On the Image-Based Detection of Tomato and Corn leaves Diseases : An in-depth comparative experiments

• URL: http://arxiv.org/abs/2312.08659v1
• Date: Thu, 14 Dec 2023 05:11:30 GMT
• Title: On the Image-Based Detection of Tomato and Corn leaves Diseases : An in-depth comparative experiments
• Authors: Affan Yasin, Rubia Fatima
• Abstract summary: The research introduces a novel plant disease detection model based on Convolutional Neural Networks (CNN) for plant image classification. The model classifies two distinct plant diseases into four categories, presenting a novel technique for plant disease identification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The research introduces a novel plant disease detection model based on Convolutional Neural Networks (CNN) for plant image classification, marking a significant contribution to image categorization. The innovative training approach enables a streamlined and efficient system implementation. The model classifies two distinct plant diseases into four categories, presenting a novel technique for plant disease identification. In Experiment 1, Inception-V3, Dense-Net-121, ResNet-101-V2, and Xception models were employed for CNN training. The newly created plant disease image dataset includes 1963 tomato plant images and 7316 corn plant images from the PlantVillage dataset. Of these, 1374 tomato images and 5121 corn images were used for training, while 589 tomato images and 2195 corn images were used for testing/validation. Results indicate that the Xception model outperforms the other three models, yielding val_accuracy values of 95.08% and 92.21% for the tomato and corn datasets, with corresponding val_loss values of 0.3108 and 0.4204, respectively. In Experiment 2, CNN with Batch Normalization achieved disease detection rates of approximately 99.89% in the training set and val_accuracy values exceeding 97.52%, accompanied by a val_loss of 0.103. Experiment 3 employed a CNN architecture as the base model, introducing additional layers in Model 2, skip connections in Model 3, and regularizations in Model 4. Detailed experiment results and model efficiency are outlined in the paper's sub-section 1.5. Experiment 4 involved combining all corn and tomato images, utilizing various models, including MobileNet (val_accuracy=86.73%), EfficientNetB0 (val_accuracy=93.973%), Xception (val_accuracy=74.91%), InceptionResNetV2 (val_accuracy=31.03%), and CNN (59.79%). Additionally, our proposed model achieved a val_accuracy of 84.42%.

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