A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks
Abstract
:1. Introduction
Related Work
2. Materials and Methods
2.1. The Concept of CNN
2.2. Materials
2.3. Methodology
2.4. Data Collection and Testing of the CNN
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Type of Maize Disease | Total Percentage of Training Images | Total Percentage of Testing Images | CNN Classifier Accuracy |
---|---|---|---|
Northern Corn Leaf Blight. | 70% | 30% | 99.9% |
Gray Leaf Spot. | 70% | 30% | 91% |
Common Rust. | 70% | 30% | 87% |
Healthy | 70% | 30% | 93.5% |
Input Image | Convolution Result | Histogram Result |
---|---|---|
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Sibiya, M.; Sumbwanyambe, M. A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks. AgriEngineering 2019, 1, 119-131. https://doi.org/10.3390/agriengineering1010009
Sibiya M, Sumbwanyambe M. A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks. AgriEngineering. 2019; 1(1):119-131. https://doi.org/10.3390/agriengineering1010009
Chicago/Turabian StyleSibiya, Malusi, and Mbuyu Sumbwanyambe. 2019. "A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks" AgriEngineering 1, no. 1: 119-131. https://doi.org/10.3390/agriengineering1010009