A survey of the applications of text mining for agriculture

Drury, Brett; Roche, Mathieu

doi:10.1016/j.compag.2019.104864

Cited by 48 publications

(33 citation statements)

References 51 publications

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“…The core of an IR model is to assess the relevancy of a keyword query and a text document. Relevancy is determined by a similarity measure such as the cosine similarity that assumes that the queries and documents are represented as a vector of words (Drury & Roche, ). Effective IR models generally capture three heuristics, that is, TF weighting, IDF weighting, and document length normalization (Aggarwal and Zhai, ).…”

Section: Text Data Analysis Methodsmentioning

confidence: 99%

“…The utilization of text data analytics for assisting food supply chain management is rarely reported. The application of text information from the semantic web in assisting agricultural decision‐makings was emphasized in a recent survey (Drury & Roche, ). Also, the rapid development of business intelligence from consumer opinions might be useful for optimizing food production.…”

Section: Application Of Text Data In Food‐related Studiesmentioning

confidence: 99%

“…Big data has found extensive use in solving complicated real‐world issues with strong applications in fields outside of computer science, such as chemistry, biomedicine, pharmacy, and agriculture (Chiang et al., ; Drury & Roche, ). With increasing number of food‐related data produced on the semantic web, it is natural to think that the leverage of these data can benefit the food industry widely.…”

Section: Opportunitiesmentioning

confidence: 99%

“…Also, text data are generally rich in semantic content, containing people's knowledge, opinions, and preferences. They have, therefore, been used in a wide range of studies, in areas such as business intelligence, biomedical–literature mining, public health surveillance, agricultural management, and so on (Drury & Roche, ; Zhai & Massung, ). As more and more text information becomes accessible online, it has the potential to provide new insights, assist in decision‐making, and improve product and service quality (Marvin et al., ).…”

Section: Introductionmentioning

confidence: 99%

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Utilization of text mining as a big data analysis tool for food science and nutrition

Tao

Yang

Feng

2020

Comp Rev Food Sci Food Safe

168

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Big data analysis has found applications in many industries due to its ability to turn huge amounts of data into insights for informed business and operational decisions. Advanced data mining techniques have been applied in many sectors of supply chains in the food industry. However, the previous work has mainly focused on the analysis of instrument‐generated data such as those from hyperspectral imaging, spectroscopy, and biometric receptors. The importance of digital text data in the food and nutrition has only recently gained attention due to advancements in big data analytics. The purpose of this review is to provide an overview of the data sources, computational methods, and applications of text data in the food industry. Text mining techniques such as word‐level analysis (e.g., frequency analysis), word association analysis (e.g., network analysis), and advanced techniques (e.g., text classification, text clustering, topic modeling, information retrieval, and sentiment analysis) will be discussed. Applications of text data analysis will be illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer‐opinion mining, new‐product development, food knowledge discovery, food supply‐chain management, and online food services. The goal is to provide insights for intelligent decision‐making to improve food production, food safety, and human nutrition.

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Section: Text Data Analysis Methodsmentioning

confidence: 99%

Section: Application Of Text Data In Food‐related Studiesmentioning

confidence: 99%

Section: Opportunitiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Utilization of text mining as a big data analysis tool for food science and nutrition

Tao

Yang

Feng

2020

Comp Rev Food Sci Food Safe

168

View full text Add to dashboard Cite

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“…Computer programs are useful and inevitable software in information technologies. Moreover, they are used in all area of the science and life such as medicine [1], agriculture [2], law [3], physics [4], education [5] etc. These programs are written by programmers and software engineers.…”

Section: Introductionmentioning

confidence: 99%

Türkçe Sözde Algoritma Kodu için LSTM Tabanlı Kaynak Kod Üretimi

İnce

2021

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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Algorithmic thinking and programming abilities of students is controversial and popular issue in technological education programs in schools and universities. Students that have not best mathematical and analytical background may have difficulties in learning computer programing. Moreover, learning programming is highly difficult for a single individual to establish connection between discrete pseudo code of algorithm and source code. Another problem is required time to write a piece of program code. In order to solve this problem, there are some tools that tutor students to get analyze and realize relation between pseudo code and source code. In this study, we propose a deep learning method that is Long Short Term-Memory (LSTM) based source code generator from Turkish pseudo codes. For this purpose, we used Introduction to programming course exams in vocational high school as dataset to train LSTM. When users query a Turkish pseudo code of algorithm, C# source code is generated. In order to measure success of proposed system, generated source code and instructor's source code is analyzed with text similarity methods. Results show that proposed system is useful for students to learn fundamental programming skills.

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Realizing virtual research environments for the agri‐food community: The AGINFRA PLUS experience

Assante

Boizet

Candela

et al. 2020

Concurrency and Computation

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The enhancements in IT solutions and the open science movement are injecting changes in the practices dealing with data collection, collation, processing, analytics, and publishing in all the domains, including agri-food. However, in implementing these changes one of the major issues faced by the agri-food researchers is the fragmentation of the "assets" to be exploited when performing research tasks, for example, data of interest are heterogeneous and scattered across several repositories, the tools modelers rely on are diverse and often make use of limited computing capacity, the publishing practices are various and rarely aim at making available the "whole story" including datasets, processes, and results. This paper presents the AGINFRA PLUS endeavor to overcome these limitations by providing researchers in three designated communities with Virtual Research Environments facilitating the use of the "assets" of interest and promote collaboration. K E Y W O R D Sagroclimatic modeling, food safety risks assessment, food security, virtual research environment INTRODUCTIONThe developments in information and communication technologies, including big data availability and management, web and cloud technologies,as well as open science-related practices are not yet fully embraced by the Agriculture and Food Science research domain. 1,2 The fragmentation of "resources" needed to exploit these new opportunities across several and heterogeneous "places" is certainly one of the major factors hindering this uptake process, for example, data are heterogeneous and scattered across several repositories, modeling tools and supporting systems are diverse, the amount of available computing capacity varies a lot across teams and laboratories.The AGINFRA PLUS project has been set up to develop an innovative approach in Agri-food digital science practices aiming at overcoming the limitations stemming from the above settings by leveraging on existing e-Infrastructures and services. In particular, AGINFRA PLUS promotes the exploitation of Virtual Research Environments (VREs) 3 to provide designated communities with seamless access to the data, services, and

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A survey of the applications of text mining for agriculture

Cited by 48 publications

References 51 publications

Utilization of text mining as a big data analysis tool for food science and nutrition

Utilization of text mining as a big data analysis tool for food science and nutrition

Türkçe Sözde Algoritma Kodu için LSTM Tabanlı Kaynak Kod Üretimi

Realizing virtual research environments for the agri‐food community: The AGINFRA PLUS experience

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