Artificial intelligence applied to estimate soybean yield


  • Wesley Prado Leão dos Santos São Paulo State University (UNESP), School of Sciences and Engineering, Tupã, São Paulo State, Brazil
  • Mariana Bonini Silva São Paulo State University (UNESP), College of Agricultural and Technological Sciences, Dracena, São Paulo State, Brazil
  • Alfredo Bonini Neto São Paulo State University (UNESP), School of Sciences and Engineering, Tupã, São Paulo State, Brazil
  • Carolina dos Santos Batista Bonini São Paulo State University (UNESP), College of Agricultural and Technological Sciences, Dracena, São Paulo State, Brazil
  • Adônis Moreira Department of Soil Science, Embrapa Soja, Londrina, Paraná State, Brazil



Artificial Neural Network, Forecast, Intelligent systems, Soy, Mathematical modelling


The application of mathematical models using biotic and abiotic factors for the efficient use of fertilizers to obtain maximum economic productivity can be an important tool to minimize the cost of soybean (Glycine max (L.) Merr.) grain yield. In this sense, using Artificial Neural Networks (ANN) is an important tool in studies involving optimization. This study aimed to estimate soybean yield in Luiziana, Paraná state, Brazil, by considering two growing seasons and an Artificial Neural Network (ANN) as a function of the morphological and nutritional parameters of the plants. Results reveal a well-trained network, with a margin of error of approximately 10-5, thus acting as a tool to estimate soybean data. For the phases, model validation and network test, i.e., data that were not part of the training (validation), the errors averaged 10-3. These results indicate that our approach is adequate for optimizing soybean yield estimates in the area studied.


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How to Cite

dos Santos, W. P. L., Silva, M. B., Bonini Neto, A., Bonini, C. dos S. B., & Moreira, A. (2024). Artificial intelligence applied to estimate soybean yield. Revista Brasileira De Engenharia De Biossistemas, 18.



IX Biosystems Engineering Week