Application of machine learning techniques in classification of soil types based on their nutrient profiles and phytopathological analysis - Pereira, Eron Ponce; Tanaka, Sergio Kenji Sawasaki; Coleto, Bruno Henrique; Tanaka, Sergio Akio (Revista Terra & Cultura: Cadernos de Ensino e Pesquisa; Vol 40 No 77 (2024): Publicação Contínua, 2024)
Precision agriculture is an area that is growing exponentially, as this sector is the largest contributor to Brazilian GDP. The problem to be resolved is the slowness in identifying patterns in soil analysis, requiring demand from different actors in the precision agriculture process. This work has a practical application using Artificial Intelligence with a focus on machine learning with computer vision and data analysis. A practical case study, in the area of agribusiness, is being implemented to solve problems in the classification of soil types, based on their nutrient profiles and the identification of phytopathological factors in plants. A workflow was developed to understand the soil analysis process. The initial results generated with the implementation of the project are promising. When using a soil planning database containing 2,200 lines, eight supervised learning models were generated, varying in confidence in results from 93% to 99%. Therefore, it is possible to identify the best crop to be planted with data from an existing soil analysis.