An Automatic Coffee Plant Diseases Identification Using Hybrid Approaches of Image Processing and Decision Tree
Coffee Leaf Rust (CLR), Coffee Berry Disease (CBD) and Coffee Wilt Disease (CWD) are the three main diseases that attack coffee plants. This paper presents the identification of these types diseases using hybrid approaches of image processing and decision tree. The images are taken from Southern Eth...
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2018-03-01.
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| Online Access: | Get fulltext |
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| LEADER | 02000 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs10137_8075 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Debasu Mengistu, Abrham |e author |
| 100 | 1 | 0 | |a Bahir Dar University |e contributor |
| 700 | 1 | 0 | |a Mengistu, Seffi Gebeyehu |e author |
| 700 | 1 | 0 | |a Alemayehu, Dagnachew Melesew |e author |
| 245 | 0 | 0 | |a An Automatic Coffee Plant Diseases Identification Using Hybrid Approaches of Image Processing and Decision Tree |
| 260 | |b Institute of Advanced Engineering and Science, |c 2018-03-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/10137 | ||
| 520 | |a Coffee Leaf Rust (CLR), Coffee Berry Disease (CBD) and Coffee Wilt Disease (CWD) are the three main diseases that attack coffee plants. This paper presents the identification of these types diseases using hybrid approaches of image processing and decision tree. The images are taken from Southern Ethiopia, Jimma and Zegie. In this paper backpropagation artificial neural network (BPNN) and decision tree had been used as techniques; a total of 9100 images were collected. From these, 70% are used for training and the remaining 30% are used for testing. In general, 94.5% accuracy achieved when decision tree and BPNN with tanh activation function are combined. | ||
| 540 | |a Copyright (c) 2017 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc-nd/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a BPNN; decision tree; CLR; CBD; CWD | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 9, No 3: March 2018; 806-811 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v9.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/10137/8075 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/10137/8075 |z Get fulltext |