基于KNearest近邻的数字百科全书字符串Ubi变体

Bahtiar Adi Prasetya, Zilvanhisna Emka Fitri, Abd. Madjid, Arizal Mujibtamala Nanda Imron
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引用次数: 0

摘要

红薯是碳水化合物的来源,碳水化合物是加速食物多样化的替代食品。这是因为红薯的生产力很高,所以种植起来非常有利可图。红薯有很多品种,其中一个差异是根据叶片形状观察到的,叶片形状有四种,即心形、浅裂、三角形和几乎分裂。经常出现的问题是,许多品种有相似之处,导致难以区分红薯品种,尤其是对新手农民来说。为了克服这个问题,研究人员使用计算机视觉创建了一个基于叶片形状的红薯品种数字百科全书。使用的参数包括面积、周长、公制、长度、直径、ASM、IDM、熵、对比度和0°、45°、90°和135°角的相关性。所使用的数据量是256个训练数据和40个测试数据。K-最近邻方法能够以95%的准确度对数字百科全书的红薯叶图像进行分类,K值为23和K值为25。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ensiklopedia Digital Varietas Ubi Jalar Berdasarkan Klasifikasi Citra Daun Menggunakan KNearest Neighbor
Sweet potato is a source of carbohydrates which is an alternative food in order to accelerate food diversification. This is due to the high productivity of sweet potato so it is very profitable to cultivate. Sweet potato has many varieties, one of the differences is observed based on leaf shape which has four kinds of leaf shape, namely cordate, lobed, triangular and almost divided. The problem that often occurs is that many varieties have similarities, causing difficulties in distinguishing sweet potato varieties, especially for novice farmers. To overcome this problem, the researchers created a digital encyclopedia of sweet potato varieties based on leaf shape using computer vision. The parameters used are area, perimeter, metric, length, diameter, ASM, IDM, entropy, contrast and correlation at angles of 0°, 45°, 90° and 135°. The amount of data used is 256 training data and 40 testing data. The K-Nearest Neighbor method is able to classify sweet potato leaf images for digital encyclopedias with an accuracy of 95% with variations in the values of K = 23 and K = 25.
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