JSON Corpus of Artificial Intelligence Studies in the Sugarcane Sector: A Systematic Mapping Dataset

Description

This dataset contains a structured JSON corpus of 62 primary studies on the application of Artificial Intelligence (AI) in the sugarcane sector, identified through a systematic mapping study. The selected studies cover three main operational domains: Harvesting/Agriculture, Transportation/Logistics, and Industry/Process. Each article was manually analyzed and transcribed into a machine-readable JSON format. The extracted information preserves the original organization of each study, including sections such as abstract, introduction, methods, results, and conclusions, together with metadata describing publication year, country, research topics, object of study, AI task, learning models, keywords, and publication source. Information reported in tables was also transcribed into the corresponding JSON records, while figures remain accessible through the original publication links. The corpus adopts a two-level architecture composed of a central index.json file and a collection of pack.json files. The index.json file stores metadata and mapping attributes for each study, while the pack.json files contain the complete structured content of the articles. This organization enables efficient indexing, scalable retrieval, and integration with Large Language Models (LLMs) and AI agents. The dataset was developed to support systematic literature reviews, evidence retrieval, AI-assisted question answering, and the development of intelligent systems for the sugar-energy sector. It can also be reused in applications involving Retrieval-Augmented Generation (RAG), knowledge management, semantic search, and domain-specific AI assistants. Contents: index.json: metadata and indexing information for all selected studies. pack01.json–pack07.json: structured representations of the selected articles grouped by research domain. Documentation describing the JSON schema and corpus organization. This dataset accompanies the systematic mapping study entitled "Artificial Intelligence Applications in the Sugarcane Sector: A Systematic Mapping Study" and is intended to promote reproducibility, transparency, and reuse of the mapped evidence by the research community.

Institutions

• Universidade do Estado de Minas Gerais

  Minas Gerais, Belo Horizonte

Categories

Funders

• Fundação de Amparo à Pesquisa do Estado de Minas Gerais

  Federal Government of Brazil

  Belo Horizonte

Licence