ABSTRACT
Plant disease detection is a critical phenomenon, which may enable farmers to reduce losses by increasing productivity. The ability of Convolutional Neural Networks (CNNs) is still limited when dealing with small datasets, which impacts their broader application. A model is prone to overfitting when dealing with a few samples. This limits generalization and reduces its performance since the model may be memorizing data instead of learning general patterns. We used the regularization technique known as early stopping to avoid overfitting and obtain a better generalization. Early stopping ensures the model to generalize and perform well on new data. The EfficientNetB0 architecture — one of the highly advanced CNN architectures — to achieve greater accuracy with fewer parameters. Coupled with the early stopping technique integrated into our approach, to achieve improved generalization for small and diverse datasets.
