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Recognizing Named Entities in Agriculture Documents using LDA based Topic Modelling Techniques

By analyzing vast amounts of agricultural data, NLP algorithms are able to extract valuable insights and provide farmers with crucial information that can optimize their crop production. These analytics can help farmers identify the right time to plant, detect diseases or pests early on, and provide recommendations for effective pest control measures. With such precise data at their disposal, farmers can make informed decisions, leading to increased crop yields, reduced costs, and ultimately, greater profitability. In the vast landscapes that stretch across the agricultural heartlands of the world, a quiet revolution is taking place. Unseen by most, an array of advanced technologies is quietly working to transform the way crops are cultivated and harvested, promising to enhance both yield and quality. At the forefront of this transformation is Natural Language Processing (NLP) analytics, a powerful branch of Artificial Intelligence (AI) that provides invaluable insights into the complexities of agriculture.

CM contributed to challenges and future directions and has critically revised the manuscript. AK, AF, SB, RB, FR, and JS contributed to conceptualisation and writing of nutrition and climate challenges and review of the manuscript. It is only in the last few years that these methods have been applied to combining recipes, food texts, and other environmental, nutritional, and economic databases, but this work is still incipient.

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The doctor could later use a combination of NER and text classification to analyze their clinical from that appointments and flag “headache,” “anxious,” “alopecia,” and “pain” as PROBLEM entities. From there, the doctor could further categorize those problems by making assertions as to whether they were present, conditional, or absent — in this case, the headache would be present, anxiousness would be conditional, and alopecia and pain would be absent.

  • We conclude by discussing how such techniques can be used to engage and translate food challenges to stakeholders and forecast possible future applications such as novel kinds of recommender systems that encourage positive behavioral change.
  • This is why conversational systems, often known as chatbots, have gain popularity in recent years.
  • Current nutrient and environmental impact databases are not detailed enough to provide analysis and recommendations at different geographic levels (e.g. Western Europe and East Asia have very different requirements).
  • By harnessing the power of NLP, farmers can now tap into a wealth of data and insights to improve crop yield and quality.
  • QA systems are built with different components such as document processing, query reformulation, passage retrieval, and answer selection.

Through harnessing the potential of NLP analytics, farmers and agronomists are now able to make informed decisions, optimize resource allocation, and address challenges in real-time. In this article, we delve into the world of NLP analytics in agriculture, exploring its potential to revolutionize crop production and elevate sustainability to new heights. NLP enables farmers to analyze vast amounts of textual data from diverse sources such as research papers, weather reports, market trends, and social media, among others. By employing advanced algorithms, NLP can extract valuable information and patterns, providing farmers with actionable insights. For instance, by analyzing weather data combined with crop-specific information, NLP can help farmers optimize irrigation schedules and make informed decisions on when to plant, harvest, or protect their crops from adverse weather conditions. By analyzing textual data from sources like agricultural research papers, scientific journals, and even social media discussions, farmers can uncover valuable information about emerging crop varieties, disease-resistant traits, best farming practices, and innovative techniques.

Interactive Agricultural Chatbot Based on Deep Learning

The system will act as an interactive virtual assistant for farmers, answering all queries related to agriculture. This paper will go through the implementation of the chatbot using the chatterbot libraries and Django framework. We navigated the unchartered terrain of disease and pest management, witnessing how NLP shines a light on hidden patterns within agricultural texts.

NLP in agriculture

For example, the USDA has a large archive of its national nutritional recommendations organized chronologically, allowing researchers to investigate changes in nutritional recommendations across time. The FAO, on the other hand, organizes its data around global food systems with a strong mission to fight malnutrition and hunger and incorporate global UN programs. In recent years, there has been a growing interest in the application of NLP techniques to agriculture. This has led to the development of several NLP-based solutions for various agricultural applications, including crop monitoring, disease detection, and yield prediction. Here at Hitachi Solutions, we’re committed to helping organizations within the healthcare and health insurance industries do more with their data using innovative solutions and services, including natural language processing. All of our offerings come backed by decades of proven data science expertise, and we have the resources to help your organization go further, faster, and at scale.

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One such game-changing technology that is revolutionizing modern farming is Natural Language Processing (NLP) analytics. By incorporating NLP analytics into agricultural practices, farmers and agricultural experts are unlocking new possibilities and enhancing productivity in ways unimaginable before. Healthcare providers can actually use NLP to pinpoint potential pieces of content containing PHI and deidentify or obfuscate them by replacing PHI with semantic tags. Now that we’ve covered the basics, let’s discuss NLP applications in a healthcare-specific setting. Before you can use NLP on any text, all paperwork — be it clinical notes, patient records, medical forms, or anything in between — must be converted into a digital format using OCR. Despite being a major technological advancement — one that stands at the crossroads of computer science and linguistics — NLP is more commonplace than you might realize.

NLP in agriculture

By harnessing the power of NLP analytics, the industry is moving away from a surplus-driven model towards a more sustainable and efficient approach. Farmers can use NLP tools to monitor crop growth and health by analyzing data from sensors, satellite images, and weather forecasts. NLP can be used to analyze textual data from these sources to provide insights into the current state of the crop, identify potential issues, and suggest appropriate actions to be taken. These are just a few of the many possible applications for natural language processing (NLP) in the healthcare industry. Because of this, a growing number of healthcare providers and practitioners are adopting NLP in order to make sense of the massive quantities of unstructured data contained in electronic health records (EHR) and to offer patients more comprehensive care.

A guide to Natural Language Processing — Basics

In the agriculture domain, we have identified Names of Crops, Soil Types, Names of Pathogen, Crop Diseases and Fertilizers as the key entities. Our work presents a hybrid approach using the agriculture vocabulary AGROVOC and the AERTM algorithm. Hence, for those entities we propose a Latent Dirichlet Allocation (LDA) based topic modelling algorithm. These named entities can be used for creating a knowledge base which can be further used mainly in Relation Extraction systems, forums supported by various Government distinguished repositories, etc. Because of the absence of benchmark agriculture data, we tested our model using 3000 sentences extracted from reputed agriculture sites. If adopted and implemented correctly, recipes analyzed and contextualized with NLP and linked to recommender systems will be useful to the general public as well as providing an analytical tool for specialists (including nutritionists, historians, chefs, educators, and policymakers).

NLP in agriculture

Armed with this knowledge, farmers can optimize their farming techniques and adopt proven methods to improve the quality of their crops. Whether it’s selecting the right seeds or implementing precise irrigation techniques, NLP analytics empowers farmers to make data-driven decisions that lead to better crop quality. NLP analytics enables farmers to analyze and extract valuable insights from vast amounts of unstructured data, including research papers, weather reports, sensor data, and historical crop yield records.

Challenges of Analyzing Contemporary Recipes for Nutrition

There has been an increase in the world’s population, a reduction in available farmland as well as competition for agricultural land from biofuels. Advances from traditional agricultural areas have been resisted by consumers and politicians, and consequently … When it comes to providing your patients with exceptional and, in some cases, life-saving care, you can’t afford to let anything stand in your way — especially not unstructured data. The exception to this rule is data that has been deidentified — that is, data from which specified individual identifiers, such as name, address, telephone number, and so on, have been removed. Deidentified data is no longer considered to be Protected Health Information (PHI) because it does not contain any information that could possibly expose the patient’s privacy.

Finally, Herrera (2020) used a recommender system to minimize food waste and recommend recipes using (organic) locally grown food. Interestingly, this provides a link between recipes, supply chain, and modes of production. In the field of nutrition, the presentation and analysis of recipes is usually done through “technical preparation sheets.” Traditionally, these sheets contain a list of ingredients, culinary techniques, preparation times of the dishes, necessary equipment, and portion sizes. This latter quantification is carried out by manually linking to food composition tables or automatically with specific nutrition software (Tufts University, 2020).

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DM helped to shape both the idea and focus of this paper and consolidated the various components. She contributed to all sections, especially the introduction, discussion, and NP and AI aspects of the paper, as well as general editing. AS involved in conceptualisation and writing future directions, recommender systems, and NP challenges and review of the manuscript. RI was responsible for general setup, contextualisation of food as a relevant proxy for research, analysis of the different coding strategies of nutritional databases, and Section 2. XR involved in conceptualisation and writing of climate challenges, LCA database review, and review of the manuscript. CT was responsible for data, challenges, and analysis and commented on successive drafts of the manuscript.