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 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.

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

scholarly journals Effect of Low Zeolite Doses on Plants and Soil Physicochemical Properties

Materials ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2617
Author(s):  
Alicja Szatanik-Kloc ◽  
Justyna Szerement ◽  
Agnieszka Adamczuk ◽  
Grzegorz Józefaciuk
Keyword(s):  
Plant Growth ◽  
Physicochemical Properties ◽  
Cation Exchange ◽  
Surface Area ◽  
Cation Exchange Capacity ◽  
N Fertilization ◽  
Exchange Capacity ◽  
First Year ◽  
Soil Physicochemical Properties ◽  
Water Holding

Thousands of tons of zeolitic materials are used yearly as soil conditioners and components of slow-release fertilizers. A positive influence of application of zeolites on plant growth has been frequently observed. Because zeolites have extremely large cation exchange capacity, surface area, porosity and water holding capacity, a paradigm has aroused that increasing plant growth is caused by a long-lasting improvement of soil physicochemical properties by zeolites. In the first year of our field experiment performed on a poor soil with zeolite rates from 1 to 8 t/ha and N fertilization, an increase in spring wheat yield was observed. Any effect on soil cation exchange capacity (CEC), surface area (S), pH-dependent surface charge (Qv), mesoporosity, water holding capacity and plant available water (PAW) was noted. This positive effect of zeolite on plants could be due to extra nutrients supplied by the mineral (primarily potassium—1 ton of the studied zeolite contained around 15 kg of exchangeable potassium). In the second year of the experiment (NPK treatment on previously zeolitized soil), the zeolite presence did not impact plant yield. No long-term effect of the zeolite on plants was observed in the third year after soil zeolitization, when, as in the first year, only N fertilization was applied. That there were no significant changes in the above-mentioned physicochemical properties of the field soil after the addition of zeolite was most likely due to high dilution of the mineral in the soil (8 t/ha zeolite is only ~0.35% of the soil mass in the root zone). To determine how much zeolite is needed to improve soil physicochemical properties, much higher zeolite rates than those applied in the field were studied in the laboratory. The latter studies showed that CEC and S increased proportionally to the zeolite percentage in the soil. The Qv of the zeolite was lower than that of the soil, so a decrease in soil variable charge was observed due to zeolite addition. Surprisingly, a slight increase in PAW, even at the largest zeolite dose (from 9.5% for the control soil to 13% for a mixture of 40 g zeolite and 100 g soil), was observed. It resulted from small alterations of the soil macrostructure: although the input of small zeolite pores was seen in pore size distributions, the larger pores responsible for the storage of PAW were almost not affected by the zeolite addition.

scholarly journals Vitamin A and E Homologues Impacting the Fate of Acrylamide in Equimolar Asparagine-Glucose Model System

Antioxidants ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 993
Author(s):  
Su Lee Kuek ◽  
Azmil Haizam Ahmad Tarmizi ◽  
Raznim Arni Abd Razak ◽  
Selamat Jinap ◽  
Maimunah Sanny
Keyword(s):  
Vitamin E ◽  
Linear Regression ◽  
Vitamin A ◽  
Regression Model ◽  
Linear Regression Model ◽  
Model System ◽  
Concentration Increase ◽  
Acrylamide Formation ◽  
Β Carotene

This study aims to evaluate the influence of Vitamin A and E homologues toward acrylamide in equimolar asparagine-glucose model system. Vitamin A homologue as β-carotene (BC) and five Vitamin E homologues, i.e., α-tocopherol (AT), δ-tocopherol (DT), α-tocotrienol (ATT), γ-tocotrienol (GTT), and δ-tocotrienol (DTT), were tested at different concentrations (1 and 10 µmol) and subjected to heating at 160 °C for 20 min before acrylamide quantification. At lower concentrations (1 µmol; 431, 403, 411 ppm, respectively), AT, DT, and GTT significantly increase acrylamide. Except for DT, enhancing concentration to 10 µmol (5370, 4310, 4250, 3970, and 4110 ppm, respectively) caused significant acrylamide formation. From linear regression model, acrylamide concentration demonstrated significant depreciation over concentration increase in AT (Beta = −83.0, R2 = 0.652, p ≤ 0.05) and DT (Beta = −71.6, R2 = 0.930, p ≤ 0.05). This study indicates that different Vitamin A and E homologue concentrations could determine their functionality either as antioxidants or pro-oxidants.

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