeks passively collecting sensor data including accelerometer, microphone, location, and communication information to infer behavioral and contextual patterns. Participants also completed SRM entries using a smartphone app. Results We found that automated sensing can be used to infer the SRM score. Using location, distance traveled, conversation frequency, and non-stationary duration as inputs, our generalized model achieves root-mean-square-error of 1.40, a reasonable performance given the range of SRM score (0–7). Personalized models further improve performance with mean root-mean-square-error of 0.92 across users. Classifiers using sensor streams can predict stable (SRM score ≥3.5) and unstable (SRM score <3.5) states with high accuracy (precision: 0.85 and recall: 0.86). Conclusions Automatic smartphone sensing is a feasible approach for inferring rhythmicity, a key marker of wellbeing for individuals with BD.

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.

Comparison of Thresholding and Edge Detection Segmentation Techniques

2013 ◽  
Vol 860-863 ◽  
pp. 2783-2786
Author(s):  
Yu Bing Dong ◽  
Hai Yan Wang ◽  
Ming Jing Li
Keyword(s):  
Image Segmentation ◽  
Root Mean Square Error ◽  
Edge Detection ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Experimental Results ◽  
Mean Square ◽  
Segmentation Method ◽  
Segmentation Algorithms

Edge detection and thresholding segmentation algorithms are presented and tested with variety of grayscale images in different fields. In order to analyze and evaluate the quality of image segmentation, Root Mean Square Error is used. The smaller error value is, the better image segmentation effect is. The experimental results show that a segmentation method is not suitable for all images segmentation.

Quantitative Analysis of Forsythiae fructus by Near Infrared Spectroscopy

2013 ◽  
Vol 807-809 ◽  
pp. 1967-1971
Author(s):  
Yan Bai ◽  
Xiao Yan Duan ◽  
Hai Yan Gong ◽  
Cai Xia Xie ◽  
Zhi Hong Chen ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Ethanol Extract ◽  
Mean Square ◽  
Validation Set

In this paper, the content of forsythoside A and ethanol-extract were rapidly determinated by near-infrared reflectance spectroscopy (NIRS). 85 samples of Forsythiae Fructus harvested in Luoyang from July to September in 2012 were divided into a calibration set (75 samples) and a validation set (10 samples). In combination with the partical least square (PLS), the quantitative calibration models of forsythoside A and ethanol-extract were established. The correlation coefficient of cross-validation (R2) was 0.98247 and 0.97214 for forsythoside A and ethanol-extract, the root-mean-square error of calibration (RMSEC) was 0.184 and 0.570, the root-mean-square error of cross-validation (RMSECV) was 0.81736 and 0.36656. The validation set were used to evaluate the performance of the models, the root-mean-square error of prediction (RMSEP) was 0.221 and 0.518. The results indicated that it was feasible to determine the content of forsythoside A and ethanol-extract in Forsythiae Fructus by near-infrared spectroscopy.

Analysis of candlenut oil as oil adulterant in three functional oils of soybean oil, sunflower oil and grapeseed oil in quaternary mixture systems using FTIR spectroscopy and chemometrics

Food Research ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 248-253
Author(s):  
A.B. Riyanta ◽  
S. Riyanto ◽  
E. Lukitaningsih ◽  
A. Rohman
Keyword(s):  
Root Mean Square Error ◽  
Soybean Oil ◽  
Ftir Spectroscopy ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Sunflower Oil ◽  
Ftir Spectra ◽  
Mean Square ◽  
Grapeseed Oil ◽  
Quaternary Mixture

Soybean oil (SBO), sunflower oil (SFO) and grapeseed oil (GPO) contain high levels of unsaturated fats that are good for health and have proximity to candlenut oil. Candlenut oil (CNO) has a lower price and easier to get oil from that seeds than other seed oils, so it is used as adulteration for gains. Therefore, authentication is required to ensure the purity of oils by proper analysis. This research was aimed to highlight the FTIR spectroscopy application with multivariate calibration is a potential analysis for scanning the quaternary mixture of CNO, SBO, SFO and GPO. CNO quantification was performed using multivariate calibrations of principle component (PCR) regression and partial least (PLS) square to predict the model from the optimization FTIR spectra regions. The highest R2 and the lowest values of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were used as the basis for selection of multivariate calibrations created using several wavenumbers region of FTIR spectra. Wavenumbers regions of 4000-650 cm-1 from the second derivative FTIR-ATR spectra using PLS was used for quantitative analysis of CNO in quaternary mixture with SBO, SFO and GPO with R2 calibration = 0.9942 and 0.0239% for RMSEC value and 0.0495%. So, it can be concluded the use of FTIR spectra combination with PLS is accurate to detect quaternary mixtures of CNO, SBO, SFO and GPO with the highest R2 values and the lowest RMSEC and RMSEP values.

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