Mathematical Modeling in Agriculture (eBook)

Sabyasachi Pramanik, Niranjanamurthy M., Ankur Gupta, Ahmed J. Obaid (Herausgeber)

eBook Download: EPUB

2024
666 Seiten
Wiley-Scrivener (Verlag)
978-1-394-23370-0 (ISBN)

Infos

The main goal of the book is to explore the idea behind data modeling in smart agriculture using information and communication technologies and tools to make agricultural practices more functional, fruitful and profitable.

The research in the book looks at the likelihood and level of use of implemented technological components with regard to the adoption of different precision agricultural technologies. To identify the variables affecting farmers' choices to embrace more precise technology, zero-inflated Poisson and negative binomial count data regression models were utilized. Outcomes from the count data analysis of a random sample of various farm operators show that various aspects, including farm dimension, farmer demographics, soil texture, urban impacts, farmer position of liabilities, and position of the farm in a state, were significantly associated with the approval severity and likelihood of precision farming technologies.

Farm management information systems (FMIS) have constantly advanced in complexity as they have incorporated new technology, the most recent of which is the internet. However, few FMIS have fully tapped into the internet's possibilities, and the newly developing idea of precision agriculture receives little or no support in the FMIS that are now being sold. FMIS for precision agriculture must meet a few more criteria beyond those of regular FMIS, which increases the technological complexity of these systems' deployment in a number of ways. In order to construct an FMIS that meet these extra needs, the authors here evaluated various cutting-edge web-based methods. The goal was to determine the requirements that precision agriculture placed on FMIS.

Sabyasachi Pramanik, PhD, is an associate professor in the Department of Computer Science and Engineering, Haldia Institute of Technology, India. He has many publications in technical conferences and journals, as well as online book chapter contributions. He is also a reviewer for and on numerous editorial boards for technical journals. He has authored one book and edited nine books, including books for Scrivener Publishing.

Niranjanamurthy M., PhD, is an assistant professor in the Department of Artificial Intelligence and Machine Learning, BMS Institute of Technology and Management, Yelahanka, Bengalore, India. He has over ten years of teaching experience and two years of industry experience as a software engineer. He has published five books and is working on numerous books for Scrivener Publishing. He has published 54 research papers in various scientific refereed journals and filed ten patents, with two granted so far. He is a reviewer for more than 20 journals and has received numerous awards.

Ankur Gupta, MTech, is an assistant professor in the Department of Computer Science and Engineering at Vaish College of Engineering, Rohtak, India. He has many publications in scientific journals and conferences and online book chapter contributions.

Ahmed J. Obaid, PhD, is an assistant professor in the Department of Computer Science, Faculty of Computer Science and Mathematics, University of Kufa, Iraq. He has over 14 years of teaching experience and is a board member on numerous scientific journals. He has published over 75 journal research articles, five book chapters, 15 conference papers, 10 conference proceedings, and has edited eight books.

Textauszug

1
Analyzing the Impact of Food Safety Regulations on Agricultural Supply Chains: A Mathematical Modeling Perspective

Nimit Kumar1, Shwetha M.S.2, Govind Shay Sharma3, Nitin Ubale4, Nuzhat Fatima Rizvi5 and Dharmesh Dhabliya6*

1College of Agriculture Sciences, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India

2Department of Food Technology, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Karnataka, India

3Department of Mathematics, Vivekananda Global University, Jaipur, Rajasthan, India

4Department of Horticulture, College of Agriculture, Parul University, Limda, Dist-Vadodara, Gujarat, India

5Symbiosis Law School, Nagpur Campus, Symbiosis International (Deemed University), Pune, India

6Department of Information Technology, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India

Abstract

With several mathematical models developed for diverse food subjects, mathematical modeling plays a significant role in the field of food engineering. There is, however, little data available on the utilization of statistical models in the foodstuff industry. This essay intends to analyze the scope and circumstances of mathematical model use in connection to the North American foodstuff and beverage sector. It examines the understanding, characteristics, and present utilization of modeling methodologies in connection to the key features of the industry. This study covered 203 food firms overall from 12 different North American nations. The size of the firm and the nation in which it operates are shown to be more relevant determinants of the utilization of statistical models than the kind of food industry. The most advanced nations are located with a better degree of knowledge and model use. Similar patterns were seen at the micro level, demonstrating that smaller and mid-category businesses had limited resources, expertise, and model adoption.

Keywords: Food industry mathematical models, modeling expertise using models

1.1 Introduction

A valuable technique for determining the influence of various system and process features on the output of a process is mathematical modeling. Modeling diverse food items and/or processes is difficult, mostly because the phenomena are not well understood, modeling experiments are complex, and there are unknowns about trustworthy data and food attributes.

While statistical models on foodstuff nature through the foodstuff chain facilitate knowledge based on food features and various events which take place, every activity, grade and foodstuff safety properties is a huge worry of both clients and the foodstuff industry.

The study of competing options is improved by foodstuff process modeling and/or multiscale simulations from foodstuff components up to the complete foodstuff supply chain (SC). It is important to remember that foodstuff tissues are multiscale aggregation having unique properties at every spatial scale, necessitating the use of multiscale modeling. Because of this, the primary goals in technological food procedures are to comprehend a specific phenomena using current theoretical knowledge and accessible data, create methods, and regulate those processes. According to [1], there are two main purposes for using foodstuff technique modeling: (i) to well realize a method, and (ii) to test potential “what-if” situations. Additionally, modeling of food products and processes might be done using sophisticated model-based methodologies. These methods could involve model-based production control or mathematically based product/process optimization. Locating “good” data points or feature representations is the goal of knowledge transfer techniques in order to improve the target model’s predictability and believability.

The use of models in the foodstuff sector often depends on static, simplistic models which don’t provide a meaningful assessment of observable methods, conditions for quality or safety, or environmental effect. Additionally, these models streamline the explanations of the food system’s mechanics and rate equations for change. Models may be divided into three categories, analytical models, numerical models, and observational models. Models may also be divided into three types based on the point of view: product, process, and product-process interactions. A newer model called multiscale [2] modeling has emerged to address the difficulty of modeling at several geographical scales. According to [3] the complexity of modeling depends on the fact that a variety of skills are required, including knowledge in food technology, applied math and statistics, technology, information technology, etc. It’s crucial to remember that every modeling technique has its drawbacks. Even though many models have been developed, [4] claims that there is very little evidence of their use in actual situations. The authors of [5] provide some scientific guidelines in modeling information extraction and formalization for addressing the function of foodstuff operators in little businesses.

Therefore, the goal of the current study was to assess the utilization of statistical models in the foodstuff sector related to the understanding of statistical approaches, the extent to which these tools are used by businesses, and obstacles to adopting mathematical modeling. Environmental aims and indicators as well as modeling of environmental consequences were also studied. This study determined the need for food modeling across a range of application sectors. According to the nation in which the firms operate, the functions of the organizations in the food chain, and the dimension of the organizations, the results were distributed.

Review of the literature
By examining published work utilizing the academic databases Web of Science, Scopus, a critical literature review was carried out. These databases located the best scientific publications about the quality and safety of food items as well as environmental models for food processes and products. No topographical limitations were used, and the hunt was only allowed to turn up research that had been published during the previous ten years. Modeling foodstuff products and dangers from a foodstuff safety belief was the main emphasis of the bulk of papers linked to modeling in the food industry.

The degree of understanding of a certain phenomenon is correlated with how difficult it is to analyze this issue. For instance, during the processing of food, a variety of heat transfer activities, like chilling, disinfecting, chilling, making, roasting, etc., take place in numerous unit operations. Mass transfer is a typical subject discussed in many publications in addition to heat transfer. Analysis of foodstuff processing processes such as baking, freezing, hydrating, filtering, dissipation, draining, osmosis, membrane separation, remoistening, aggregating, removal and repository requires modeling of mass movement. In food processing, simultaneous heat and mass transmission may be seen in roasting processes, baking processes, and food drying models. As the developing automations have distinct forms of action relying on the origin of energy transfer, modeling assumptions might vary depending on whether a food process occurs using traditional or non-thermal technologies. To ensure food safety while maintaining food quality, non-thermal processing has been developed. [6] gave one of the most recent updates on modeling heat transport in traditional and cutting-edge technology.

[7] developed quality modeling of many foodstuff grade characteristics, like taste, complexion, presence, and nutritive value. Some of the most recent efforts to model quality index were addressed by [8]. Foodstuff safety models range from risk assessment to food security to modeling in order to improve transportation and shelf life. Modeling of ecological effects and environmental indicators in the foodstuff industry is becoming more important in light of the significance of the Sustainable Development Goals established by the UN (UNESCO, 2022). Food firms, food processes, and food products are the three views that make up the scale of environmental models in the food chain.

The authors of this work recognized this as a research need since they found that examination of the deployment of statistical models [9] in foodstuff firms was not the center of the research. This study’s working premise was that food corporations don’t often employ mathematical models.

1.2 Resources and Techniques

Specifications of the survey
The investigation took place in the first half of 2018. An English-language questionnaire was created and then translated into the native tongues of the participating nations. There were 203 food firms overall, from 12 different European nations, and they were split into two groups: Asian Nations (AN) and Other North American (ONA) countries. The System Partnership Accord between the American Organization in Technology defines ITC as less research-intensive nations. Companies were picked from every region of the tested nations. The authors acknowledge that this approach only provides a “convenience sample” of food firms rather than a genuinely random sample of them. The specimen is compared to numerous produced surveys on the execution of specific tools in various countries having fewer than 80 food companies in a nation like quality management, hygiene practices, pest control, or food fraud. This is true despite the...

Produkt-Details

• Erscheint lt. Verlag: 16.10.2024
• Sprache: englisch
• Themenwelt: Naturwissenschaften ► Biologie
• Themenwelt: Weitere Fachgebiete ► Land- / Forstwirtschaft / Fischerei
• ISBN-10: 1-394-23370-1 / 1394233701
• ISBN-13: 978-1-394-23370-0 / 9781394233700