Enhancing Efficiency in Sugarcane Industry Forecasting through Integrated Data Analysis and Artificial Intelligence Techniques

• Grant number: 24/04718-8
• Support Opportunities: Regular Research Grants
• Start date: October 01, 2024
• End date: September 30, 2026
• Field of knowledge: Interdisciplinary Subjects
• Principal Investigator: Marilaine Colnago
• Grantee: Marilaine Colnago
• Host Institution: Instituto de Biociências, Letras e Ciências Exatas (IBILCE). Universidade Estadual Paulista (UNESP). Campus de São José do Rio Preto. São José do Rio Preto , SP, Brazil
• Associated researchers: José Nuno Moura Marques Fidalgo ; Marluce da Cruz Scarabello ; Wallace Correa de Oliveira Casaca

Abstract

This project aims to deepen the understanding of the complex aspects surrounding sugarcane production in the state of Sao Paulo, focusing on the use of Exploratory Data Analysis (EDA) techniques and Artificial Intelligence (AI), including machine learning predictive models. The research is motivated by the strategic importance of sugarcane cultivation in the national economy, especially in Sao Paulo, which accounts for approximately 55% of the total cultivated area in the country. Through EDA methods, we intend to investigate the impact of various variables such as climate data, planting, and harvesting seasons, among others, on the production and prices of sugarcane and its by-products, including ethanol, sugar, and biomass. Subsequently, AI predictive models will be developed and validated, covering classical techniques such as random forests to deep neural networks, focusing on predicting the production and price of these commodities. The integration of different data collections that are currently decentralized, such as crop histories, market prices, and economic indices, will enable the creation of a robust and unified repository for decision-making and problem analysis. In addition to developing data-driven technological solutions in the sugarcane industry, we aim to build a new computational infrastructure in this context, enabling a more efficient and holistic approach to estimating the historical series of sugarcane production in the state. (AU)