scholarly journals Plant Growth and LAI Estimation using quantized Embedded Regression models for high throughput phenotyping

2021 ◽  
Author(s):  
Dhruv Sheth
Keyword(s):  
Image Processing ◽  
Linear Regression ◽  
Plant Growth ◽  
Regression Model ◽  
Real Time ◽  
Crop Yield ◽  
High Throughput ◽  
Linear Regression Model ◽  
Growth Patterns ◽  
Biomass Estimation

Abstract Due to the influence of climate change, and due to it's unpredictable nature, majority of agricultural crops have been affected in terms of production and maintenance. Hybrid and cost-effective crops are making their way into the market, but monitoring factors which affect the increase in yield of these crops, and conditions favorable for growth have to be manually monitored and structured to yield high throughput. Farmers are showing transition from traditional means to hydroponic systems for growing annual and perennial crops. These crop arrays possess growth patterns which depend on environmental growth conditions in the hydroponic units. Semi-autonomous systems which monitor these growth may prove to be beneficial, reduce costs and maintenance efforts, and also predict future yield beforehand to get an idea on how the crop would perform. These systems are also effective in understanding crop drools and wilt/diseases from visual systems and traits of plants.Forecasting or predicting the crop yield well ahead of its harvest time would assist the strategists and farmers for taking suitable measures for selling and storage. Accurate prediction of crop development stages plays an important role in crop production management. In this article, I~propose an Embedded Machine Learning approach to predicting crop yield and biomass estimation of crops using an Image based Regression approach using EdgeImpulse that runs on Edge system, Sony Spresense, in real time. This utilizes few of the 6 Cortex M4F cores provided in the Sony Spresense board for Image processing, inferencing and predicting a regression output in real time. This system uses Image processing to analyze the plant in a semi-autonomous environment and predict the numerical serial of the biomass allocated to the plant growth. This numerical serial contains a threshold of biomass which is then predicted for the plant. The biomass output is then also processed through a linear regression model to analyze efficacy and compared with the ground truth to identify pattern of growth. The image Regression and linear regression model contribute to an algorithm which is finally used to test and predict biomass for each plant semi-autonomously.

Correlation of pH on Soil Physicochemical Properties using Linear Regression Model for Spectral Data Analysis

2021 ◽  
Vol 10 (1) ◽  
pp. 3492-3500
Author(s):  
Vipin Y. Borole ◽  
◽  
Sonali B. Kulkarni ◽  
Keyword(s):  
Linear Regression ◽  
Plant Growth ◽  
Physicochemical Properties ◽  
Regression Model ◽  
Soil Properties ◽  
Spectral Data ◽  
Linear Regression Model ◽  
Soil Analysis ◽  
Soil Physicochemical Properties ◽  
Analytical Technique

Soil properties may be varied by spatially and temporally with different agricultural practices. An accurate and reliable soil properties assessment is challenging issue in soil analysis. The soil properties assessment is very important for understanding the soil properties, nutrient management, influence of fertilizers and relation between soil properties which are affecting the plant growth. Conventional laboratory methods used to analyses soil properties are generally impractical because they are time-consuming, expensive and sometimes imprecise. On other hand, Visible and infrared spectroscopy can effectively characterize soil. Spectroscopic measurements are rapid, precise and inexpensive. Soil spectroscopy has shown to be a fast, cost-effective, environmentally friendly, non-destructive, reproducible and repeatable analytical technique. In the present research, we use spectroscopy techniques for soil properties analysis. The spectra of agglomerated farming soils were acquired by the ASD Field spec 4 spectroradiometer. Different fertilizers treatment applied soil samples are collected in pre monsoon and post monsoon season for 2 year (4 season) for banana and cotton crops in the form of DS-I and DS-II respectively. The soil spectra of VNIR region were preprocessed to get pure spectra. Then process the acquired spectral data by statistical methods for quantitative analysis of soil properties. The detected soil properties were carbon, Nitrogen, soil organic matter, pH, phosphorus, potassium, moisture sand, silt and clay. Soil pH is most important chemical properties that describe the relative acidity or alkalinity of the soil. It directly effect on plant growth and other soil properties. The relationship between pH properties on soil physical and chemical parameters and their influence were analyses by using linear regression model and show the performance of regression model with R2 and RMSE. Keywords soil; physicochemical properties; spectroscopy; pH

scholarly journals PM2.5 Concentration Estimation Based on Image Processing Schemes and Simple Linear Regression

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2423 ◽  
Author(s):  
Jiun-Jian Liaw ◽  
Yung-Fa Huang ◽  
Cheng-Hsiung Hsieh ◽  
Dung-Ching Lin ◽  
Chin-Hsiang Luo
Keyword(s):  
Image Processing ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Simple Linear Regression ◽  
Simple Linear Regression Model ◽  
Concentration Estimation ◽  
Single Feature ◽  
The Difference ◽  
Pm2.5 Concentration

Fine aerosols with a diameter of less than 2.5 microns (PM2.5) have a significant negative impact on human health. However, their measurement devices or instruments are usually expensive and complicated operations are required, so a simple and effective way for measuring the PM2.5 concentration is needed. To relieve this problem, this paper attempts to provide an easy alternative approach to PM2.5 concentration estimation. The proposed approach is based on image processing schemes and a simple linear regression model. It uses images with a high and low PM2.5 concentration to obtain the difference between these images. The difference is applied to find the region with the greatest impact. The approach is described in two stages. First, a series of image processing schemes are employed to automatically select the region of interest (RoI) for PM2.5 concentration estimation. Through the selected RoI, a single feature is obtained. Second, by employing the single feature, a simple linear regression model is used and applied to PM2.5 concentration estimation. The proposed approach is verified by the real-world open data released by Taiwan’s government. The proposed scheme is not expected to replace component analysis using physical or chemical techniques. We have tried to provide a cheaper and easier way to conduct PM2.5 estimation with an acceptable performance more efficiently. To achieve this, further work will be conducted and is summarized at the end of this paper.

scholarly journals ESTIMATING ABOVEGROUND BIOMASS OF BAMBOO AND MIXED BAMBOO FOREST IN THUA THIEN-HUE PROVINCE, VIET NAM USING PALSAR-2 AND LANDSAT OLI DATA

2019 ◽  
Vol XLII-4/W20 ◽  
pp. 11-19
Author(s):  
T. T. Cat Tuong ◽  
H. Tani ◽  
X. F. Wang ◽  
V.-M. Pham
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Vegetation Indices ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Landsat Data ◽  
Bamboo Forest ◽  
Landsat 8 Oli ◽  
L Band

Abstract. In this study, above-ground biomass (AGB) performance was evaluated by PALSAR-2 L-band and Landsat data for bamboo and mixed bamboo forest. The linear regression model was chosen and validated for forest biomass estimation in A Luoi district, Thua Thien Hue province, Vietnam. A Landsat 8 OLI image and a dual-polarized ALOS/PALSAR-2 L-band (HH, HV polarizations) were used. In addition, 11 diferrent vegetation indices were extracted to test the performance of Landsat data in estimating forest AGB Total of 54 plots were collected in the bamboo and mixed bamboo forest in 2016. The linear regression is used to evaluate the sensitivity of biomass to the obtained parameters, including radar polarization, optical properties, and some vegetation indices which are extracted from Landsat data. The best-fit linear regression is selected by using the Bayesian Model Average for biomass estimation. Leave-one-out cross-validation (LOOCV) was employed to test the robustness of the model through the coefficient of determination (R squared – R2) and Root Mean Squared Error (RMSE). The results show that Landsat 8 OLI data has a slightly better potential for biomass estimation than PALSAR-2 in the bamboo and mixed bamboo forest. Besides, the combination of PALSAR-2 and Landsat 8 OLI data also has a no significant improvement (R2 of 0.60) over the performance of models using only SAR (R2 of 0.49) and only Landsat data (R2 of 0.58–0.59). The univariate model was selected to estimate AGB in the bamboo and mixed bamboo forest. The model showed good accuracy with an R2 of 0.59 and an RMSE of 29.66 tons ha−1. The comparison between two approaches using the entire dataset and LOOCV demonstrates no significant difference in R (0.59 and 0.56) and RMSE (29.66 and 30.06 tons ha−1). This study performs the utilization of remote sensing data for biomass estimation in bamboo and mixed bamboo forest, which is a lack of up-to-date information in forest inventory. This study highlights the utilization of the linear regression model for estimating AGB of the bamboo forest with a limited number of field survey samples. However, future research should include a comparison with non-linear and non-parametric models.

scholarly journals Rainfall estimation using Image Processing and Regression model on DWR Rainfall Product for Delhi-NCR Region

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Kuldeep Srivastava ◽  
Ashish Nigam
Keyword(s):  
Image Processing ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Weather Radar ◽  
Least Square ◽  
Rainfall Data ◽  
Doppler Weather Radar ◽  
Rainfall Estimation ◽  
Radar Rainfall

Observed rainfall is a very essential parameter for the analysis of rainfall, day to day weather forecast and its validation. The observed rainfall data is only available from five observatories of IMD; while no rainfall data is available at various important locations in and around Delhi-NCR. However, the 24-hour rainfall data observed by Doppler Weather Radar (DWR) for entire Delhi and surrounding region (up to 150 km) is readily available in a pictorial form. In this paper, efforts have been made to derive/estimate the rainfall at desired locations using DWR hydrological products. Firstly, the rainfall at desired locations has been estimated from the precipitation accumulation product (PAC) of the DWR using image processing in Python language. After this, a linear regression model using the least square method has been developed in R language. Estimated and observed rainfall data of year 2018 (July, August and September) was used to train the model. After this, the model was tested on rainfall data of year 2019 (July, August and September) and validated.With the use of linear regression model, the error in mean rainfall estimation reduced by 46.58% and the error in max rainfall estimation reduced by 84.53% for the year 2019. The error in mean rainfall estimation reduced by 81.36% and the error in max rainfall estimation reduced by 33.81% for the year 2018. Thus, the rainfall can be estimated with a fair degree of accuracy at desired locations within the range of the Doppler Weather Radar using the radar rainfall products and the developed linear regression model.

Forest Biomass Estimation Based on Remote Sensing Method for North Daxingan Mountains

2011 ◽  
Vol 339 ◽  
pp. 336-341 ◽  
Author(s):  
Yuan Yuan Zhang ◽  
Feng Ri Li ◽  
Fu Xiang Liu
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Forest Biomass ◽  
Multiple Linear Regression Model ◽  
Biomass Estimation ◽  
Individual Tree ◽  
Forest Region

Using the Landsat 5 TM images in 2002 as source data，the paper constructed individual tree biomass models of seven principal species based on the data from field surveying and fixed Plots in Tahe and Amur forest Region in Daxiangan Mountains. The remote sensing biomass model between TM images and data from forest fixed Plots was developed by the methods of multiple linear regression and BP neutral net. The result showed that R in multiple linear regression model was 0.764 and the model passed the F test, D-W test and multi-collinearity test. In the independent sample estimation，The neutral net model with the precision of 91.25% was significantly higher than multiple linear regression model with the precision of 81.02%. Although the“black-box”neutral net model could not give the concrete analytical equation, this kind of model with high precision might be applied to estimate the forest biomass in large level forest biomass.

A Real-Time Traffic Information Model Using GPS-Based Probe Car Data

2011 ◽  
Vol 467-469 ◽  
pp. 1433-1437
Author(s):  
Wan Jia Chen ◽  
Chi Hua Chen ◽  
Bon Yeh Lin ◽  
Chi Chun Lo
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Real Time ◽  
Traffic Flow ◽  
Linear Regression Model ◽  
Traffic Information ◽  
Distribution Model ◽  
Vehicle Speed ◽  
Real Time Traffic ◽  
Error Ratio

In recent year, the rise of economic growth and technology advance leads to improve the quality of service of traditional transport system. Intelligent Transportation System (ITS) has become more and more popular. At present, the collection of real-time traffic information is executed in two ways: (1) Stationary Vehicle Detectors (VD) and (2) Global Position System (GPS)-based probe cars reporting. However, VD devices need a large sum of money to build and maintain. Therefore, we propose the linear regression model to infer the equation between vehicle speed and traffic flow. The traffic flow can be estimated from the speed which is obtained from GPS-based probe cars. In experiments, the Speed Error Ratio (SER) and Flow Error Ratio (FER) of linear regression model are 4.60% and 24.63% respectively. The estimated speed and traffic flow by using linear regression model is better than by using linear model, power law model, exponential model, and normal distribution model. Therefore, the linear regression model can be used to estimate traffic information for ITS.

scholarly journals Real-time grower-finisher pigs’ growth monitored and forecasted using a dynamic linear regression model

2018 ◽  
Author(s):  
Alberto Peña Fernández ◽  
Tomas Norton ◽  
Erik Vranken ◽  
Daniel Berckmans
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Real Time ◽  
Linear Regression Model

scholarly journals Evaluation of MJO Forecast Skill from Several Statistical and Dynamical Forecast Models

2009 ◽  
Vol 22 (9) ◽  
pp. 2372-2388 ◽  
Author(s):  
Kyong-Hwan Seo ◽  
Wanqiu Wang ◽  
Jon Gottschalck ◽  
Qin Zhang ◽  
Jae-Kyung E. Schemm ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Real Time ◽  
Linear Regression Model ◽  
Southern Oscillation ◽  
Multiple Linear Regression Model ◽  
Forecast Skill ◽  
Maritime Continent ◽  
Forecast System

Abstract This work examines the performance of Madden–Julian oscillation (MJO) forecasts from NCEP’s coupled and uncoupled general circulation models (GCMs) and statistical models. The forecast skill from these methods is evaluated in near–real time. Using a projection of El Niño–Southern Oscillation (ENSO)-removed variables onto the principal patterns of MJO convection and upper- and lower-level circulations, MJO-related signals in the dynamical model forecasts are extracted. The operational NCEP atmosphere–ocean fully coupled Climate Forecast System (CFS) model has useful skill (>0.5 correlation) out to ∼15 days when the initial MJO convection is located over the Indian Ocean. The skill of the CFS hindcast dataset for the period from 1995 to 2004 is nearly comparable to that from a lagged multiple linear regression model, which uses information from the previous five pentads of the leading two principal components (PCs). In contrast, the real-time analysis for the MJO forecast skill for the period from January 2005 to February 2006 using the lagged multiple linear regression model is reduced to ∼10–12 days. However, the operational CFS forecast for this period is skillful out to ∼17 days for the winter season, implying that the coupled dynamical forecast has some usefulness in predicting the MJO compared to the statistical model. It is shown that the coupled CFS model consistently, but only slightly, outperforms the uncoupled atmospheric model (by one to two days), indicating that only limited improvement is gained from the inclusion of the coupled air–sea interaction in the MJO forecast in this model. This slight improvement may be the result of the existence of a propagation barrier around the Maritime Continent and the far western Pacific in the NCEP Global Forecast System (GFS) and CFS models, as shown in several previous studies. This work also suggests that the higher horizontal resolution and finer initial data might contribute to improving the forecast skill, presumably as a result of an enhanced representation of the Maritime Continent region.

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