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The research introduces a novel plant disease detection model based onConvolutional Neural Networks (CNN) for plant image classification, marking asignificant contribution to image categorization. The innovative trainingapproach enables a streamlined and efficient system implementation. The modelclassifies two distinct plant diseases into four categories, presenting a noveltechnique for plant disease identification. In Experiment 1, Inception-V3,Dense-Net-121, ResNet-101-V2, and Xception models were employed for CNNtraining. The newly created plant disease image dataset includes 1963 tomatoplant images and 7316 corn plant images from the PlantVillage dataset. Ofthese, 1374 tomato images and 5121 corn images were used for training, while589 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 corndatasets, with corresponding val_loss values of 0.3108 and 0.4204,respectively. In Experiment 2, CNN with Batch Normalization achieved diseasedetection rates of approximately 99.89% in the training set and val_accuracyvalues exceeding 97.52%, accompanied by a val_loss of 0.103. Experiment 3employed a CNN architecture as the base model, introducing additional layers inModel 2, skip connections in Model 3, and regularizations in Model 4. Detailedexperiment results and model efficiency are outlined in the paper's sub-section1.5. Experiment 4 involved combining all corn and tomato images, utilizingvarious 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 modelachieved a val_accuracy of 84.42%.

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