Non-destructive Grading of Pattavia Pineapple using Texture Analysis

被引:0
|
作者
Dittakan, Kwankamon [1 ]
Theera-Ampornpunt, Nawanol [1 ]
Boodliam, Pattaporn [1 ]
机构
[1] Prince Songkla Univ, Coll Comp, Phuket, Thailand
关键词
Pineapple Mining; Pineapple Grading; Image Mining; Image Analysis; Agriculture Informatics; LOCAL BINARY PATTERNS;
D O I
暂无
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Pineapple is a tropical fruit with many cultivars in Thailand such as Pattavia, Intarachit, Phuket, Nang-Lea and Phu-Lea. Pattavia pineapple is a popular cultivar in Thailand because of its good taste which can be planted in many areas in Thailand. The fresh Pattavia pineapple can be classified into two grades: (i) K eaw 1 and (ii) K eaw 2 depending on their sweetness and juiciness. In this paper, an automated mechanism to classify the grade of Pattavia pineapple by non-destructive process is proposed. The fundamental idea is to construct the classification model to which it is able to distinguish between K eaw 1 or K eaw 2 images. The texture analysis mechanism is suggested to analysis the pineapple skin. More specifically, Local Binary Pattern (LBP) texture descriptor is suggested to obtain key information of pineapple texture image. The best model produced the excellent results with recorded AUC value of 0.979.
引用
收藏
页码:144 / 149
页数:6
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