A data mining approach for prediction of quality attributes in Palmer mango from images

The monitoring of quality attributes such as, total soluble solids (TSS), mass, acidity and firmness are essential for a better postharvest conservation of mango. This work proposes a non destructive approach for prediction of those quality attributes using digital images. The proposed approach is composed by three stages: 1) specification of the sampling parameters of mango, 2) identification of digital images pre-processing techniques and 3) utilization of the Random Forest technique as estimator of the quality attributes. In order to validate the proposed approach, a study comparing its performance with models found in literature was carried out. The study used two metrics of performance evaluation: the correlation coefficient (R) and the root mean square error (RMSE). In order to compare the differences of performance between the proposed approach and approaches found in literature, a paired t-student's hypothesis test was carried out. Results show that the proposed approach has a superior performance regarding the existing ones, with confidence level of 95%.