http://seer.tupa.unesp.br/index.php/BIOENG/issue/feedRevista Brasileira de Engenharia de Biossistemas2024-03-14T22:33:27+00:00Prof. Dr. Celso Antonio Goulartcelso.goulart@unesp.brOpen Journal Systems<table style="height: 190px;" width="800"> <tbody> <tr> <td width="141"> <h2 class="title"> </h2> <h2 class="title"> <img src="https://seer.tupa.unesp.br/public/site/images/dfpereira/mceclip0.png" /></h2> </td> <td width="425">The <em>Brazilian Journal of Biosystems Engineering</em> (BIOENG) publishes original articles that present theoretical, experimental, computational advances and innovations in the areas of agricultural and environmental systems, bringing applications for the sustainable development of agricultural and animal biosystem productions. BIOENG journal publishes interdisciplinary scientific articles and prioritizes issues related to Sustainable Development Goals (SDGs) of the United Nations (UN).</td> </tr> </tbody> </table>http://seer.tupa.unesp.br/index.php/BIOENG/article/view/1211Artificial intelligence applied to estimate soybean yield2024-03-14T22:33:27+00:00Wesley Prado Leão dos Santoswesley.prado@unesp.brMariana Bonini Silvabonini.silva@unesp.brAlfredo Bonini Netoalfredo.bonini@unesp.brCarolina dos Santos Batista Boninicarolina.bonini@unesp.brAdônis Moreiraadonis.moreira@embrapa.br<p>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<sup>-5</sup>, 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<sup>-3</sup>. These results indicate that our approach is adequate for optimizing soybean yield estimates in the area studied.</p>2024-03-14T00:00:00+00:00Copyright (c) 2024 Revista Brasileira de Engenharia de Biossistemas