Summary of Lemon and Orange Disease Classification Using Cnn-extracted Features and Machine Learning Classifier, by Khandoker Nosiba Arifin et al.
Lemon and Orange Disease Classification using CNN-Extracted Features and Machine Learning Classifier
by Khandoker Nosiba Arifin, Sayma Akter Rupa, Md Musfique Anwar, Israt Jahan
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a disease classification approach to improve citrus farming, focusing on lemons and oranges. The goal is to enable early disease detection, reduce yield losses, and optimize resource allocation. To achieve this, the study uses deep learning architectures like VGG16, VGG19, and ResNet50, as well as basic machine learning algorithms such as Random Forest, Naive Bayes, K-Nearest Neighbors (KNN), and Logistic Regression. The model extracts base features from ResNet50 and classifies diseases using Logistic Regression, outperforming other classifiers like Softmax. Experimental results show that the proposed model achieves high accuracy in classifying lemon and orange fruit diseases, with 95.0% for lemons and 99.69% for oranges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Citrus fruits like lemons and oranges are important crops worldwide. But these fruits often get sick, which can lead to big losses. To help farmers grow healthier citrus fruits, scientists developed a new way to detect diseases early on. This method uses special computer models that look at pictures of the fruit. The researchers tested their model with two types of citrus fruits and found that it was very accurate – 95% for lemons and 99.7% for oranges! This means farmers can take action earlier to prevent losses and grow more healthy citrus fruits. |
Keywords
» Artificial intelligence » Classification » Deep learning » Logistic regression » Machine learning » Naive bayes » Random forest » Softmax
