Abstract: In the field of agriculture, fruit grading and veg-etable 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 multi-spectral 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 most optimal method. Multi-Layer Perceptron (MLP) classifier was the recommended method for classifying medicinal plant leaves. A Linear Discriminant Analysis (LDA) based classifier could accurately detect diseases in fruits and vegetables.

Shilpa Gaikwad and Sonali Tidke, “Multi-Spectral Imaging for Fruits and Vegetables” International Journal of Advanced Computer Science and Applications(IJACSA), 13(2), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130287

@article{Gaikwad2022,
title = {Multi-Spectral Imaging for Fruits and Vegetables},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130287},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130287},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {2},
author = {Shilpa Gaikwad and Sonali Tidke}
}