CLASSIFICATION OF PLANT LEAF DISEASE USING ENSEMBLE DEEP TRANSFER LEARNING | ||
| International Journal of Intelligent Computing and Information Sciences | ||
| Volume 24, Issue 1, March 2024, Pages 69-88 PDF (1.35 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijicis.2024.278335.1331 | ||
| Authors | ||
| Andrew Nader* 1; Mohamed helmy Khafagy2; Shereen Ahmed Hussien3 | ||
| 1Computer Science Department, Faculty of Information Systems and Computer Science, October 6th University, Cairo, Egypt | ||
| 2Computer Science Department, Faculty of Computers and Information, Fayoum University, Fayoum , Egypt | ||
| 3Computer Science, Faculty of Computer & Information, Fayoum University | ||
| Abstract | ||
| Crops can be affected by various types of pathogens that cause diseases, leading to significant damage and negative impacts on food production and quality, causing farmers financial losses. Therefore, it is essential to detect and prevent plant diseases at their initial stages promptly. Unfortunately, the process of detecting and controlling plant diseases can be challenging for farmers. However, deep learning techniques can potentially make a significant contribution by accurately classifying plant diseases at their earliest stages. To overcome the limitations of previous research, this work proposes a new method for diagnosing and classifying plant leaf diseases. The proposed approach enhances the classification of popular crops such as tomato, potato, and pepper, using transfer ensemble learning with MobilenetV3 small and Resnet50. The model has only 37 million training parameters, which saves time, and computational power and reduces overfitting while achieving an accuracy of 99.50%. The model's ability to address false negatives and false positives ensures reliable and accurate plant disease classification at an early stage. The proposed method can work with any image dataset and achieve high performance, and it has been validated on the benchmark open dataset Plantvillage, demonstrating excellent performance for the proposed diagnosis approach. | ||
| Keywords | ||
| Convolutional Neural Network (CNN); Ensemble learning; Transfer learning; Deep Learning; Plant leaf disease classification | ||
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