Comparative Analysis of Naive Bayes Algorithms for Date Fruit Classification
Abstract
Date organic products are the most widely recognized natural product in the Center East and North Africa. There are a wide assortment of dates with various kinds, colors, shapes, tastes, and healthy benefits. Grouping, distinguishing, and perceiving dates would assume an essential part in the farming, business, food, and wellbeing areas. Deciding the range of natural products by taking a gander at their outer appearance might require mastery, which is tedious and requires incredible exertion. The aim of this study is to classify the types of date fruit, that are, Barhee, Deglet Nour, Sukkary, Rotab Mozafati, Ruthana, Safawi, and Sagai by using two different machine learning methods. This study presents a comparative analysis of two Naive Bayes algorithms, namely Naive Bayes and Naive Bayes Multinomial, for the classification of date fruits. The experimental results evaluate the performance of these algorithms in terms of accuracy, precision, and recall. The findings contribute to understanding the effectiveness of different Naive Bayes variants in the context of date fruit classification.
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Introduction
Dates are the natural product created from palm trees. It is a late spring natural product that is far reaching in the Middle Easterner world. The qualities of dates incorporate taste and surface, which contrast and must be anticipated in the event that they have been tasted previously. Many dates have various sorts, colors, shapes, tastes, and healthy benefits [1]. For ranchers to protect the right assortments, the shoots are shipped, appropriated, and planted once more. Curiously, in regards to palm trees, manors use seeds to develop palms, and another assortment seldom becomes like the mother.
Date natural product, which has numerous assortments all through the world, is utilized in the development of food, clinical, and restorative items. Well-qualified assessment is expected to recognize date natural product assortments because of various healthy benefit, different utilization times, various costs, and quality contrasts [2] . Every district of the realm is renowned for a couple of kinds of dates.
There might be interesting species that don't have business esteem and are not boundless among ranchers. This type might have a high dietary benefit and quality, and this assortment is excluded from the data set. There is no examination with some other organic product as well as how much creation of dates. With this block, there is practically no exploration in the characterization, recognizable proof, and acknowledgment of dates [9]. Improvement of this examination might twofold the amount of creation and the worth of deals and give a reasonable logical impression of the dietary benefit of dates, which should be followed during utilization. Building data sets for dates experimentally considers examination into improvement and coherence, and in this manner for a logical, financial advantage.
Conclusion
The findings of this study contribute to the understanding of different Naive Bayes variants and their performance in the classification of date fruits. Future research could explore the incorporation of additional features or consider other classification algorithms to further enhance the accuracy and overall performance of date fruit classification systems.
In conclusion, this study highlights the comparative analysis of Naive Bayes and Naive Bayes Multinomial algorithms for date fruit classification, providing insights into their performance based on accuracy, precision, and recall metrics. These findings can serve as a valuable reference for researchers and practitioners working in the field of fruit classification and related applications.