Authors - Deepak Mane, Deepak R. More, Arya Kale, Ravina Jagtap , Soumya Dubewar , Diya Nair Abstract - Timely detection of crop diseases is essential to ensuring high agricultural produc- tivity; thus, early and accurate detection has always been a priority for the farmers. So we pro- posed a deep learning based framework that classifies the condition of basil leaves in three cat- egories - wilting, infection by mildew and healthy - through an EfficientNet-B0 convolutional neural network fine-tuned using transfer learning. We leverage a curated dataset of 1,442 plant images available at the Roboflow platform, splitting the dataset into 70% training, 20% valida- tion and 10% testing. Transfer learning was used where we started EfficientNet-B0 with weights learned on large scale ImageNet pretraining. Training was done in two stages: first the whole model was trained with the backbone frozen and only the newly added classification head being trained, followed by unfreeze the last 100 layers and perform fine-tuning to the domain. Leaf orientation and illumination variability were treated by a group of data augmentation methods including random horizontal flipping, rotational transforms, zoom perturbations, and contrast adjustments. The proposed system achieved a remarkable result with high generalization of 96.6% training accuracy and 97.8% test accuracy. The detailed analysis of the confusion matrix and the ROC-AUC curves corroborate faithful multi-class discrimination. A Streamlit-based web interface was also developed to facilitate live inference, farmers and agronomists are now able to make immediate predictions of the disease with confidence estimates. The results showed that the well optimized EfficientNet-B0 model can be a feasible and scalable solution for automated monitoring of crop diseases in the context of smart agriculture.0
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