Summary of Sugarcanenet: An Optimized Ensemble Of Lasso-regularized Pre-trained Models For Accurate Disease Classification, by Md. Simul Hasan Talukder et al.
SugarcaneNet: An Optimized Ensemble of LASSO-Regularized Pre-trained Models for Accurate Disease Classification
by Md. Simul Hasan Talukder, Sharmin Akter, Abdullah Hafez Nur, Mohammad Aljaidi, Rejwan Bin Sulaiman, Ali Fayez Alkoradees
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 proposed sugarcaneNet2024 model outperforms previous methods in automatically detecting sugarcane disease through leaf image processing. This deep learning-based approach combines an optimized weighted average ensemble of seven pre-trained models, including InceptionV3, DenseNet201, and ResNet152V2, with additional dense layers, dropout layers, and batch normalizations to improve performance. Comparative studies show that the ensemble technique outperforms individual models, achieving high accuracy, precision, recall, and F1 score. The optimized sugarcaneNet2024 model achieves an impressive 99.67% accuracy in detecting sugarcane diseases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new computer program can quickly and accurately detect diseases in sugarcane plants by looking at pictures of their leaves. This is important because diseases can greatly reduce the amount and quality of sugar produced from sugarcane. The program, called sugarcaneNet2024, uses a combination of different artificial intelligence models to analyze leaf images and identify signs of disease. The program was tested and found to be very accurate, with an accuracy rate of 99.67%. This technology could help farmers and researchers better manage diseases in sugarcane plants and improve the overall health of these crops. |
Keywords
* Artificial intelligence * Deep learning * Dropout * F1 score * Precision * Recall
