Proceedings of the First Workshop on NLP in Agriculture and Livestock Management

NLP analytics, a subfield of artificial intelligence, focuses on extracting insights and understanding human language to make sense of vast amounts of textual data. By using advanced algorithms and machine learning techniques, NLP analytics can analyze agricultural texts, like research papers, articles, weather reports, market trends, and even social media posts related to agriculture. This valuable information can then be leveraged to make data-driven decisions, improve crop yield, manage resources effectively, and mitigate risks. In summary, the power of NLP analytics is revolutionizing the agricultural industry, particularly in optimizing crop production and improving the quality of agricultural yields. By harnessing the capabilities of NLP, farmers can gain valuable insights into various factors influencing crop growth and make data-driven decisions to enhance their farming practices.

  • Natural language processing is just starting to impact business operations across different industries.
  • Immediately, I rushed to my room and sat back with my laptop, made some google searches, and discovered that a new terminology named Natural Language Processing (NLP) is the key to the voice assistant.
  • At the very onset of my 4th semester, I came to know about the buzzword machine learning.
  • NLP analytics holds immense potential in addressing various obstacles faced by farmers today.
  • 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.
  • With such precise data at their disposal, farmers can make informed decisions, leading to increased crop yields, reduced costs, and ultimately, greater profitability.

By harnessing the power of NLP, farmers can gain valuable insights, make more informed decisions, and ultimately, foster sustainable and efficient agricultural practices. As this technology continues to evolve, we can expect exciting advancements that will revolutionize the way we grow, manage, and consume food. So, let’s embrace NLP and reap the bountiful benefits it has to offer to farmers and consumers alike. In this article, we encourage an interdisciplinary approach to the exploration of nutrition and sustainability. We highlight challenges and opportunities of using AI to analyze the food domain through recipes and present use cases that form the basis of a collaborative movement to provide a multifaceted and data-driven analysis of nutrition and sustainability.

Procedia Computer Science

One of the significant advantages of integrating NLP analytics into agriculture is the ability to gain valuable insights into crop diseases, pests, and weather patterns. By analyzing historical and real-time data, farmers can identify patterns or anomalies that may affect their crops’ health. This knowledge enables proactive measures to be taken, such as deploying preventive measures or adjusting irrigation schedules, to optimize crop growth and protect against potential threats. Overall, leveraging Natural Language Processing in agriculture holds tremendous potential for transforming crop yield and quality.

NLP in agriculture

Bushel’s software digitizes, permissions, and accelerates the flow of information, transactions, and payments through secure mobile and desktop applications. Understanding human language, including its intricacies, can be difficult even for people, let alone algorithms. The challenge is getting the algorithms to understand the words and their underlying meaning. Machine learning is beneficial when you consider the sheer number of variables that need to be accounted for in a natural learning process application to be effective.

Unleashing the Power of NLP Analytics to Optimize Crop Production and Quality

Headquartered in Fargo, N.D., the Bushel platform includes its flagship mobile app, websites, trading tools, digital payments and money facilitation, market feeds, API services, farm management software, and a professional services division focused on agriculture. Bushel complies with SOC 2 standards and debit card transactions over PCI-certified https://www.globalcloudteam.com/9-natural-language-processing-examples-in-action/ networks. Natural language processing (NLP) uses artificial intelligence and machine learning to extract meaning from human language while it is spoken. Depending on the natural language programming, the presentation of that meaning could be through pure text, a text-to-speech reading, or within a graphical representation or chart.

A further limitation of current NLP recipe analysis is related to geographically contextualizing diets, nutrients, and food footprints, which is critical for global relevance. 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). In this narrative exploration of NLP analytics in agriculture, we’ve only begun to scratch the surface of what this technology can achieve. As the evening settles over the fields, we leave with a sense of awe and anticipation for the future of farming. The marriage of ancient traditions and modern innovation paves the way for an agricultural revolution, where NLP analytics serves as a faithful companion to farmers, guiding them towards enhanced crop yield and quality. With the advancements in technology, the future of farming is experiencing a revolution like never before.

The Limitations of Famous Testing Tools that You Need to Know

“Bushel’s connectivity with thousands of farmers, grain elevators, and ag retailers presents a unique opportunity to embed digital payment functionality across the granular, paper-based, agricultural industry. Even for contemporary, digital-born recipes enriched with structured data, quantity extraction is not trivial. It is not always easy to retrieve the publication dates of these recipes, making it difficult to correctly assess the recipe’s publication date and to use it in recipe trend analysis. It is clear that while a number of knowledge sources are available, a major challenge is that there are no (or limited) direct links between and among nutrition, sustainability, and recipe databases, with differing levels of data granularity even in databases of the same type. In today’s rapidly evolving world, where technology is advancing at an unprecedented pace, the agricultural sector is also leveraging the power of innovative solutions to address the challenges it faces.

NLP in agriculture

This needs to be addressed first to enable comparison between recipes over time and space. In this section, we present a use case on automatically analyzing sugar quantities from historical apple pie recipes to illustrate some of the challenges. Insurers can use machine learning and artificial intelligence to analyze customer communication to identify indicators of fraud and flag these claims for deeper analysis. Concurrently, Andres and collaborators (Andres, 2018; Andres et al., 2018; Andres et al., 2019; de Toledo et al., 2020; Toledo et al., 2020) created the CROPPER service (CaRbon fOotprint reciPe oPtimizER). CROPPER improves an input recipe by updating its ingredients and cooking procedures to reduce its carbon footprint while keeping it savoury. CROPPER is part of the CRWB group research6 created in memory of the cook Nicole Andres (1942–2016) to manage her 60-year cooking recipe collection legacy and enhance it for data science research.

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

As we reached the conclusion of this article, we recognized that NLP analytics in agriculture is not without its challenges. While it holds immense promise, the adoption of this technology requires bridging gaps between different stakeholders, ensuring accessible and user-friendly platforms, addressing privacy concerns, and fostering collaboration between farmers, scientists, and AI experts. However, the possibilities are undeniable, with the potential to transform food production, increase global food security, and contribute towards sustainable farming practices. Through the lens of neutral storytelling, we embarked on a journey of discovery, immersing ourselves in the challenges faced by the agricultural industry.

NLP in agriculture

Many global challenges are directly related to food, nutrition, and sustainability.1 At least 6 of the UN’s Sustainable Development Goals involve food (UN, 2015). Food is also central to many countries’ economies (11% of total employment in the US and the Netherlands (Hausmann et al., 2014; FAO, 2019)) and cultural heritage (Richard and Coste, 2020). The ability to research food and recipes can help us address the challenges of sustainable and healthy eating in diverse cultural contexts, particularly given the current need to move to a more plant-based diet (Willett et al., 2019). With the rapid advancements in Natural Language Processing (NLP) analytics, the agricultural industry is experiencing a transformative shift towards sustainable practices. NLP, a branch of artificial intelligence, is revolutionizing the way farmers, researchers, and policymakers make decisions and optimize resource management in agriculture.

NLP Analytics in Agriculture: Enhancing Crop Yield and Quality

One could use a typical portion size, calculated from similar recipes, or have a portion size based on calories, but this requires further analysis and transformation. Quantities can be expressed by numerals and fractions or spelled out, and units are expressed as metric, imperial, or other measurements such as teacups. Here, not just conversion tables but contextual knowledge are needed as, for example, teacups vary in size between North American and Europe. Quantities are also not always specified (e.g., “Honig oder Zucker nach Süße der Äpfel und Gusto” or “2 sucre”) or are difficult to assess when recipes use preprocessed ingredients such as compote and/or ready-made pastry, with unknown sugar content. Clinical Named Entity Recognition Posology — shown in the image below — is a more specified version of the Clinical NER General Model.

We present the state-of-the-art and some use cases, followed by a discussion of challenges. NLP analytics holds immense potential in addressing various obstacles faced by farmers today. For instance, when it comes to pest management, NLP analytics can analyze a plethora of scientific research papers, reports, and historical data, providing farmers with invaluable insights and recommendations to mitigate pest-related risks. Instead of relying solely on traditional methods, farmers can now make informed decisions based on the analysis of an extensive knowledge base. Asano and Biermann (2019) used NLP-driven recipe analysis to examine dietary transitions toward sustainable diets but did not link this to environmental impacts, examining instead the number and composition of vegetarian and vegan recipes submitted.

Unlocking the Potential of IoT: How GPT-3’s Natural Language Processing is Transforming the Landscape of Technology

This information can help farmers to plan their harvests, manage their resources more efficiently, and make informed decisions about their operations. This shows that our farmers want relevant information today and are willing to ask for it. That is why we must develop intelligent https://www.globalcloudteam.com/ chatbots where farmers can ask in their local language any query and our bot will give a meaningful reply. The project will involve a lot of natural language processing because we can’t expect farmers to type commands to our bot (and anyways no one likes to talk to a machine!).

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Tomsk State University of Control Systems and Radio Electronics

Tomsk State University of Control Systems and Radio Electronics was founded in 1962. It contains more than 25 destinations for studying, 12 faculties.

Nowadays more than 5000 students living in more than 100 cities of Russia and CIS countries are studying with the use of distance learning technologies.

It takes the 17th place in the ranking of world universities (among Russian universities) and 1001+ place in the ranking of world universities.

Moscow State University of Technology and Management of K.G. Razumovsky

Moscow State University of Technology and Management of K.G. Razumovsky was founded in 1953. In 2010, by the order of the Ministry of Education and Science of the Russian Federation, the university was named by Count Kirill Grigoryevich Razumovsky, the first Russian president of the Imperial Academy of Sciences. It contains more than 30 destinations for studying.

In 2014, by the decision of the Ministry of Education and Science of Russia, the university was awarded the status of the First Cossack University. The university has 18 regional institutes, where about 40 thousand students are studying.

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St. Petersburg Polytechnic University of Peter the Great

St. Petersburg Polytechnic University of Peter the Great was founded in 1899.

The university has a status of Public / National research university. In July 2013, Polytechnic University was among the winners of the competition for the status of “Leading Universities of Russia”. It is included in the TOP 1000 best Russian universities. There are more than 50 destinations for studying.

St. Petersburg State Technological Institute

St. Petersburg State Technological Institute was founded in 1828. It contains 6 faculties, more than 68000 graduates, 110 of which were elected academicians and corresponding members of the Russian Academy of Sciences and branch academies.

St. Petersburg State Institute of Technology witnessed the first scientific achievements and raised hundreds of technical specialists who are the engine of financial progress in Russia.

Moscow Aviation Institute

Moscow Aviation Institute (MAI) nowadays is one of the largest universities in Russia, which has a technical destination. Moscow Aviation Institute was founded in 1930. It contains 5 branches, 13 institutes, more than 30 destinations for studying.

During all time of its existence, MAI has graduated more than 175 thousand engineers and technical specialists.

In 1980 MAI was awarded with the Order of the October Revolution. In October 2009, MAI became one of the 12 best universities in Russia. The title “National Research University” was awarded to the Institute by the decision of the Government of the Russian Federation.

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Peoples' Friendship University of Russia

Peoples’ Friendship University of Russia (RUDN) is a multidisciplinary university in Moscow, which was founded in 1960. The university is known for graduating specialists not only for Russia, but also for foreign countries.

In 2010, the American-Russian Business Union awarded gold medals “Innovations for investments to the future” to 3 innovative developments of the RUDN. In 2011, RUDN took first place in the national ranking of universities according to the criterion of “International activity”. In October 2015, RUDN joined the League of BRICS Universities, which united the most prestigious universities of the BRICS countries.

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National Research Tomsk State University (TSU) was founded in 1878.

In summer 2013, TSU won the nationwide competition of universities for the right to the status of a leading university in Russia. In 2020, the Quacquarelli Symonds agency published the ranking of the best universities in the world QS World University Ranking 2021 — one of the most authoritative rankings in the academic community. Tomsk State University entered the TOP 300 strongest universities in the world, taking the 250th position.

St. Petersburg State Agrarian University

St. Petersburg State Agrarian University was founded in 1904. It contains 8 faculties and more than 30 destinations for studying.

St. Petersburg State Agrarian University is the oldest agricultural university in the country. The University graduated highly qualified specialists who are in demand of the labor market of the agro-industrial complex of Russia.

St. Petersburg State University of Architecture and Civil Engineering

St. Petersburg State University of Architecture and Civil Engineering was founded in 1828. It is one of the oldest universities in Russia, which contains more than 20 destinations for studying.

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St. Petersburg State Forest Engineering University

St. Petersburg State Forest Engineering University was founded in 1803. It contains more than 20 destinations for studying and takes 235 place in the TOP-1000 universities in Russia.

It is the oldest forest university in the country. The whole history of the university is connected with the forest industry, with its development and changes. Now the number of specialists graduated by the university in the sector of forestry and the forest industry exceeds over 75 thousand.

Leningrad State University of A.S. Pushkin

Leningrad State University of A.S.Pushkin was founded in 1992. It contains 11 faculties and 5 branches.

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St. Petersburg State University of Civil Aviation

St. Petersburg State University of Civil Aviation was founded in 1930. It contains 4 faculties and 20 destinations for studying.

Thousands of graduates were awarded by government awards and badges “Excellent student of Aeroflot”, more than 200 were awarded the honorary titles “Honored Navigator of the USSR”, “Honored Pilot of the USSR”, “Honored Transport Worker”, and more than 20 graduates were awarded by the high title of Hero of Socialist Labor and Hero of the Soviet Union.

St. Petersburg State University of Information Technologies, Mechanics and Optics

St. Petersburg State University of Civil Aviation was founded in 1930. It contains 4 faculties and 20 destinations for studying.

Thousands of graduates were awarded by government awards and badges “Excellent student of Aeroflot”, more than 200 were awarded the honorary titles “Honored Navigator of the USSR”, “Honored Pilot of the USSR”, “Honored Transport Worker”, and more than 20 graduates were awarded by the high title of Hero of Socialist Labor and Hero of the Soviet Union.

Moscow State Technical University of M.A. Bauman

Moscow State Technical University of M.A. Bauman was founded in 1830.

In 2008, it received the European Quality Award “for striving to achieve high quality educational services in accordance with international standards.” In 2014, the Expert RA agency awarded the university a rating class “B”, meaning a “very high” level of graduates; the only university in the CIS.

In the international university ranking QS 2019/2020, Bauman Moscow State Technical University ranked  the 284th place among the TOP 1000 universities of the world, which is the fifth result among all universities in Russia.

St. Petersburg State University of Telecommunications named after Professor M. A. Bonch-Bruevich

St. Petersburg State University of Telecommunications named by Professor M. A. (SPbSUT) Bonch-Bruevich was founded in 1930. There are 30 faculties in the university.

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Izhevsk State Medical Academy

Izhevsk State Medical Academy was founded in 1933.

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Saint Petersburg State University was founded in 1724.

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Smolensk State Academy of Physical Culture, Sports and Tourism was founded in 1950. It contains 19 destinations for studying and 5 faculties.

During all time of its existence, more than 15 thousand specialists in the field of physical culture and sports, sports medicine and sports management, 600 masters of sports, MSMC and Honored Masters of Sports were graduated from Academy University. Many graduates of SSUS have gained the worldwide fame.

In 2005, the Smolensk State Institute of Physical Culture was awarded the status of “academy”.

Smolensk State University

Smolensk State University was founded in 1918. For a long time it functioned as a pedagogical university; it received the status of a classical university in 2005.

As of 2020, the university has 9 faculties and more than 30 departments; graduating conducted in bachelor’s, specialist’s, master’s and postgraduate programs. The staff consists of more than 300 teachers; about 5 thousand students are graduated.

St. Petersburg State University of Aerospace Instrumentation

St. Petersburg State University of Aerospace Instrumentation (GUAP) was founded in 1941.

In 2015, GUAP became a member of the international CDIO community and entered the international ranking of the best higher education institutions ARES-2015. In 2014, the Expert RA agency included the university in the list of the best higher educational institutions of the Commonwealth of Independent States, where it was awarded the rating class “D”.

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