Artificial neural network–based assessment and prediction of internal quality parameters of mango fruit cv. Timor

Abstract

Abstract Most existing techniques for assessing mango quality rely on biochemical analyses that destroy the fruit. This study evaluated and predicted the quality parameters of Timor mangoes using an artificial neural network (ANN) model. Data were collected from nine plantation sites in Gizan, Jazan Province, southwestern Saudi Arabia, over two growing seasons. The ANN model was configured with a 6-30-8 architecture and trained using 54 data patterns, while 18 patterns were used for testing. The input variables included peel weight, fruit length, fruit weight, fruit width, stone weight, and fruit firmness. The predicted output variables were ash content, pH, total soluble solids (TSS), titratable acidity (TA), vitamin C (VC), carotenoid content, total sugar content (TSC), and reducing sugar content (RSC) in mango juice, representing key internal quality attributes. The ANN model demonstrated satisfactory predictive performance on the testing dataset, with coefficients of determination (R2) of 0.9537, 0.9807, 0.9892, 0.9894, 0.9912, 0.9928, 0.9779, and 0.8436, respectively, for the output variables following the aforementioned sequence. These findings indicate that the proposed ANN-based approach is an effective, nondestructive tool for predicting internal mango quality attributes at harvest.