Peach yield production prediction models are little known worldwide. This gap can be filled by combining machine learning techniques and well-documented databases. The aims of this study are: (i) to assess the effect of different prediction variable inputs applied to peach yield prediction models adopted to peach trees grown in orchards under different subtropical climate; (ii) to test the prediction accuracy performance of models calibrated through different machine learning methods; and (iii) to quantify the relevance of peach trees’ yield predictor variables. A database (soil and leaf nutrient content, climatic and plant variables) with information from 208 peach trees (Prunus persica) in production, belonging to the cultivars ‘Maciel’ and ‘Chimarrita’ grown in Southern Brazil, was used. The models were developed by using three machine learning methods: Radom Forest, Multiple Linear Regression, and Support Vector Machine. We demonstrate that the calibration of the models was affected by machine learning method as well as by different predictor variable inputs. The model Random Forest showed the greatest potential to predict peach yield. The variable presenting the greatest relevance to explain peach yield variations was ‘hours of chilling’, which was followed by K and N content in leaves and mean temperature, which recorded relevance of >55%.
Moura-Bueno et al. (Thu,) studied this question.