Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

World University root node

VIT School for Agricultural Innovations and Advanced Learning

Vellore Campus

Challenges Faced and Preparedness of Agriculture Supply Chain During COVID-19

Agriculture forms the major part of our Indian economy. In the current world, agriculture and irrigation are the essential and foremost sectors. It is a mandatory need to apply information and communication technology in our agricultural industries to aid agriculturalists and farmers to improve vice all stages of crop cultivation and post-harvest. It helps to enhance the country’s G.D.P. Agriculture needs to be assisted by modern automation to produce the maximum yield. The recent development in technology has a significant impact on agriculture. The evolutions of Machine Learning (ML) and the Internet of Things (IoT) have supported researchers to implement this automation in agriculture to support farmers. ML allows farmers to improve yield make use of effective land utilisation, the fruitfulness of the soil, level of water, mineral insufficiencies control pest, trim development and horticulture. Application of remote sensors like temperature, humidity, soil moisture, water level sensors and pH value will provide an idea to on active farming, which will show accuracy as well as practical agriculture to deal with challenges in the field. This advancement could empower agricultural management systems to handle farm data in an orchestrated manner and increase the agribusiness by formulating effective strategies. This paper highlights contribute to an overview of the modern technologies deployed to agriculture and suggests an outline of the current and potential applications, and discusses the challenges and possible solutions and implementations. Besides, it elucidates the problems, specific potential solutions, and future directions for the agriculture sector using Machine Learning and the Internet of things.

School of Computer Science and Engineering

Ubiquitous Computing

Internet of Things and Machine Learning Applications for Smart Precision Agriculture

Among the various natural disasters, landslides are considered to be one of the serious geological hazards that are triggered due to intensive rainfall, earthquakes, deforestation, mining, floods, etc. Landslides result in devastating impacts causes thousands of deaths and injuries, damage to properties. The changing pattern of landslide hazard zones every year forces the need to safeguard people and properties in the respective areas. Weighted linear combination (WLC) is used to prepare the landslide hazard zonation map in the Nilgiris district of Tamil Nadu, India. Nine layers, namely slope, aspect, lineament density, rainfall, distance of roads, elevation, distance from rivers, landuse/landcover, geology, are used in overlay analysis. Supply chain facilities are widely used in the field of transportation of goods to the consumers with the reduction of transportation costs. The implementation of supply chain mechanism along with GIS in disaster management could help to save numerous lives during disaster events.

VIT Unitversity

Emerging Applications in Supply Chains for Sustainable Business Development Advances in Logistics, Operations, and Management Science

Risk Mapping Analysis With Geographic Information Systems for Landslides Using Supply Chain

Sustainability of a supply chain has gained more attention from economists, environmentalists, consumers, manufacturers, government and the academia. In this paper, the literature survey has been performed on production allocation problem in a multi-echelon supply chain with carbon policies. With web-based search engines such as Scopus and Web of Science several resources such as journals, conference proceedings and books are selected and reviewed. It is observed from the literature that the mentioned problem traces the progression of carbon policies in a supply chain over the past 22 years to provide substantiation for Green Supply Chain. The research papers are then analyzed and categorized to construct the useful foundation of previous studies. Moreover, the importance of this problem in recent years needs has been highlighted by mentioning the gaps in the literature. Further, at the end of the paper, several future work directions in this area also suggested.

Lecture Notes in Engineering and Computer Science

Production distribution planning in a multiechelon supply chain using carbon policies: A review and reflections

The most commonly used hydrological models for runoff estimation in soft bedrock, is the Soil Conservation Service Curve Number (SCS-CN) model. For a heterogeneous geomorphic land form the model may limit its applicability. Therefore, the SCS-CN model is tested to a small watershed, Udaygiri mandal of Nellore district, Andhra Pradesh. A linear regression method was employed to quantify the SCS-CN model performance. SCS-CN method estimation was evaluated with the help of runoff data, simulated data and actual rainfall data. It was found an accuracy of R2=0.966 for a 5 years (2012-2016) rainfall data. © IAEME Publication.

International Journal of Civil Engineering and Technology

Estimation analysis of runoff for Udaygiri Mandal using a potential method: A model study

Change detection is the process of identifying differences in the state of a feature or phenomenon by observing it at different times. In remote sensing it is useful in land use/land cover change analysis such as monitoring deforestation or vegetation phenology. In the present study the data i.e. resources at imagery of period (2007 to 2017) has been collected near the limestone mine of Devapur village of Adilabad district. ARC GIS, ERDAS Software’s were used interpretation of vegetation the preprocess multi spectral imagery and source sat and Finally, Spatial distribution and extent of land use land cover classes in study area (25km)and by using the above data we are giving the detail classification of study area. © IAEME Publication.

Preparation of change detection mapping using remote sensing and GIS: A model study

Land use and Land cover change have turned into a focal part in current systems for overseeing regular assets and checking ecological changes.. In this project an endeavor is made to think about the land use and land cover in the Guntur district region over a period of time in 2013. The study has been done through remote sensing approach utilizing Land Sat symbolisms of this area. GIS programming is utilized to set up the thematic maps of the area. Remote sensing methodology is adopted to study the geographical land use changes occurred during the study period. Land sat images of the TM and ETM+ of Guntur district area are collected from the USGS Earth Explorer website. After picture pre-handling, un-directed and administered picture grouping has been performed to characterize the pictures into various land utilize classifications. Five land use classes have been distinguished as Urban (Developed), Water body, Horticultural land, Infertile land and Vegetation. Information on land use and land cover change study is very useful to local government and urban planners for the betterment of the future plans of sustainable development of the city. © IAEME Publication.

Evaluation of land use and land coverusing remote sensing and GIS: A model study from Guntur district

Application of Remote sensing (RS) data and Geographic information system (GIS) techniques has been given much emphasis for the pipeline peline alignment study. The present study is covering an aerial extension of 18 km from Patancheru to Balnagar of Hyderabad district, Telengana. The study area covers a variety of geomorphological features. Interpretation of satellite data I.e. IRS-LISS III of 1:50,000 scales are used in the present study to understand the geomorphological nature of the study area. For suggesting the pipeline alignment in the study area the thematic information used are, the occurrence and distribution of various landforms, mining/industrial belts, densely/sparsely habituated areas (villages, towns etc.. ), forest cover, changing river courses, flood prone areas, erosion prone areas, marshy/water logged areas, stream/canal/railway line/road network etc. LISS III data of spectral resolution of 23.5m and repetitive coverage within 24 days provided an integrated information which is required for pipeline alignment. Finally a pipeline corridor map of 1:50,000 scaleis prepared using the developed query based decision support system for locating a short, safe and cost effective pipeline route. It's a regional appreciation showing a tentative alignment by shortest route with detailed information i.e. actual number of turning point, road/railway line /stream/canal etc. © IAEME Publication.

Creation of query based decision support system for selection of pipeline corridor site using RS &amp; GIS: A model study

In this study, Atterberg limits were estimated using pedotransfer functions (PTFs), spectral-transfer functions (STFs), and spectral PTFs (SPTFs). In the PTF approach, basic soil properties were used to calibrate PTFs using stepwise multiple linear regression and support-vector regression (SVR) approaches. In the STF approach, spectral reflectance values across the visible to near-infrared (VNIR) and mid-infrared (MIR) regions were used to estimate Atterberg limits using the SVR and partial least squares regression (PLSR) approaches along with bootstrapping. Also, two locally weighted PLSR models were developed using covariance and correlation as weighting schemes. Finally, feature selection was combined with the PLSR (PLSRFS) to identify the optimum set of spectral features to use in the estimations. In the SPTF approach, both basic properties and geometrical shape factors of spectral absorption were used for the prediction of Atterberg limits. Comparison of the coefficient of determination (R2) and root mean squared residual values for the validation data sets across different modeling approaches suggested that the PLSRFS model along with pooled VNIR + MIR data yielded the highest R2 of 0.77 for the liquid limit. The PLSRFS model along with VNIR data alone yielded the highest R2 of 0.70 for the plastic limit. A maximum R2 value of 0.65 was obtained for the plasticity index using both the PLSR and SVR models on MIR data and the PLSRFS model on pooled VNIR + MIR data. This study shows that the STF approach may be adopted as an alternate to PTFs to estimate the Atterberg limits because it can account for up to 77% of the variance in the data. © Soil Science Society of America.

Soil Science Society of America Journal

Rapid and noninvasive assessment of atterberg limits using diffuse reflectance spectroscopy

A new approach to estimate weathering indices (WIs) in soil was developed using proximally-sensed spectral reflectance over visible to shortwave-infrared (vis-NIR) and mid-infrared (MIR) region of electromagnetic spectrum. Partial-least-squares regression (PLSR) analysis was used to develop spectral algorithms for estimating twelve different WIs commonly used in geochemical literature. For each index, two different models were calibrated: 1) based on all the features in spectra and 2) based on the features obtained by variable importance projection, which we denote by vis-NIRvip, MIRvip, and (vis-NIR+MIR)vip. The residual prediction deviation (RPD) was used for checking the robustness of spectral models. Results showed that the MIR reflectance data provided superior estimation capability for all WIs compared with the vis-NIR reflectance data with the best possible prediction obtained for index of laterization (IOL; RPD=5.86) in the MIR and Mg Index (MgI; RPD=2.43) in the vis-NIR approach. The highest RPD values of 3.12, 4.13, 3.78, 6.13, and 5.08 were obtained for chemical index of alteration (CIA), MgI, mafic index of alteration (MIA(O)), IOL, and weathering index of Parker (WIP), respectively, when the PLSR model was based on (vis-NIR+MIR)vip. Best predictions were obtained when vis-NIR and MIR were combined and important spectral features were selected through variable importance projection (VIP) approach. Both vis-NIR and MIR technologies are available in the form of portable devices and are amenable for remote sensing mode of operation. This study demonstrates for the first time that the WI values in soil may be estimated in a rapid and non-destructive way in situ. © 2015 Elsevier B.V.

Geoderma

Estimation of weathering indices using spectral reflectance over visible to mid-infrared region

Identification of similar soils is an important step in different branches of soil hydrology. Generally, a characteristic length in porous media is used for estimating scaling factors in a similarity analysis. Such an approach amounts to the use of the first moment of the pore size distribution (PSD). Previous research has suggested that the s of PSD of similar soils should also be similar. Authors of this research proposed a new criterion in terms of the CV for s (CVs) with the suggestion that the “geometrically similar” soils should satisfy the condition of CVs < 10%. Here, we validate this result using the aggregate size distribution (ASD) data of 1375 soils collected from 10 different zones with varying scale and properties. The lognormal distribution function and the physically based scaling (PBS) approach were used to scale the ASD datasets. The effectiveness of scaling was determined by the RMSE between the scaled ASD curve and the reference ASD curve. Four out of 10 soil datasets had CVs < 12%, and their corresponding RMSE values were an order of magnitude lower than those for the remaining soil groups. A plot between the RMSE and CVs values showed that CVs < 10% may be used as a practical limit for identifying similar soils. © Soil Science Society of America.

Vadose Zone Journal

Variance of aggregate size distribution as a criterion for soil similarity

Assessment of soil structure and soil aggregation remains a challenging task. Routine methods such as dry- and wet-sieving approaches are generally time consuming and tedious, which calls for a robust, fast, and nondestructive method of soil aggregate characterization. Over the last two decades, diffuse reflectance spectroscopy (DRS) has emerged as a rapid and noninvasive technique for soil characterization. Combined with chemometric and data-mining algorithms, it provides an effective way of measuring several soil attributes and has the added advantage of being amenable to a remote sensing mode of operation. The objective of this study was to determine if the DRS approach could be used as a rapid, noninvasive technique to estimate soil aggregate characteristics. The DRS approach was examined for the estimation of soil aggregate characteristics such as the geometric mean diameter and two statistical parameters of the lognormal aggregate size distribution (ASD) functions using 910 soil samples from India representing three important soil groups. Results showed that the geometric mean diameter and the median aggregate size parameter provided excellent predictions, with ratio of performance deviation (RPD) values ranging from 1.99 to 2.28. The RPD value for the standard deviation of the ASD ranged from 1.36 to 1.72, suggesting moderate prediction. It was further observed that soil aggregates influence the incident electromagnetic radiation on soils primarily in the visible region and to some extent the shortwave- and near-infrared regions. Electronic transitions of Fe-bearing minerals, clay minerals, and C-H functional groups of organic matter may be responsible for modifying the spectral reflectance from soils in addition to the self-shadowing effects of surface roughness. The results of this study suggest that the chemometric approach may be combined with DRS to estimate soil aggregate size characteristics. © Soil Science Society of America.