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Abstract <p>The online monitoring of news related to pests and diseases is a key component of early warning systems, as it enables the localization, extraction, and processing of relevant documents to generate timely information about potential agricultural threats. The National Service for Agro-Alimentary Health, Safety and Quality (SENASICA), the agency responsible for protecting Mexico’s agricultural, aquacultural, and livestock resources, requires efficient tools to identify and analyze phytosanitary events that may pose a risk of spread within the national territory. This work presents FREM, an automated news processing system that integrates web scraping, natural language processing (NLP), and Transformer-based models to detect and analyze phytosanitary risk events. The system extracts key information from online news – including the title, date, country, pest, and affected crop – and presents it in a structured and interactive way. To enhance accuracy, FREM combines named entity recognition (NER) with Transformer-based models (RoBERTa and BERT), achieving 95% precision in pest detection, 90% accuracy in country identification, and 92% precision in crop recognition. A sentiment analysis module evaluates the tone of each article, supporting risk interpretation, while an automatic translation module (based on the facebook/nllb-200-distilled-600M model) extends coverage to English-language news sources. Finally, the extracted data are visualized through interactive dashboards that allow SENASICA analysts to explore statistics, assess event relevance, and prioritize alerts of potential pest spread in Mexico, strengthening national phytosanitary surveillance and supporting strategic decision-making.</p>

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Automating Phytosanitary Intelligence: The FREM System

  • 期刊论文

  • 发表日期：

    2026年6月23日

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  • Sagrario Hernandez-Mata,
  • Gilberto Lorenzo Martínez-Luna,
  • Manolo Muñiz-Merino,
  • Adolfo Guzmán-Arenas,
  • Morgan Noe Porcayo-López,
  • Ponciano Jorge Escamilla-Ambrosio

摘要

Abstract

The online monitoring of news related to pests and diseases is a key component of early warning systems, as it enables the localization, extraction, and processing of relevant documents to generate timely information about potential agricultural threats. The National Service for Agro-Alimentary Health, Safety and Quality (SENASICA), the agency responsible for protecting Mexico’s agricultural, aquacultural, and livestock resources, requires efficient tools to identify and analyze phytosanitary events that may pose a risk of spread within the national territory. This work presents FREM, an automated news processing system that integrates web scraping, natural language processing (NLP), and Transformer-based models to detect and analyze phytosanitary risk events. The system extracts key information from online news – including the title, date, country, pest, and affected crop – and presents it in a structured and interactive way. To enhance accuracy, FREM combines named entity recognition (NER) with Transformer-based models (RoBERTa and BERT), achieving 95% precision in pest detection, 90% accuracy in country identification, and 92% precision in crop recognition. A sentiment analysis module evaluates the tone of each article, supporting risk interpretation, while an automatic translation module (based on the facebook/nllb-200-distilled-600M model) extends coverage to English-language news sources. Finally, the extracted data are visualized through interactive dashboards that allow SENASICA analysts to explore statistics, assess event relevance, and prioritize alerts of potential pest spread in Mexico, strengthening national phytosanitary surveillance and supporting strategic decision-making.