INTELLIGENT SYSTEM FOR IDENTIFYING AVOCADO RIPE USING EXTRACTION FEATURES AND K-NEAREST NEIGHBOR METHOD

Maryam Hasan
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Abstract

So far, farmers in harvesting avocados often experience obstacles, namely farmers are still not precise in determining ripe avocados. The ripeness of avocado when harvested is one of the most important factors in maintaining the quality of the avocado. This is due to fatigue and takes a long time so it is not appropriate to determine which avocados are ripe and which are not. Therefore, a system is needed to find out avocados. This study formulates problems in the form of how to identify avocado identification with the KNN method and how to find out how much accuracy the method obtained. While the objectives achieved in this study can identify avocados based on the KNN method and can measure the accuracy of the method with avocado image data. The results obtained in this study were successful in identifying and using the 80% measurement confusion matrix.
利用提取特征和 K 近邻法识别鳄梨成熟度的智能系统
迄今为止,农民在收获牛油果时经常遇到障碍,即农民对成熟牛油果的判断仍然不准确。牛油果收获时的成熟度是保持牛油果品质的最重要因素之一。由于疲劳,这需要很长的时间,因此不宜确定哪些牛油果成熟,哪些未成熟。因此,需要一个系统来找出鳄梨。本研究提出的问题是如何用 KNN 方法识别鳄梨,以及如何找出该方法的准确度。本研究实现的目标可以基于 KNN 方法识别牛油果,并利用牛油果图像数据衡量该方法的准确性。本研究获得的结果成功地识别并使用了 80% 的测量混淆矩阵。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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