Automated crop prediction is needed for the following reasons: First, agricultural yields were decided by a farmer's ability to work in a certain field and with a particular crop…
Abstract
Purpose
Automated crop prediction is needed for the following reasons: First, agricultural yields were decided by a farmer's ability to work in a certain field and with a particular crop previously. They were not always able to predict the crop and its yield solely on that idea alone. Second, seed firms frequently monitor how well new plant varieties would grow in certain settings. Third, predicting agricultural production is critical for solving emerging food security concerns, especially in the face of global climate change. Accurate production forecasts not only assist farmers in making informed economic and management decisions but they also aid in the prevention of famine. This results in farming systems’ efficiency and productivity gains, as well as reduced risk from environmental factors.
Design/methodology/approach
This research paper proposes a machine learning technique for effective autonomous crop and yield prediction, which makes use of solution encoding to create solutions randomly, and then for every generated solution, fitness is evaluated to meet highest accuracy. Major focus of the proposed work is to optimize the weight parameter in the input data. The algorithm continues until the optimal agent or optimal weight is selected, which contributes to maximum accuracy in automated crop prediction.
Findings
Performance of the proposed work is compared with different existing algorithms, such as Random Forest, support vector machine (SVM) and artificial neural network (ANN). The proposed method support vector neural network (SVNN) with gravitational search agent (GSA) is analysed based on different performance metrics, such as accuracy, sensitivity, specificity, CPU memory usage and training time, and maximum performance is determined.
Research limitations/implications
Rather than real-time data collected by Internet of Things (IoT) devices, this research focuses solely on historical data; the proposed work does not impose IoT-based smart farming, which enhances the overall agriculture system by monitoring the field in real time. The present study only predicts the sort of crop to sow not crop production.
Originality/value
The paper proposes a novel optimization algorithm, which is based on the law of gravity and mass interactions. The search agents in the proposed algorithm are a cluster of weights that interact with one another using Newtonian gravity and motion principles. A comparison was made between the suggested method and various existing strategies. The obtained results confirm the high-performance in solving diverse nonlinear functions.
Details
Keywords
Shinta Rahma Diana and Farida Farida
Technology acceptance is a measure of that technology’s usefulness. Oil palm is one of the biggest contributors to Indonesia’s revenues, thus fueling its economy. Using remote…
Abstract
Purpose
Technology acceptance is a measure of that technology’s usefulness. Oil palm is one of the biggest contributors to Indonesia’s revenues, thus fueling its economy. Using remote sensing would allow a plantation to monitor and forecast its production and the amount of fertilizer used. This review aims to provide a policy recommendation in the form of a strategy to improve the added value of Indonesia’s oil palm and support the government in increasing oil palm production. This recommendation needs to be formulated by determining the users’ acceptance of remote sensing technology (state-owned plantations, private plantation companies and smallholder plantations).
Design/methodology/approach
This review’s methodology used sentiment analysis through text mining (bag of words model). The study’s primary data were from focus group discussions (FGDs), questionnaires, observations on participants, audio-visual documentation and focused discussions based on group category. The results of interviews and FGDs were transcribed into text and analyzed to 1) find words that can represent the content of the document; 2) classify and determine the frequency (word cloud); and finally 3) analyze the sentiment.
Findings
The result showed that private plantation companies and state-owned plantations had extremely high positive sentiments toward using remote sensing in their oil palm plantations, whereas smallholders had a 60% resistance. However, there is still a possibility for this technology’s adoption by smallholders, provided it is free and easily applied.
Research limitations/implications
Basically, technology is applied to make work easier. However, not everyone is tech-savvy, especially the older generations. One dimension of technology acceptance is user/customer retention. New technology would not be immediately accepted, but there would be user perceptions about its uses and ease. At first, people might be reluctant to accept a new technology due to the perception that it is useless and difficult. Technology acceptance is the gauge of how useful technology is in making work easier compared to conventional ways.
Practical implications
Therefore, technology acceptance needs to be improved among smallholders by intensively socializing the policies, and through dissemination and dedication by academics and the government.
Social implications
The social implications of using technology are reducing the workforce, but the company will be more profitable and efficient.
Originality/value
Remote sensing is one of the topics that people have not taken up in a large way, especially sentiment analysis. Acceptance of technology that utilizes remote sensing for plantations is very useful and efficient. In the end, company profits can be allocated more toward empowering the community and the environment.
Details
Keywords
Flavian Emmanuel Sapnken, Khazali Acyl Ahmat, Michel Boukar, Serge Luc Biobiongono Nyobe and Jean Gaston Tamba
In this study, a new neural differential grey model is proposed for the purpose of accurately excavating the evolution of real systems.
Abstract
Purpose
In this study, a new neural differential grey model is proposed for the purpose of accurately excavating the evolution of real systems.
Design/methodology/approach
For this, the proposed model introduces a new image equation that is solved by the Runge-Kutta fourth order method, which makes it possible to optimize the sequence prediction function. The novel model can then capture the characteristics of the input data and completely excavate the system's evolution law through a learning procedure.
Findings
The new model has a broader applicability range as a result of this technique, as opposed to grey models, which have fixed structures and are sometimes over specified by too strong assumptions. For experimental purposes, the neural differential grey model is implemented on two real samples, namely: production of crude and consumption of Cameroonian petroleum products. For validation of the new model, results are compared with those obtained by competing models. It appears that the precisions of the new neural differential grey model for prediction of petroleum products consumption and production of Cameroonian crude are respectively 16 and 25% higher than competing models, both for simulation and validation samples.
Originality/value
This article also takes an in-depth look at the mechanics of the new model, thereby shedding light on the intrinsic differences between the new model and grey competing models.
Details
Keywords
Nikita Dhankar, Srikanta Routroy and Satyendra Kumar Sharma
The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India…
Abstract
Purpose
The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.
Design/methodology/approach
Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.
Findings
The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R2 values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.
Research limitations/implications
To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.
Originality/value
The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.
Details
Keywords
V. Senthil Kumaran and R. Latha
The purpose of this paper is to provide adaptive access to learning resources in the digital library.
Abstract
Purpose
The purpose of this paper is to provide adaptive access to learning resources in the digital library.
Design/methodology/approach
A novel method using ontology-based multi-attribute collaborative filtering is proposed. Digital libraries are those which are fully automated and all resources are in digital form and access to the information available is provided to a remote user as well as a conventional user electronically. To satisfy users' information needs, a humongous amount of newly created information is published electronically in digital libraries. While search applications are improving, it is still difficult for the majority of users to find relevant information. For better service, the framework should also be able to adapt queries to search domains and target learners.
Findings
This paper improves the accuracy and efficiency of predicting and recommending personalized learning resources in digital libraries. To facilitate a personalized digital learning environment, the authors propose a novel method using ontology-supported collaborative filtering (CF) recommendation system. The objective is to provide adaptive access to learning resources in the digital library. The proposed model is based on user-based CF which suggests learning resources for students based on their course registration, preferences for topics and digital libraries. Using ontological framework knowledge for semantic similarity and considering multiple attributes apart from learners' preferences for the learning resources improve the accuracy of the proposed model.
Research limitations/implications
The results of this work majorly rely on the developed ontology. More experiments are to be conducted with other domain ontologies.
Practical implications
The proposed approach is integrated into Nucleus, a Learning Management System (https://nucleus.amcspsgtech.in). The results are of interest to learners, academicians, researchers and developers of digital libraries. This work also provides insights into the ontology for e-learning to improve personalized learning environments.
Originality/value
This paper computes learner similarity and learning resources similarity based on ontological knowledge, feedback and ratings on the learning resources. The predictions for the target learner are calculated and top N learning resources are generated by the recommendation engine using CF.
Details
Keywords
Sunita George and Raymond Greene
The work of caring has assumed utmost importance during the devastation caused by the pandemic. We employ the feminist theory of care ethics within the context of food…
Abstract
The work of caring has assumed utmost importance during the devastation caused by the pandemic. We employ the feminist theory of care ethics within the context of food provisioning during the pandemic, and examine the work of Food for Chennai, a group of micro-volunteers in the city of Chennai, India who provide home-cooked meals, free of charge, to COVID-19 patients and households that are in quarantine. Using textual and visual data from social media posts (Facebook, WhatsApp, and Instagram), interviews with an organizer of the movement, and print – media articles, we trace the evolution of this movement, and argue that this network of care could not have developed or grown without the use of digital infrastructure and the affective campaigning that it enables. We add to the scholarship of three linked bodies of work – digital activism, food ethics, and the ethics of care – by grounding our analysis in the immediacy of the crisis and suggesting avenues for thinking about ethical issues and digital activism as crisis response in the future. We conclude by offering ways of reimagining food systems that could embrace values of care in the post-pandemic world.
Details
Keywords
Priyadarshini R., Latha Tamilselvan and Rajendran N.
The purpose of this paper is to propose a fourfold semantic similarity that results in more accuracy compared to the existing literature. The change detection in the URL and the…
Abstract
Purpose
The purpose of this paper is to propose a fourfold semantic similarity that results in more accuracy compared to the existing literature. The change detection in the URL and the recommendation of the source documents is facilitated by means of a framework in which the fourfold semantic similarity is implied. The latest trends in technology emerge with the continuous growth of resources on the collaborative web. This interactive and collaborative web pretense big challenges in recent technologies like cloud and big data.
Design/methodology/approach
The enormous growth of resources should be accessed in a more efficient manner, and this requires clustering and classification techniques. The resources on the web are described in a more meaningful manner.
Findings
It can be descripted in the form of metadata that is constituted by resource description framework (RDF). Fourfold similarity is proposed compared to three-fold similarity proposed in the existing literature. The fourfold similarity includes the semantic annotation based on the named entity recognition in the user interface, domain-based concept matching and improvised score-based classification of domain-based concept matching based on ontology, sequence-based word sensing algorithm and RDF-based updating of triples. The aggregation of all these similarity measures including the components such as semantic user interface, semantic clustering, and sequence-based classification and semantic recommendation system with RDF updating in change detection.
Research limitations/implications
The existing work suggests that linking resources semantically increases the retrieving and searching ability. Previous literature shows that keywords can be used to retrieve linked information from the article to determine the similarity between the documents using semantic analysis.
Practical implications
These traditional systems also lack in scalability and efficiency issues. The proposed study is to design a model that pulls and prioritizes knowledge-based content from the Hadoop distributed framework. This study also proposes the Hadoop-based pruning system and recommendation system.
Social implications
The pruning system gives an alert about the dynamic changes in the article (virtual document). The changes in the document are automatically updated in the RDF document. This helps in semantic matching and retrieval of the most relevant source with the virtual document.
Originality/value
The recommendation and detection of changes in the blogs are performed semantically using n-triples and automated data structures. User-focussed and choice-based crawling that is proposed in this system also assists the collaborative filtering. Consecutively collaborative filtering recommends the user focussed source documents. The entire clustering and retrieval system is deployed in multi-node Hadoop in the Amazon AWS environment and graphs are plotted and analyzed.
Details
Keywords
Latha Madhuri Poonem, Rajitha Gurijala, Sindhuja Ala and Malla Reddy Perati
The purpose of this paper is to investigate the effect of initial stress and heterogeneity on the propagation of torsional waves in dissipative medium. The problem consists of dry…
Abstract
Purpose
The purpose of this paper is to investigate the effect of initial stress and heterogeneity on the propagation of torsional waves in dissipative medium. The problem consists of dry sand poroelastic half-space embedded between heterogeneous self-reinforced half-space and poroelastic medium. The frequency equation is derived in the framework of Biot's theory with some variants.
Design/methodology/approach
Torsional wave propagation in dry sand poroelastic half-space embedded between self-reinforced half-space and poroelastic medium. All the constituents here are assumed to be dissipative, heterogeneous and initial stressed.
Findings
Phase velocity and attenuation are computed against wavenumber for various values of self-reinforcement parameter, inhomogeneity parameter and initial stress. Particular cases are discussed in absence of dissipation. The numerical results are presented graphically.
Originality/value
Initial stress and heterogeneity effects on torsional waves in dry sand half-space between reinforced half-space and poroelastic medium are investigated. The frequency equation is derived, and which intern gives the phase velocity and attenuation coefficient for various values of initial stress, self-reinforcement parameter and heterogeneity parameter. From the numerical results, it is clear that as wavenumber varies phase velocity and attenuation are periodic in nature for all the cases. Particular cases are discussed in absence of dissipation. This kind of analysis can be extended to any elastic solid by taking magnetic, thermo and piezoelectric effects into account.
Details
Keywords
Subhodeep Mukherjee, Manish Mohan Baral, B. Latha Lavanya, Ramji Nagariya, Bharat Singh Patel and Venkataiah Chittipaka
Blockchain can track the material from the manufacturer to the end customers. Therefore, it can ensure the product's authenticity, transparency and trust in the retail supply…
Abstract
Purpose
Blockchain can track the material from the manufacturer to the end customers. Therefore, it can ensure the product's authenticity, transparency and trust in the retail supply chain (SC). There is a need to trace and track the retail products before it reaches the customers to check the quality of the products so that expired products can be recycled and reused, which in turn will help gain customers' trust. This research aims to investigate retail employees' behavioural intention to adopt blockchain in the retail SC.
Design/methodology/approach
To examine the behavioural intention of employees in the retail SC, the research uses three theories – the technology acceptance model; the unified theory of acceptance and use of technology; and the theory of planned behaviour. The technology acceptance model measures the employee's acceptance of blockchain in the retail SC. The unified theory of acceptance is used in this research to measure how blockchain adoption will improve the performance of the employees. The theory of planned behaviour is used in this research to measure whether the employees intend to adopt blockchain. A survey was carried out in the retail stores of India. Exploratory factor analysis and structural equation modelling were used for data analysis.
Findings
This study found that the employees of the retail stores have a positive intention and attitude to adopt blockchain technology. Further, it was found that perceived behavioural control and effort expectancy was not promoting blockchain adoption in the retail sector.
Practical implications
This study will help the retail stores' employees understand the blockchain in their operations and will motivate the top management of the retail companies to adopt this technology. The study is limited to the retail SC in India only.
Originality/value
This study uses three theories technology acceptance model; the unified theory of acceptance and use of technology; and the theory of planned behaviour, which were not used in earlier studies of blockchain adoption in the retail SC.
Details
Keywords
Aneeta Elsa Simon and Latha Ramesh
Upon completion of the case study, student will be able to discuss valuation of new-age ventures and understand how it is different from the valuation of organisations with a…
Abstract
Learning outcomes
Upon completion of the case study, student will be able to discuss valuation of new-age ventures and understand how it is different from the valuation of organisations with a longer history; analyse the considerations (quantitative and qualitative) while evaluating investments in new-age ventures; and develop a framework involving the various dimensions of investment readiness.
Case overview/synopsis
The fintech space in India has seen an upsurge of activities since 2016. The growth of Paytm, RazorPay and many such ventures and the drastic improvements in this ecosystem have been significant catalysts for this segment of new-age tech companies. Funding and valuations have seen a sharp increase, especially when businesses worldwide felt the after-effects of the pandemic, with India being home to a large number of unicorns, second only to the USA. Open Financial Technologies Ltd (OPEN TECH) is one such venture that claimed its spot as the 100th unicorn of India within a span of five years since inception. With a strong focus on disrupting the banking sector in India, this neo-bank aspires to be the equivalent of Stripe in India and eventually be a strong competitor in the international market.
Richard O’Neil is an active investor in the fintech space, based out of the UK, and he is currently looking to expand the market by considering investment options. In the process, Richard and his team have identified India as a viable and competitive market, as new venture support and funding are increasingly emphasized through policies such as Startup India, Make in India and many such more to sustain and propel its benefits. As the team was exploring ventures worth investing, Open Financial Technologies caught their attention. However, Richard, given his experience across fields and being a seasoned private equity investor, realised that valuing new-age companies is as much an art as it is a science. Multiple quantitative and qualitative aspects need to be considered while relevance of traditional valuation techniques to put a value on such entrepreneurial ventures is questioned. At this juncture, he finds it crucial to evaluate the investment readiness of OPEN TECH.
This case allows students to understand how valuation of new ventures is different from that of established companies and analyse the crucial factors worth considering while evaluating an investment proposal as a venture capitalist, which eventually helps shape the funding pitch of an entrepreneur in the space.
Complexity academic level
This case study can be useful for students undertaking graduate- and executive-level courses on business valuation and strategy and entrepreneurship, as well as entrepreneurial finance elective at the undergraduate level. One could use this case in courses on entrepreneurship and innovation, such as an introductory course on entrepreneurial finance and a course on venture capital and private equity. It also allows discussion on fintech and neobanking and the valuation of privately held companies.
Supplementary materials
Teaching notes are available for educators only.
Subject code
CSS 1: Accounting and finance.