Multi-Spectral Imaging for Fruits and Vegetables

被引:5
|
作者
Gaikwad, Shilpa [1 ]
Tidke, Sonali [2 ]
机构
[1] Symbiosis Int Univ, Symbiosis Inst Technol, Elect & Telecommun Engn, Pune 412115, Maharashtra, India
[2] Symbiosis Int Univ, Symbiosis Inst Technol, Comp Sci & Engn, Pune 412115, Maharashtra, India
关键词
Multi-spectral imaging; fruit grading; vegetable classification; fruit quality; disease detection; fruit maturity; INSPECTION; VISION;
D O I
10.14569/IJACSA.2022.0130287
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In the field of agriculture, fruit grading and vegetable classification is an important and challenging task. The current fruit and vegetable classifications are done manually, which results in inconsistent performance. There is an influence of external surroundings on this manual classification. Sometimes getting an expert fruit or vegetable grader, is challenging and the performance of that person may become stagnant over some time. With the recent development in computer technology and multispectral camera system, it is possible to achieve an efficient fruit grading or vegetable classification system. In this manuscript, we summarize different automated fruit grading as well as vegetable classification systems, which are based on multi-spectral imaging. We have focused our analysis on four major areas such as varietal identification, fruit quality assessment, classification, and disease detection. From our analysis, we have found that the Partial Least Square Discriminant Analysis (PLS-DA) was most effective for identifying varieties of tomato seeds. For analyzing the quality of pomegranate fruits, the multiple linear regression model was the was the recommended method for classifying medicinal plant leaves. A Linear Discriminant Analysis (LDA) based classifier could accurately detect diseases in fruits and vegetables.
引用
收藏
页码:743 / 760
页数:18
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