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

被引:0
作者
Reis, Daniele Silva [1 ]
de Oliveira Neto, Rosalvo Ferreira [1 ]
de Souza Costa, Josenara Daiane [3 ]
Figueiredo Neto, Acacio [1 ]
Costa, Marylia de Sousa [2 ]
机构
[1] Fed Univ Sao Francisco Valley, Petrolina, PE, Brazil
[2] Univ Fed Campina Grande, Campina Grande, Paraiba, Brazil
[3] Fed Inst Piaui, Teresina, PI, Brazil
来源
REVISTA BRASILEIRA DE COMPUTACAO APLICADA | 2020年 / 12卷 / 02期
关键词
Image processing; Non-destructive methods; Random forest; Regression; SYSTEM; FRUIT; CLASSIFICATION; COLOR;
D O I
10.5335/rbca.v12i2.10866
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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%.
引用
收藏
页码:54 / 66
页数:13
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