Summary of Analysis Of Convolutional Neural Network-based Image Classifications: a Multi-featured Application For Rice Leaf Disease Prediction and Recommendations For Farmers, by Biplov Paneru et al.
Analysis of Convolutional Neural Network-based Image Classifications: A Multi-Featured Application for Rice Leaf Disease Prediction and Recommendations for Farmers
by Biplov Paneru, Bishwash Paneru, Krishna Bikram Shah
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 The paper presents a novel method for improving rice disease classification using convolutional neural network (CNN) algorithms, aiming to modernize agricultural practices and guarantee sustainable crop management. The authors employ 8 different CNN architectures, including ResNet-50, InceptionV3, VGG16, MobileNetv2, DenseNet121, DenseNet169, VGG19, Nasnet, and EfficientNetB0, on the UCI dataset to achieve high accuracy rates. Notably, the study achieves 75% accuracy with ResNet-50, 90% accuracy with DenseNet121, and 95.83% accuracy with MobileNetV2. The authors also develop a Tkinter-based application that offers farmers a feature-rich interface for real-time disease prediction and personalized recommendations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses computer vision to help farmers grow healthy crops by recognizing diseases in rice plants. Scientists trained many different types of artificial intelligence models, or “algorithms,” to look at pictures of rice plants and predict what kind of disease they might have. Some algorithms were very good at making predictions, while others weren’t as accurate. The best algorithm was able to correctly identify the type of disease 95.83% of the time! The scientists also created a special tool that farmers can use on their phones or computers to get real-time help with diagnosing diseases in their rice plants. |
Keywords
» Artificial intelligence » Classification » Cnn » Neural network » Resnet
