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Article

Research on Topic Mining and Evolution Trends of Functional Agriculture Based on the BERTopic Model

by
Qiao Lin
1,†,
Zhulin Xin
2,3,†,
Shuang Peng
1,
Ruixue Zhao
1,4,*,
Yingli Nie
1,
Youtao Chen
5,
Xuebin Yin
6,7,*,
Guojian Xian
1,8 and
Qiang Zhang
9
1
Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing 100081, China
2
Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
3
National Science Library (Wuhan), Chinese Academy of Sciences, Wuhan 430071, China
4
Key Laboratory of Agricultural Integration Publishing Knowledge Mining and Knowledge Service, National Press and Publication Administration, Beijing 100081, China
5
College of Agriculture, Anhui Science and Technology University, Chuzhou 239000, China
6
Institute of Functional Agriculture (Food) Science and Technology at Yangtze River Delta, Anhui Science and Technology University, Chuzhou 239000, China
7
Anhui Province Key Laboratory of Functional Agriculture and Functional Food, Anhui Science and Technology University, Chuzhou 239000, China
8
Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
9
College of Literature, Huaiyin Normal University, Huaian 223300, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agriculture 2024, 14(10), 1691; https://doi.org/10.3390/agriculture14101691
Submission received: 21 August 2024 / Revised: 20 September 2024 / Accepted: 25 September 2024 / Published: 27 September 2024
(This article belongs to the Section Agricultural Product Quality and Safety)
Browse Figures
Figure 1
<p>Development layout of functional agriculture industries in major countries worldwide.</p> "> Figure 2
<p>Research framework.</p> "> Figure 3
<p>Document-topic distribution of global functional agriculture.</p> "> Figure 4
<p>China functional agriculture documentation-topic distribution.</p> "> Figure 5
<p>China functional agriculture theme terms bar chart.</p> "> Figure 6
<p>Annual map of global functional agriculture themes.</p> "> Figure 7
<p>Global functional agriculture theme 5-year stage distribution map.</p> "> Figure 8
<p>Global functional agriculture theme evolution map in 2013–2023.</p> "> Figure 9
<p>Thematic year distribution of functional agriculture in China.</p> "> Figure 10
<p>Five-year stage distribution of functional agriculture themes in China.</p> "> Figure 11
<p>China functional agriculture theme evolution map in 2013–2023.</p> ">
Versions Notes

Abstract

:
Based on the BERTopic model, this study analyzes 15,744 scientific papers in the field of functional agriculture from 1995 to 2024 to uncover core themes and evolutionary trends in global functional agriculture, and particularly focuses on revealing the developmental trajectory in China. The results indicate that global functional agriculture research is characterized by diverse themes and intensive study, forming a multi-topic cross-network centered on plant chemical extraction and agricultural soil research, with a focus on food nutrition, human health, and environmental protection. By contrast, China’s functional agriculture research demonstrates a more focused and in-depth approach, concentrating on functional food development and agricultural environmental protection themes, with notable growth trends in areas such as selenium-enriched products and resistant starch. Combined with China’s agricultural development environment, this study makes the following suggestions for the development of functional agriculture in China: (1) Promoting interdisciplinary cooperation between functional agriculture and other technologies. (2) Developing agricultural products with Chinese characteristics and forming Chinese functional agricultural product brands. (3) Utilizing smart farming technology to boost functional agriculture.

1. Introduction

With the continuous growth of the global population and the improvement in living standards, agriculture is facing increasingly severe challenges, and, using traditional agricultural production methods, it has become difficult to meet the ever-increasing demand for food and the requirements for improving food quality. Functional agricultural products have gradually come into people’s view, and the industrial development mode represented by functional agriculture has accelerated its expansion worldwide. Functional agriculture is a production practice that grows in soil and habitats rich in natural beneficial ingredients, or is cultivated through bio-nutritional enhancement technology and other biotechnology; it aims to achieve standardized optimization of one or more beneficial health ingredients in agricultural and sideline products based on human health needs. The development of functional agriculture with respect to strengthening the nutritional function of agricultural products, especially regarding enhancing the effective supply of micronutrients in agricultural products, and solving “hidden hunger” has become an important strategic direction for the innovative development of modern agriculture.
Functional agriculture has achieved remarkable development in many countries and regions, and in 2020, the scale of functional food in the United States reached more than USD 65 billion, accounting for 1/3 of the total sales of the food market, with per capita consumption expenditure close to USD 200. In Canada, there are more than 750 companies specializing in the R&D and production of functional foods and natural health products, with a total revenue of CAD 16.4 billion. Southeast Asian countries such as Thailand, Malaysia, the Philippines, and Laos have also expressed a desire to address the problem of malnutrition and the diseases it causes through the development of functional agriculture.
At present, the key research issues of functional agriculture are gradually becoming clearer, including two main aspects. On the one hand, the research issue will focus on “forward research”, that is, from land surveys and the evaluation of the purpose of functional agriculture, to the evaluation of the transmission pattern and health effects of functional substances in the “soil–crop–animal–food–human body” chain, and the evaluation of the transmission pattern and health effects of functional substances. For example, when discussing the “soil–crop–animal–food–human body” chain, researchers mainly focus on the issues of bioefficacy, transmission efficiency, morphology transformation, and health effect mechanism. Another research issue is “reverse control technology”, which discusses ways to meet the “personalized, standardized, and specialized” food needs of human health. In order to achieve reversing and precise control of the flow of functional substances, a process that requires more advanced engineering technology needs to be realized. Compared with traditional agriculture, functional agriculture not only focuses on yield, but also on the content of functional substances in agricultural by-products and their impact on human health. Current research focuses on the development and application of certain specific nutritional technology routes or individual functional substances, but a systematic, multi-dimensional global research summary is still lacking, especially in the development and application of “reverse control technology”. Therefore, we believe, there are still many under-explored scientific issues and technical difficulties.
Scientific and technical papers are the carriers for recording researchers’ creative scientific achievements. As the global research layout continues to spread and deepen, the number of scientific and technical papers related to functional agriculture continues to increase, and as of May 2024, there are more than 15,000 scientific and technical papers related to the field of functional agriculture in the world. However, facing the massive amount of literature, it is difficult for researchers to summarize, explore and analyze, and construct knowledge in a short period of time [1]. Topic modelling, through natural language processing technology, can automatically extract the core topics and key trends in the field of functional agriculture from a huge amount of literature, revealing the hidden knowledge structure and associative relationships, thus helping researchers to better understand the development pulse of the field, and identify the research hotspots and cutting-edge directions. In 2021, BERTopic, a new approach to topic evolution was proposed by Maarten Grootendorst. This approach overcomes the problem of inconsistency and incompatibility between density-based clustering and centroid-based sampling in traditional topic-mining methods, and outperforms traditional methods such as LDA, Top2Vec, etc., in terms of topic-identification effects, representativeness of topic terms, operability, and professionalism. Therefore, this study will analyze global and Chinese research themes and evolutionary trends in the field of functional agriculture through the BERTopic system. By sorting out the knowledge structure and developmental lineage in the relevant literature, we aim to reveal the current research hotspots, key technologies, and their application directions in the field of functional agriculture globally and in China, especially those areas that are relatively less researched but have significant potential. This will not only help scientific researchers to identify emerging themes and future research hotspots, but also provide policy makers with a basis for decision-making, enabling them to better plan and manage the innovative development strategy of functional agriculture and promote the global layout and cooperation of technology application in this field.

2. Background

2.1. The Rise of Functional Agriculture

Micronutrient deficiencies such as vitamins and trace elements are often referred to as “hidden hunger”. Global research on soil micronutrient levels and their effects on human health has been conducted in the last hundred years. For example, in 1973, Rotruck found that when erythrocyte lysates from selenium-deficient rats were incubated with ascorbic acid or H2O2 in vitro, the addition of glutathione failed to protect hemoglobin from oxidative damage, and confirmed that selenium may be involved in the process of eliminating peroxides [2]. In the same year, the World Health Organization (WHO) listed selenium as an essential trace element, confirming its role in scavenging peroxides, preventing cellular damage, and delaying cellular ageing [3]. It is estimated that about 3 billion people are already affected by hidden hunger globally, and this number is likely to grow as the deposition of trace elements in the air decreases and soil degradation continues to occur [4]. In 2008, Qiguo Zhao and the strategic research group in the agricultural field of the Chinese Academy of Sciences first proposed the concept of functional agriculture in “China’s Agricultural Science and Technology Development Roadmap to 2050”, making the judgment statement that “agricultural products should move towards nutritionalization and functionalization”. It refers to the standardized optimization of agricultural and sideline products based on human health needs, achieving one or more beneficial health components (such as minerals, biocompounds) in agricultural products through growth in naturally rich soil and environments or through bionutrient-enhancement technology and other biotechnologies [5]. Simply put, it aims to cultivate agricultural products with health-promoting functions, eliminate “hidden hunger”, and improve human health through “eating”. In 2016, Qiguo Zhao and Xuebin Yin co-authored the first book in the direction of functional agriculture, Functional Agriculture, which detailed the system construction, industrial practice, and development trends of functional agriculture [6]. In 2020, Yin published the book 100 FAQs of Functional Agriculture, answering people’s questions about functional agriculture [7]. With the introduction and development of the concept of functional agriculture, more and more micronutrients beneficial to the human body have been identified as functional ingredients, such as phytic acid, resistant starch, selenium, iron, calcium, zinc, magnesium, iodine, boron, silicon, low (GI), low gluten, omega-3 (beta-carotene), folate, vitamins, etc., GI, low gluten, omega-3, beta-carotene, folate, vitamin, amino acids, fatty acids, protein, probiotics, prebiotics, polyphenols, caffeine, flavonoids, anthocyanins, catechins, carotenoids, lutein, selenomethionine, selenocysteine, dimethyl selenide, GABA, omega-3 polyunsaturated fatty acids, etc.
With the increasing attention to nutrition and health, the development of global functional agriculture has made significant progress in many countries and regions. Some countries have established or are establishing regulations and standards related to functional agriculture to ensure the safety, effectiveness, and quality of functional agricultural products. Currently, the global functional agriculture industry has formed a production, processing, and circulation chain covering from farmland to table (Figure 1), involving breeding, crop planting, livestock and poultry farming, agricultural input use, harvesting, processing, and manufacturing, and storage and preservation. As functional agriculture has broad market prospects and large potential demand, in the future, developing functional agriculture will be a key measure to address global food challenges, achieve sustainable development, ensure food security, and promote rural revitalization, agricultural modernization, and technological innovation.

2.2. Topic Identification

Topic identification refers to the automatic extraction of valuable thematic information from large amounts of text data using various algorithms, identifying keywords contained in each topic [8]. Based on different extraction techniques, current topic-identification methods mainly include three types: those based on topic probability models, those based on weighted algorithms, and those based on ontology or knowledge bases. In the fields of text mining, information retrieval, and natural language processing, methods based on topic probability models are widely applied. The basic logic of the model is to view documents as composed of multiple topic probability distributions, generating corresponding documents through repeated iterations of the topic probability model. By estimating the model parameters, the affiliation probabilities between documents–topics and topics–words can be obtained. Classic methods include the LSI (Latent Semantic Indexing) algorithm proposed by Deerwester et al. in 1990 [9], the LDA (Latent Dirichlet Allocation) model proposed by Blei et al. in 2000 based on LSI [10], and the DTM (Dynamic Topic Model) model proposed by Blei and Lafferty in 2006 [11].
With the rise of deep learning algorithms, scholars began to explore topic-mining methods based on pre-trained word embeddings. These methods first apply pre-trained models to calculate document vectors and keyword vectors, and then embed them into the same semantic space. It is believed that documents with similar topics are close in distance in the vector space, and the closer the word vector is to the topic vector, the more it can represent the topic. BERTopic is an unsupervised deep learning model based on pre-training proposed by Maarten Grootendorst [12] in 2022. Compared with other traditional topic models, the BERTopic model has the following advantages: ① BERTopic has stronger semantic understanding ability. It uses BERT (Bidirectional Encoder Representations from Transformers), a pre-trained deep bidirectional model with powerful semantic understanding capabilities, as word vectors for topic modeling [13]. Therefore, BERTopic can more accurately capture the topic information in documents and understand the contextual meaning of polysemous words [14]. ② BERTopic has higher topic consistency. It uses Transformer and c-TF-IDF (class-based TF-IDF) techniques to create denser clusters, which helps improve topic consistency and makes the extracted topics more accurate [15]. ③ BERTopic has stronger interpretability. It retains important words in topic clustering, making the results of topics easier to interpret and understand. Therefore, this study uses the BERTopic model to identify and mine topics from the abstracts of scientific papers in the field of functional agriculture globally and in China.

2.3. Topic-Evolution Analysis

Topic evolution refers to the development process of topics characterized by words in the time dimension, reflecting the development trends and future directions of topics, including the evolution of core topics and changes in relationships between topics [16]. In the early stages, topic evolution was usually analyzed using bibliometric methods, including co-citation analysis, bibliographic coupling analysis, and citation path analysis [17]. After the emergence of the LDA model, scholars began to use LDA combined with time series methods to establish dynamic topic models for topic-evolution research. For example, Qiu et al. [18] used the DTM dynamic topic model to analyze the topic intensity evolution of domestic and international discourse, achieving dynamic identification of topic-evolution characteristics based on time series.
And with the rise of deep learning algorithms, researchers combined neural networks with LDA models to conduct topic-evolution research incorporating text semantics. For example, Ma et al. [19] analyzed the topic evolution of mental models by integrating LDA and Word2vec, revealing the life cycle-development process of topics. However, Word2vec cannot identify contextual information at the sentence or document level, limiting the understanding of text in topic-evolution research.
The BERTopic model can incorporate timestamp information when conducting topic mining, tracking topic heat changes in different time periods and visualizing them, which is conducive to mining trends and patterns of topic evolution. Therefore, this study uses the BERTopic model to study the evolution trends of topics in the field of functional agriculture domestically and internationally.

3. Research Methods

3.1. Research Approach

As shown in the specific research framework in Figure 2, this study first searched the Web of Science (Clarivate, London, United Kingdom) database for scientific papers on functional agriculture, which contains global academic journals, conference papers, and books covering a wide range of subject areas. Journals in this database are usually rigorously screened and have a high academic level and influence, which means that the papers retrieved in this database have a higher quality and reliability. In this study, the research branches of functional agriculture were used as search terms, including soil, fertilizer, seed, precision storage, precision processing, precision nutrition, and precision farming. Then it uses the NLTK (3.7, University of Pennsylvania, Philadelphia, PA, USA) natural language processing package and Jieba (0.42.1, Sun Junyi, Beijing, China) natural language processing package to preprocess the datasets for global and Chinese papers, respectively, mainly including deleting literature with missing abstracts, completing the publication dates of the literature, extracting the abstracts and publication dates of the literature, and organizing them into Excel format. The third step is to use the BERTopic model to identify and analyze research topics, topic content, and topic evolution of the processed documents, providing a detailed interpretation of functional agriculture topics.

3.2. Data Acquisition and Preprocessing

This study collects relevant keywords of functional agriculture from eight aspects: soil, fertilizer, seeds, cultivation, breeding, methodology, precision processing, and precision nutrition. By using the Web of Science Core Collection as the database, this study constructs a paper-retrieval formula. The retrieval date was 5 May 2024, spanning from 1995 to 2024, resulting in 15,890 papers. Literature unrelated to the topic, non-research literature, and literature that did not conform to the data format were removed from this study to ensure the accuracy and trustworthiness of the results, with specific labelling including the following: ① non-research literature such as news and book reviews; ② literature without authors; ③ literature without abstracts; ④ literature with no or incorrect years. Finally, a total of 15,744 articles was obtained in this study, of which 3622 are from China.

3.3. BERTopic Modeling

The working principle of the BERTopic model mainly includes the following steps:
①
Text embedding: Using a pre-trained BERT model to convert each word in the text into word vectors, bringing semantically similar words closer in vector space.
②
Text dimensionality reduction: Using the UMAP (Uniform Manifold Approximation and Projection) algorithm to reduce the dimensionality of word vectors, mapping them to a low-dimensional space while preserving important local and global structure information.
③
Text clustering: Using the HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) algorithm for clustering in the reduced vector space. HDBSCAN is a density-based clustering algorithm that finds clusters of arbitrary shape and is robust to noise and outliers. HDBSCAN has advantages over other clustering algorithms in dealing with clusters of different densities and noisy points, e.g., HDBSCAN identifies clusters by constructing a hierarchy of clusters and using stability, whereas DBSCAN may not be sufficiently effective in dealing with clusters of different densities. HDBSCAN identifies clusters through density connectivity, which is more able to capture the intrinsic structure of the data, whereas the k-means algorithm relies on distance for clustering, which will assign outliers to clusters and is more suitable for spherically distributed data than textual data.
④
Topic representation: Using the c-TF-IDF method to calculate the importance of topic words in each topic cluster, extracting topic feature words based on maximum marginal relevance. The c-TF-IDF formula is as follows:
c − T F − I D F z = b z n z × log 1 + m ∑ l = 1 t b l
where z represents the topic, b represents the word, b z represents the frequency of word b in each topic z, n z is the total number of words, m represents the average number of words in each topic z, and t is the total number of topics.
In this study, the BERTopic model parameters are set as follows: ① The text-embedding model chooses the multilingual model paraphrase-multilingual-MiniLM-L12-v2. ② In the UMAP step, n_neighbors parameter is used to construct the nearest neighbors graph and specifies the number of neighbors that should be taken into account when constructing the graph, min_cluster_size indicates the minimum number of samples that a cluster contains. n_neighbors is set to 15, meaning each sample point will look for 15 nearest neighbor sample points when constructing the graph; n_components is set to 2, mapping the original high-dimensional data to a two-dimensional space; the metric uses “cosine” similarity to measure the similarity between samples; min_dist is set to 0.0, allowing nodes after dimensionality reduction to be as close as possible without forcibly maintaining a minimum distance, thus obtaining a more detailed structure. ③ In the HDBSCAN step, considering the amount of global and Chinese literature, after several experiments, we set the min_cluster_size to 70 for global documents and 15 for Chinese literature. The cluster_selection_method is set to “eom”, indicating that HDBSCAN will use a method based on minimum density connection to select clusters. min_samples is set to 5, meaning HDBSCAN will only consider points containing at least five samples as core points; otherwise, they are considered noise.

4. Result Analysis

4.1. Global Functional Agriculture Topic Identification

After training, the BERTopic model identifies 45 research topics in global functional agriculture. The labels for each topic are determined by c-TF-IDF calculation, with the ranking of each topic positively correlated with the number of documents it contains.
To display the information of each topic in more detail, while also exploring the relationships between topics and conducting an in-depth analysis of topic content, this study uses the UMAP model to reduce the dimensionality of the topics in the document set to a two-dimensional space, obtaining the document-topic distribution in the field of functional agriculture, as shown in Figure 3. The clustered topics are represented by different colors, the grey dots represent noise literatures, with the density of points positively correlated with the number of documents included, and the distance between documents and topics is positively correlated with their degree of association. The document-topic distribution map can visually display the topic associations, topic scale, and topic heat in the global functional agriculture research field, assisting this paper in the in-depth analysis of topic content.
Looking at the distribution of various topics, Topic0-Extraction of phytochemicals has 1765 literature items and Topic1-Agriculture and soil has 1274 literature items, which are higher than the other topics, and are the two topics with the largest number of literature items and the widest distribution, highlighting the fact that phytochemical extraction and agricultural soil research in the field of functional agriculture are in the core position. This distribution of research focus is closely connected to the basic concept of functional agriculture, which aims to comprehensively improve the nutritional value of agricultural products and the sustainable development of agriculture from two perspectives: the endogenous resources of agriculture (i.e., beneficial compounds contained in plants themselves) and the exogenous environment (i.e., soil health and management). Research on plant chemical extraction focuses on how to efficiently and safely isolate active ingredients with health benefits from crops, such as antioxidants, anti-inflammatory substances, and antimicrobial compounds. By optimizing extraction processes, screening high-yield crop varieties, and improving planting conditions, it aims to increase the yield and purity of plant chemicals to meet the market demand for health foods and natural medicines. Agricultural soil research, on the other hand, explores from the exogenous condition level how to promote plant growth and improve crop quality and yield through soil-management strategies, while protecting the ecological environment from pollution. Research topics such as soil fertility restoration, microbial community construction, heavy metal pollution prevention and control, and organic matter recycling in this theme are key to developing sustainable agriculture, ensuring food safety, and maintaining biodiversity.
Apart from Topic0 and Topic1, the distribution range of other topics is smaller, but some topics are close to each other, such as Topic2-Child nutrition, Topic29-Vitamins, Topic39-Osteoporosis, Topic6-Iron element, Topic4-Zinc, Topic38-Calcium absorption, Topic8-Glycolipid metabolism and health, and Topic35-Fibre and digestive health, indicating a close correlation between these topics. The cross-pollination of multiple fields is driving the development of functional agriculture, providing new solutions to address agricultural product and nutrition issues.
In this study, the themes were divided into five larger clusters based on their content associations and similarity of theme content, which facilitates a better understanding of the associations between the themes. The five clusters are agriculture and soil, food and health, functional agricultural products, precision nutrition, and precision processing. The themes contained in each cluster, the number of themes, the percentage, and the representative words are shown in Table 1.
(1)
Agriculture and soil
The representative topic of the agriculture and soil cluster is Topic1-Agriculture and soil, which is one of the important research topics in the field of functional agriculture. From the distribution of keywords shown in Table 1, the agriculture and soil cluster focuses on agricultural soil health and restoration, soil heavy metal pollution treatment, sustainable agricultural development, water resource management, and optimized fertilizer use [20]. This topic distribution not only reveals the dilemma faced by modern agriculture under the goal of green development but also reflects the irreplaceability of functional agriculture in realizing sustainable agricultural development. The problem of soil heavy metal pollution has raised concerns among global researchers. Soil quality, health, and restoration issues are closely related to healthy agriculture. Current solutions include using new materials such as biochar to adsorb harmful substances and thereby improve soil structure [21], but the soil-pollution problem still needs to be traced back to its source, achieving control, detection, and treatment of heavy metal substances from the source. In addition to soil, water resources are also a focus of functional agriculture. Improperly managed wastewater can also cause soil heavy metal pollution, especially wastewater containing chemical pollutants [22]. Therefore, researchers are focusing on finding safe and effective methods to treat and reuse wastewater, alleviating water resource shortages while preventing pollutants from entering the food chain. This topic distribution indicates that one of the research goals of functional agriculture is to develop environmentally friendly agricultural technologies, actively seeking practical solutions to accelerate the green transformation of agricultural production, ensure agricultural food safety, and maintain the ecological balance of nature.
In addition, the integration of agricultural research with emerging technologies has become an important research trend, especially with information technology, data science, and artificial intelligence technologies. For example, IoT technology allows real-time monitoring of field environments through sensors, data collection, and remote monitoring, allowing farmers to access key data such as soil moisture, air temperature, light intensity, etc. in real time, and to make precise irrigation and fertilizer decisions based on these data to improve crop yield and quality [23]. The water- and fertilizer-integration technology in precision agriculture carries out precise irrigation and fertilizer application through automated equipment to ensure that the water and nutrient requirements of crops are scientifically matched during the growth process, and it can also use IoT sensors to monitor the soil condition and automatically adjust the irrigation and fertilizer plan in combination with meteorological data to avoid water and fertilizer wastage to the maximum extent, which can not only reduce the production cost, but also improve the health of the soil and the nutritional value of the crops [24].
(2)
Food and Health
The food and health cluster includes a rich variety of topic categories, with representative topics including Topic8-Glycolipid metabolism and health, Topic13-Pathogen detection, Topic17-Probiotics, Topic22-Antioxidants and disease, etc., reflecting the close connection between functional agriculture and food health. Functional agriculture, through scientific planting and bionutrition-enhancement technologies, produces agricultural products with specific health functions, directly or indirectly increasing disease-prevention levels and bodily health functions. Diabetes is one of the typical diseases related to glucose and lipid metabolism. Whole grain products in agricultural products are beneficial for regulating glucose enzymes and hyperglycemia. Functional agriculture can increase the content of anti-diabetic compounds in seeds by optimizing the optimal germination conditions of grains, making grains functional foods [25]. In addition, functional agriculture also has a promoting effect on the detection of pathogens. By improving the natural resistance of crops and reducing the use of pesticides, it indirectly reduces the probability of pathogen residues in food, and combined with pathogen-detection technology, it jointly ensures food safety. For example, drug-resistant pathogens may lead to multidrug-resistant foodborne diseases. Ambreen Bano et al. [26] studied the phytochemical components of two North Indian cultivated varieties, Lalit and Shweta, evaluated their bioactive potential for antibiotic resistance, and explored the reasons why plants have antibacterial potential, realizing the improvement of the safety potential of agricultural products from the perspective of the plants themselves. Functional agriculture also focuses on crops rich in specific nutrients. For example, crops rich in specific fibers and prebiotics are beneficial for promoting the proliferation of intestinal probiotics and maintaining human intestinal health [27]; calcium-rich agricultural products can effectively enhance human calcium intake and maintain bone health [28]; selenium-rich, potassium-rich, and vitamin-rich crops are beneficial for clearing free radicals, maintaining blood pressure stability, and preventing chronic cardiovascular diseases [29]. Overall, functional agriculture cultivates agricultural products with specific health benefits through scientifically innovative methods, directly or indirectly enhancing human disease-prevention capabilities and health levels, reflecting the potential of agriculture in promoting human health.
(3)
Functional agricultural products
The functional agricultural products cluster is closely related to agricultural products, with representative topics including Topic11-Highly resistant starch, Topic12-Functional vegetable, Topic15-Dairy products, Topic19-Flavonoid, etc., indicating that functional agriculture research covers a wide range of foods, including plant-based foods, animal-based foods, terrestrial crops, marine resources, natural ingredients, and food extracts. Cruciferous vegetables are an important vegetable category that functional agriculture focuses on. Waseem et al. [30] studied the nutritional value, functional characteristics, and safe preservation of cabbage powder, comparing the nutrient content of raw and processed cabbage, and pointed out that microwave heating is an effective technique for reducing the content of toxic substances in cabbage powder. Shree et al. [31] provided a detailed introduction to the health functions, processing methods, improvement and protection methods, and production methods of cruciferous vegetables, pointing out that cruciferous vegetables such as kale, collards, kohlrabi, and Brussels sprouts are the main sources of secondary metabolites (i.e., flavonoids, anthocyanins, carotenoids, polyphenols, vitamins, minerals, etc.), and long-term consumption is beneficial for preventing and treating the risk of obesity, cancer, atherosclerosis, metabolic syndrome, and other diseases.
It is worth noting that as research on functional agricultural products deepens, it becomes particularly important to explore the long-term effects of these foods on human health. For example, Zhang et al. explored the role of selenium protein consumption in the prevention and treatment of cardiovascular diseases, osteoarthritis, tumors, and selenium-deficiency endemic diseases [32]. Although some functional foods have shown to have significant health benefits, systematic long-term follow-up studies are still limited. Therefore, strengthening scientific research in this area is essential to verify the health effects of functional agricultural products. In addition, in order to ensure that functional agricultural products sold in the market comply with quality and safety standards, it is necessary to formulate and improve corresponding regulations and industry standards. China’s All-China Federation of Supply and Marketing Cooperatives, the Xi’an Geological Survey Center, and the Hunan Province Selenium-enriched Bio-industry Association have issued standards for selenium-enriched agricultural products, standards for the classification of selenium content of selenium-enriched selenium-containing foods, and standards for the selenium content requirements of selenium-enriched agricultural products, which not only protect the rights and interests of consumers but also help to regulate the market order and promote the healthy development of the industry.
(4)
Precision Nutrition
The precision nutrition cluster enhances the nutritional components of crops through optimized planting and cultivation methods to meet specific human health needs. This cluster includes topics related to nutritional components, such as Topic4-Zinc, Topic5-Selenium, Topic6-Iron element, Topic27-Carotenoids, Topic29-Vitamins, Topic35-Plant active ingredients, etc. Zinc and selenium are essential trace elements for the human body, playing important roles in immune regulation and growth and development. Functional agriculture focuses on research on various forms of trace elements, promoting human absorption of trace elements through soil improvement, scientific fertilization, and cultivation of crops rich in trace elements. For example, Singh et al. [33] applied zinc oxide nanoparticles (ZnONPs) to determine zinc biofortification in potatoes and tomatoes, indicating that biofortification using ZnONPs is a cost-effective and eco-friendly method that can replace harmful chemical zinc fertilizers. Parra-Torrejón et al. [34] incorporated zinc ions into calcium phosphate nanoparticles to prepare multifunctional nanomaterials for efficient agronomic enhancement and plant protection; they showed that this material can both provide nutrients for crops and protect plants from bacterial and other diseases. Deliboran [35] investigated the effect of foliar application of sodium selenate on the selenium content of corn, and the experiment showed that the Se content in corn grains was proportional to the amount of selenate foliar fertilization.
Iron, as a key element in the synthesis of hemoglobin and myoglobin, is closely related to oxygen transport. In the field of functional agriculture, methods such as soil fertilization and crop breeding are used to increase the iron content in agricultural products [36]. Vitamins are organic compounds essential for maintaining normal physiological functions of the human body. Topic words such as vitamin deficiency, hydroxyvitamin, and supplements indicate that functional agriculture has extensive research on vitamins, and multiple studies have shown how to meet human vitamin needs through plant-based dietary supplements [37].
(5)
Precision processing
The precision processing cluster enhances the nutritional value and functionality of agricultural products through novel food-processing methods. This cluster includes topics such as Topic0-Extraction of phytochemicals, Topic3-Biopolymers, Topic16-Finely processed meat, Topic30-Refined potatoes, Topic31-Nanotechnology, etc. Research on extraction methods of phytochemicals is the focus of this cluster, with related literature accounting for 73.7% of all literature in this cluster. Phytochemicals are naturally occurring compounds in plants with biological activity. The most frequently occurring topic words in this cluster include: phenolics, flavonoids, citrus, mango, etc., indicating that a large amount of research focuses on how to efficiently extract beneficial components from fruits or other plants for use in food additives and health products, thereby enhancing the nutritional value and functionality of products.
It is worth noting the growing importance of research and development of precision processing technologies, such as high hydrostatic pressure, ultrasound, pulsed electric field, and supercritical fluid [38]. These technologies not only improve the extraction efficiency of beneficial compounds, but also maintain the nutritional and functional integrity of the produce throughout processing. For example, studies on the extraction methods and processing techniques of palm sugar [39], the effects of processing methods on the nutritional value and antioxidant properties of Lepidium sativum sprouts [40], the impact of phytochemicals and antioxidant properties on the nutritional value and processed products of Sacha Inchi [41], and the industrial application of Cornelian cherry as a functional food ingredient [42] demonstrate the role of precision processing technologies in retaining the original nutritional value and functional properties of agricultural products. By adopting advanced processing technologies, the loss of nutrients can be minimized while the stability and shelf-life of the products can be improved, thus providing consumers with higher-quality functional foods.

4.2. Chinese Functional Agriculture Topic Identification

After training, the BERTopic model identifies 15 research topics in Chinese functional agriculture. The labels for each topic are determined by c-TF-IDF calculation. The document-topic distribution for each topic in the Chinese functional agriculture field is shown in Figure 4, the grey dots represent noise literatures. As the number of topics in the Chinese functional agriculture field is relatively small, a bar chart is used to display the 15 topics and the top 10 most frequent topic words (Figure 5).
Looking at the distribution of various research topics, Topic0-Functional food is the research focus in the Chinese functional agriculture field. This topic contains 1958 articles, accounting for about 54% of the total volume of Chinese functional agriculture literature, and occupies a central position in Figure 4. The prominent position of Topic0-Functional food indicates that Chinese scholars pay high attention to functional foods. Functional foods can provide additional health benefits beyond basic nutrition, and their research and development have become an important branch of the modern food industry. Topic0-Functional food is closely connected with Topic7-Curcumin and resveratrol, Topic14-Buckwheat, Topic9-Grain, Topic13-Amino acids, indicating that the development of functional foods not only focuses on single nutritional components but also considers the combination and application of various composite components. Grains and buckwheat are agricultural products that functional foods focus on [43].
Topic5-Soil and heavy metals, Topic6-Soil and microorganisms, Topic10-Crops and salt and drought tolerance, Topic14-Biochar show a clustering trend, indicating that functional agriculture not only focuses on the nutritional value of food itself but also on sustainable agricultural development and protection of the agricultural production environment. Agricultural soil is the connecting link for related topics. Soil microbial communities can decompose organic matter, provide nutrients needed by plants, and also inhibit the proliferation of pathogens, maintaining the stability of the soil ecosystem. However, soil heavy metal pollution will affect microbial activity, thereby affecting soil quality and crop growth. Using improvement methods such as foliar fertilizers and biochar is an effective solution to address soil pollution, for example, spraying zinc-containing foliar fertilizers to alleviate cadmium pollution [44], applying MgO-NPs to improve plant tolerance to heavy metal toxicity [45], and using phosphate modification to improve the adsorption performance of biochar for cadmium and lead [46].
Topic3-Zinc-rich and Topic12-Micronutrient, two closely related topics, are associated with microelement enrichment. Zinc, as an important trace element in the human body, participates in enzyme synthesis, cell division, immune system, and other functions, making zinc-rich foods a research focus in micronutrients. Topic4-Resistant starch and Topic8-Rice and Nutrition are two closely related topics. Resistant starch, as a special dietary fiber, can control appetite through satiety, reduce glucose levels in the stomach and small intestine, and increase short-chain fatty acid content in the colon, thereby controlling glucose production, promoting gluconeogenesis, maintaining glucose and lipid balance, improving pancreatic function, and preventing and treating metabolic syndrome [47]. In rice variety-improvement research, cultivating rice varieties with excellent agronomic traits, high flavor, and nutritional quality is both a response to people’s demand for cereal foods and a response to the development of healthy and green agriculture. For example, zinc-rich rice can effectively prevent zinc deficiency-induced child-developmental delays [48], while anthocyanin-rich rice has a unique appearance and antioxidant function [49].
Overall, Chinese functional agriculture research includes multiple aspects such as food nutrition enhancement, environmental sustainability development, and crop improvement. It emphasizes the development of functional foods with health benefits, focuses on the combination of composite nutritional components, such as curcumin, resveratrol, grains, buckwheat, and amino acids, reflecting the diversification of functional agriculture and functional food research and development.

4.3. Comparative Analysis of Functional Agriculture Topic Evolution

Through topic analysis, it can be found that there are differences in research focuses between global and Chinese functional agriculture fields. Generally speaking, the development of global functional agriculture has gone through a starting period, a rapid development period and a deepening research period, with the topics ranging from few to many, the clustering gradually becoming clearer, and the focus being on technological progress. Although China’s functional agriculture started later, it has developed rapidly since the clustering period, with diversified research themes and emphasis on the layout of the whole industrial chain from soil to food. Both global and China’s functional agriculture have paid attention to food nutrition and health, but there are differences in specific research hotspots and time nodes. Tracking and identifying research hotspots in the field of functional agriculture and conducting topic-evolution analysis can help researchers quickly discover and evaluate hot topics in a timely manner. Therefore, this study visualizes and compares the annual quantity distribution of each topic in functional agriculture (Figure 6 and Figure 9), the 5-year periodic topic quantity distribution in the field (Figure 7 and Figure 10), and the topic evolution during the past 10 years in functional agriculture (Figure 8 and Figure 11), following the idea of “from time points to time periods”.
The topic-evolution trend of global functional agriculture is shown in Figure 6 and Figure 7, with colored dots representing literature on different themes and grey dots representing noise literature. It can be found that the development of global functional agriculture can be roughly divided into three stages: the initial stage (1995–2003), the rapid development stage (2004–2015), and the in-depth research stage (2016–present). Though related research began in 1995, during the 20th century, functional agriculture research had few results, few topics, and slow development. It was not until 2004 that the topic distribution of functional agriculture began to take shape, with various topics beginning to show a blurred-edge clustering distribution trend.
From 2004 to 2015, global functional agriculture topics were in a rapid development stage, with the clustering of various topics becoming increasingly clear. During this period, biotechnology, gene editing, precision agriculture technology and other technologies advanced significantly, providing technical support for the development of functional agriculture, and countries represented by China, the United States, Canada, and Japan began to pay attention to the issue of food security and the issue of invisible hunger, increased investment in and attention to agricultural research and development, and provided a stable research and development environment for the development of functional agriculture. Among them, Topic0-Extraction of phytochemicals showed the fastest scale-up and most obvious expansion. The agriculture and soil cluster and the precision nutrition cluster were closely related, with an increasing number of sub-topics, marking technological progress in nutrient content, nutrient extraction, and nutrient processing in functional agriculture centered on precision processing. After 2015, there was no significant change in the scale of topic clustering in functional agriculture, and various research topics began to focus on in-depth research.
Figure 8 shows the topic-evolution trend from 2013 to 2023 through a ridge plot. The height of the ridge represents the publication density of the corresponding topic in specific years, and the fluctuation of the ridge over time represents the publication trend of topic papers. As the figure shows, various topics have focused on in-depth research in the past ten years, among which are Topic4-Zinc and Topic19-Flavonoid. We hypothesize that this may relate to the outbreak of COVID-19 in 2020. The spread of pandemics has caused a worldwide health crisis; nutrients such as zinc and flavonoids are thought to boost immunity, so there has been a significant growth in the attention to functional agriculture research topics related to them. The growth in virus research suggests that researchers are beginning to focus on the food, agriculture, virus, and health associations, such as how nutritional interventions can reduce the risk of new coronavirus infections, which shows that functional agriculture in the new era not only focuses on food and nutrition, but also emphasizes the mechanisms of diseases and viruses. Agriculture and health are closely connected, and emphasizing agriculture is protecting human health.
The topic-evolution trend of Chinese functional agriculture is shown in Figure 9 and Figure 10, and can be roughly divided into the initial stage (1996–2008), the transitional stage (2008–2014), and the rapid development stage (2014–present). Related research began in 1996, and comparing with global functional agriculture, Chinese functional agriculture research started later, developed more slowly, and had fewer topics. After Qiguo Zhao first proposed functional agriculture and clarified its unique concepts, ideas, and goals in 2008 [50], functional agriculture gradually received attention and promotion from academia. Research on functional agriculture in China began to show systematization and clustering, with research focus and direction gradually becoming clear.
In 2014, functional agriculture topics began to take shape and showed a scattered distribution, with Topic0-Functional food and Topic1-Bioassay Methods being the first to form topic clusters, indicating that functional foods rich in nutrients were the first research area that Chinese researchers focused on. The years 2015–2023 marked a stage of explosive growth in Chinese functional agriculture research, signifying that Chinese functional agriculture had transitioned from initial exploration to comprehensive development, with research topics showing diversification, extensiveness, and clustering characteristics.
Currently, Topic0-Functional food still holds a dominant position in the field of functional agriculture in China, but the research content is no longer limited to functional foods themselves. Topics such as bioactive substance-detection methods, micronutrient-enrichment techniques, soil health and pollution treatment, crop stress-resistance improvement, and applications of biochar in agriculture indicate that Chinese functional agriculture has layout across the front, middle, and back ends of the industrial chain, which aligns with the innovative chain layout of “soil–fertilizer–crop (animal)–food–human body” proposed by the research team of Zhao and Yin [5].
Since 2019, Topic2-Selenium-enriched, Topic4-Resistant starch, Topic5-Soil and heavy metals, and Topic13-Amino acids have shown the most significant growth (Figure 11), representing China’s emphasis on improving the nutritional value of agricultural products and promoting national health, and also marking China’s positive trend towards nutrition-enriched and green healthy agriculture transformation.
There are both commonalities and differences between China’s research focus, development characteristics, and recent developments in the field of functional agriculture compared to the overall development trend of functional agriculture. Specifically:
(1)
Functional agriculture research topics are broad and detailed globally, while Chinese research topics are focused and in-depth. Global functional agriculture research covers a wide range of fields, with intensive research and abundant results. Plant chemical extraction, agricultural soil management, and precision nutrition are all areas of focus. In comparison, China’s research in the field of functional agriculture is more concentrated on specific topics, such as functional food development, bioactive substance detection, micronutrient-enrichment technology, soil health and pollution treatment, and crop stress-resistance improvement, showing focused and in-depth characteristics in functional agriculture research.
(2)
Functional agriculture focuses on agricultural nutrition and sustainable development, while China emphasizes food development, environmental protection, and crop cultivation. Research in the field of functional agriculture overall is based on plant chemical extraction and agricultural soil management, focusing on how to enhance the nutritional value of agricultural products and agricultural sustainability through endogenous nutrition and exogenous environment. It places more emphasis on technological innovation and interdisciplinary integration, such as the joint promotion of functional agriculture development by botany, nutrition, environmental science, and other disciplines. By contrast, China pays more attention to functional food development and agricultural environmental protection. The development trend of functional food dominating is related to China’s large population and urgent need for healthy food. In addition, Chinese research also focuses on soil heavy metal pollution, crop stress resistance, and biochar applications, indicating that China is implementing a dual-driven strategy of agricultural production and environmental health.
(3)
In the past five years, functional agriculture has focused on healthy food and disease prevention, while China places more emphasis on functional food and nutritional enhancement.
The overall functional agriculture field has had nearly thirty years from its origin to its formation as a system, with different developmental characteristics at different time stages. And in the recent five years, functional agriculture has formed characteristics of significant topic clustering, a considerable number of topics, and expanding research boundaries, and has begun to focus on the relationship between healthy food and disease prevention. Compared to the overall research trend of functional agriculture, China places more emphasis on nutritional enhancement, environmental sustainability, and crop improvement. Selenium-rich products and resistant starch are the fastest-growing functional food topics, reflecting China’s pursuit of improving the nutritional value of agricultural products, ensuring food safety, and promoting the green transformation of agriculture.

5. Conclusions and Outlook

This paper uses the BERTopic model to identify topics, interpret topics, and analyze topic evolution in the field of functional agriculture. It finds that research in the field of functional agriculture has formed a multi-topic, multi-disciplinary cross-complementary research network, with plant chemical extraction and agricultural soil research being two core hotspots. The research boundaries of functional agriculture continue to expand around multiple dimensions such as food nutritional content, human metabolism and health care, and agricultural environmental protection. The related knowledge involves fields such as agronomy, botany, zoology, nutrition, and biology, with multiple disciplines innovating together to promote the development of functional agriculture towards health, nutrition, breadth, and sustainability. China’s functional agriculture field is consistent with the overall development trend, showing characteristics of accelerating development speed, enriching research topics, and expanding research scope.
Based on the current status and trend characteristics of China’s functional agriculture development, we propose the following three insights:
(1)
Promoting interdisciplinary cooperation between functional agriculture and other technologies. The development of functional agriculture faces many complex problems, such as soil degradation, crop nutritional imbalance, and agricultural adaptability under climate change. These issues transcend the scope of a single discipline and require the combination of soil science, plant nutrition, ecology, environmental science, nutrition, and other fields to achieve technological breakthroughs and promote technological complementation and innovation. Currently, China’s functional agriculture is at a critical stage of development, needing to maintain development speed and expand research scope, and interdisciplinary cooperation will become the key to promoting the development of functional agriculture and solving complex agricultural challenges [51].
(2)
Developing agricultural products with Chinese characteristics and forming Chinese functional agricultural product brands. China has rich agricultural resources and local characteristic agricultural products, such as black fungus from Northeast China, Pu’er tea from Yunnan, red dates from Xinjiang, Tieguanyin tea from Fujian, and chili peppers from Sichuan. Currently, selenium-rich agricultural products have become representatives of China’s functional agriculture, such as selenium-rich rice, selenium-rich tea, and selenium-rich water. However, functional agriculture is not limited to the selenium-rich industry. The “single prosperity” of selenium-rich products in the field indicates an imbalance in the product types of China’s functional agriculture. Therefore, in-depth research and development of characteristic agricultural resources need to be conducted in the future. And by transforming those scientific achievements into competitive functional products, it will reflect the diversity of Chinese agriculture, and promote farmers’ income and rural economic development.
(3)
Utilizing smart farming technology to boost functional agriculture. With the advancement of digital technologies and the rise of the AI4S research paradigm [52,53], smart agricultural technology is becoming an important driving force for the innovative development of functional agriculture [54]. Smart agricultural technology is represented by big data analysis, Internet of Things, artificial intelligence, agricultural robots, intelligent breeding, soil monitoring, pest and disease identification, intelligent expert systems, etc. [55], providing a technical foundation for the upgrade of functional agriculture, enabling it to develop in a more precise, high-quality, and sustainable direction. Therefore, smart agriculture technology should be integrated with functional agriculture, such as combining deep learning algorithms and near-infrared calibration platforms with the Internet of Things (IoT), which can be used to rapidly and quantitatively detect selenium content in rice samples [56].
In conclusion, although China has unique emphases in the field of functional agriculture, the common goal of functional agriculture research is to enhance the nutritional value of agricultural products, promote sustainable agricultural development, and improve human health and living standards. Through technological innovation, policy support, public education, and international cooperation, China still has great potential for technological breakthroughs in the field of functional agriculture, contributing more to global food security and health. Facing the future, the world should work hand in hand in the field of functional agriculture research, jointly facing new opportunities and challenges in agricultural development.

Author Contributions

Conceptualization, Q.L., X.Y. and Y.C.; Methodology, Q.L., Z.X. and R.Z.; Software, Q.L., Z.X. and G.X.; Validation, Q.L., Z.X. and Q.Z.; Formal analysis, Q.L., Z.X. and S.P.; Resources, Y.N., X.Y. and Y.C.; Data curation, Q.L., Z.X. and Y.C.; Writing—original draft, Q.L., X.Y., S.P., Y.N. and Y.C.; Visualization, X.Y., G.X. and Q.Z.; Supervision, R.Z., Y.N. and X.Y.; Funding acquisition, R.Z. All authors have read and agreed to the published version of the manuscript.

Funding

National Science and Technology Major Project (2021ZD0113705); Innovation Project of AII, CAAS (CAAS-ASTIP-2024-AII).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Development layout of functional agriculture industries in major countries worldwide.
Figure 1. Development layout of functional agriculture industries in major countries worldwide.
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Figure 2. Research framework.
Figure 2. Research framework.
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Figure 3. Document-topic distribution of global functional agriculture.
Figure 3. Document-topic distribution of global functional agriculture.
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Figure 4. China functional agriculture documentation-topic distribution.
Figure 4. China functional agriculture documentation-topic distribution.
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Figure 5. China functional agriculture theme terms bar chart.
Figure 5. China functional agriculture theme terms bar chart.
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Figure 6. Annual map of global functional agriculture themes.
Figure 6. Annual map of global functional agriculture themes.
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Figure 7. Global functional agriculture theme 5-year stage distribution map.
Figure 7. Global functional agriculture theme 5-year stage distribution map.
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Figure 8. Global functional agriculture theme evolution map in 2013–2023.
Figure 8. Global functional agriculture theme evolution map in 2013–2023.
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Figure 9. Thematic year distribution of functional agriculture in China.
Figure 9. Thematic year distribution of functional agriculture in China.
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Figure 10. Five-year stage distribution of functional agriculture themes in China.
Figure 10. Five-year stage distribution of functional agriculture themes in China.
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Figure 11. China functional agriculture theme evolution map in 2013–2023.
Figure 11. China functional agriculture theme evolution map in 2013–2023.
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Table 1. Global functional agriculture research thematic cluster.
Table 1. Global functional agriculture research thematic cluster.
Topic ClusterTopic NameNumber of Topics Containing LiteratureProportionTopic Feature Words
Agriculture and soilTopic1: Agriculture and soil12748.09%environmental, soil, wastewater, ecological, agricultural, agriculture, fertilizers, organic, sustainable
Food and HealthTopic8: Glycolipid metabolism and health2201.40%metabolic, obesity, metabolism, diet, adipogenesis, adipocytes, dietary, diabetics, diabetic
Topic13: Pathogen detection1500.95%bacterial, bacteria, biosensor, coli, pathogens, salmonella, pathogen, biofilm, immunosensor
Topic17: Probiotics1120.71%probiotic, lactobacillus, lactobacilli, synbiotic, lactiplantibacillus, soymilk, bacteria, fermented, lactis
Topic22: Antioxidants and disease850.54%inflammation, inflammatory, disease, chronic, pathogenesis, disorders, dysfunction, antioxidant, neuroinflammation
Topic25: Antihypertensive experiment790.50%hypertension, hypertensive, antihypertensive, arterial, acetaminophen, fructose, glutathione, dose, insulin, glucose
Topic37: Gastrointestinal probiotics420.26%microbiome, microbiol, microbial, prebiotic, bacteria, bacterial, probiotic, digestible, gastrointestinal
Topic38: Calcium absorption410.26%calcium, dietary, calcification, diets, intake, foods, phosphate, consumption, carbonate
Topic39: Osteoporosis380.24%osteoporosis, osteoblastic, osteoclast, osteoclasts, bone, osteo, osteocalcin
Topic41: Fiber and digestive health350.22%fiber, dietary, diets, digestive, cholesterol, digestion
Functional agricultural productsTopic11: Highly resistant starch1841.17%starch, digestible, potato, glucose, glycemic, gelatinized, cooked, carbohydrate
Topic12: Functional vegetable1510.96%vegetable, tomatoes, tomatine, cabbage, cultivars, fruit, antioxidant
Topic15: Dairy products1280.81%dairy, milk, lactating, lactation, lactose, lactostatin, cow, nutritional
Topic19: Flavonoid1020.65%flavonoids, flavonols, flavanone, flavonol, flavanones, flavones, flaxseed
Topic20: Offtake980.62%seaweeds, algae, ulva, sargassum, brown, marine, queensland, laminarin, caulerpa
Topic21: Mushrooms930.59%mushroom, edible, extracts, cultivated, glucans, culinary, glucan, medicinal, nutritional
Precision NutritionTopic4: Zinc3272.08%zinc, zn, znonps, maize, znso4, grains, soil, wheat, soils, grain
Topic5: Selenium2981.89%se, selenium, selenate, selenite, selenomethionine, enriched, biofortification, sodium, element, inorganic
Topic6: Iron element2421.54%iron, fe, anemia, ferritin, deficiency, fortified, ferrous, children, hemoglobin, anaemia
Topic27: Carotenoids740.47%carotenoids, bioavailability, phytoene, carotene, carnosinase, carnosine, phytofluene, carotenoid
Topic29: Vitamins620.39%vitamin, hypovitaminosis, hydroxyvitamin, supplements, dehydrocholesterol, supplementation, supplement, d3, d2, osteoporosis
Topic35: Plant active ingredients580.37%resveratrol, zeaxanthin, gliadin, fructofuranoside, talassemia, xanthophyll, biotecnologie, microparticles, phosphatidylglycerol, thalassemia
Precision processingTopic0: Extraction of phytochemicals176511.21%phytochemicals, cultivars, citrus, fruit, extracts, phenolics, mango, bioactive
Topic3: Biopolymers4042.57%lipids, biopolymers, oils, polymers, microcapsules, emulsification, acids
Topic16: Finely processed meat1140.72%protein, meat, peptides, bioactive, fatty, burgers, beef
Topic30: Refined potatoes580.37%sweetpotatoes, batatas, potatoes, cultivars, flour, cultivar, frying, cooking
Topic31: Nanotechnology550.34%nanotechnology, nanotechnological, nanoscience, nanofertilizers, nanoscale, nanosensors, nanofoods, technologies
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Lin, Q.; Xin, Z.; Peng, S.; Zhao, R.; Nie, Y.; Chen, Y.; Yin, X.; Xian, G.; Zhang, Q. Research on Topic Mining and Evolution Trends of Functional Agriculture Based on the BERTopic Model. Agriculture 2024, 14, 1691. https://doi.org/10.3390/agriculture14101691

AMA Style

Lin Q, Xin Z, Peng S, Zhao R, Nie Y, Chen Y, Yin X, Xian G, Zhang Q. Research on Topic Mining and Evolution Trends of Functional Agriculture Based on the BERTopic Model. Agriculture. 2024; 14(10):1691. https://doi.org/10.3390/agriculture14101691

Chicago/Turabian Style

Lin, Qiao, Zhulin Xin, Shuang Peng, Ruixue Zhao, Yingli Nie, Youtao Chen, Xuebin Yin, Guojian Xian, and Qiang Zhang. 2024. "Research on Topic Mining and Evolution Trends of Functional Agriculture Based on the BERTopic Model" Agriculture 14, no. 10: 1691. https://doi.org/10.3390/agriculture14101691

APA Style

Lin, Q., Xin, Z., Peng, S., Zhao, R., Nie, Y., Chen, Y., Yin, X., Xian, G., & Zhang, Q. (2024). Research on Topic Mining and Evolution Trends of Functional Agriculture Based on the BERTopic Model. Agriculture, 14(10), 1691. https://doi.org/10.3390/agriculture14101691

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