Artificial neural network as an alternative for peach fruit mass prediction by non-destructive method

Abstract

The mass and diameter traits of peach fruits are important parameters to define fruit quality, harvest planning, and management of peach production through the estimation of fresh mass. However, currently, fruit mass estimation methods are based on conventional destructive analyzes which, as they are invasive measurements, can take considerable time, in addition to being costly. Thus, this work aimed to use Artificial Neural Networks in the prediction of the fresh mass of peach fruits as an alternative to the destructive method. Fruits of the peach cultivars, 'Tropic Beauty' (G1), 'BRS Kampai' (G2), and 'BRS Rubimel' (G3) were used in the 2017 and 2018 harvests, and the cultivars (G1) and (G2) were used in the 2019 harvest. Fruits were individually measured for fresh mass (FM) (dependent variable), and suture diameter (SD), equatorial diameter (ED), fruit height (FH) (independent variables). Multiple linear regression analysis (R-MLM) and multilayer perceptron artificial neural network (ANN-MLP) were performed, involving the set of dependent variables SD, ED, and FH, all expressed as a function of the independent variable FM. In all scenarios, we observed that ANN_MLP was more effective than R-MLM in predicting FM. The best ANN-MLP was used to predict the mean peach fruit yield of the genotypes and also for their combinations in the 2018 and 2019 harvests. The presence of high values of R2 in the validation sample indicates that the trained network is efficient and has generalization power to predict FM in all situations evaluated. The use of neural networks to predict the fresh mass (FM) of the peach fruit is an alternative to conventional destructive analyzes and the chance to use the same neural network topology in consecutive harvests will allow the collection of data directly in the field, reducing the analysis time and costs.