High-performance prediction of macauba fruit biomass for agricultural and industrial purposes using artificial neural networks

Abstract

Biomass estimation plays of crucial role in agriculture and agro-based industries. The macauba, Acrocomia aculeata (Jacq.) Lood., ex Mart., is a palm species that has been a focal point for research and development of an alternative biomass-bioenergy crop for the tropics. The macauba fruit components (exocarp, mesocarp, endocarp and seed/kernel) present different constitutional characteristics and their biomass determination, by traditional methods, is labor-consuming. Therefore, the validation of procedures that can streamline this process is relevant, since it can reduce costs and time for both breeding programs and industries. This study tested the efficacy of Artificial Neural Networks (ANN) on biomass prediction of the macauba fruit components by comparing it to the multiple linear regression method. The data used came from fruits collected in 18 localities, distributed throughout the state of Minas Gerais, Brazil. According to their provenance, the matrices were clustered into two groups with the k-means method for posterior ANN cross-validation. Each group was interchangeably used for both training and validation purposes. The ANN was more efficient than multivariate linear model in the predictions of dry weight of the fruit́s four components and oil content of the mesocarp and seed. As for variables related to dry weight, ANN reached 98% predictive accuracy (i.e., 98% accuracy of the value predicted by the network), and for variables related to oil contents, accuracy was around 90%. Additionally, non-invasive measurements of the fruit (i.e., low-cost and low-time measurement variables) were adequate enough to predict most of the variables of interest. These results show the ANN's prediction potential, saving time and efforts for the consolidation of macauba as a crop.