Recognizing the Types of Beans Using Artificial Intelligence

Authors

  • Nur Nafi'iyah Universitas Islam Lamongan
  • Endang Setyati Institute of Sciences and Technology Integrated Surabaya
  • Yosi Kristian Institute of Sciences and Technology Integrated Surabaya
  • Retno Wardhani Universitas Islam Lamongan

DOI:

https://doi.org/10.26594/register.v9i2.3054

Keywords:

optimal feature, selection feature, correlation, bean leaves, backpropagation

Abstract

Many studies have previously addressed the recognition of plant leaf types. The process of identifying these leaf types involves a crucial feature extraction stage. Image feature extraction is pivotal for distinguishing the types of objects, thus demanding optimal feature analysis for accurate leaf type determination. Prior research, which employed the CNN method, faced challenges in effectively distinguishing between long bean and green bean leaves when identifying bean leaves. Therefore, there is a need to conduct optimal feature analysis to correctly classify bean leaves. In our research, we analyzed 69 features and explored their correlations within various image types, including RGB, L*a*b, HSV, grayscale, and binary images. The primary objective of this study is to pinpoint the features most strongly correlated with the recognition of bean leaf types, specifically green bean, soybeans, long beans, and peanuts. Our dataset, sourced from farmers' fields and verified by experienced senior farmers, consists of 456 images. The most highly correlated feature within the bean leaf image category is STD b in the L*a*b image. Furthermore, the most effective method for leaf type recognition is Neural Network Backpropagation, achieving an accuracy rate of 82.28% when applied to HSV images.

Author Biography

Nur Nafi'iyah, Universitas Islam Lamongan

Department of Informatics

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Published

2023-11-21

How to Cite

[1]
N. Nafi’iyah, E. Setyati, Y. Kristian, and R. Wardhani, “Recognizing the Types of Beans Using Artificial Intelligence”, regist. j. ilm. teknol. sist. inf., vol. 9, no. 2, pp. 134–143, Nov. 2023.

Issue

Vol. 9 No. 2 (2023): July

Section

Article

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Copyright (c) 2023 Nur Nafi'iyah, Endang Setyati, Yosi Kristian, Retno Wardhani

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