The PV-ALE Dataset: Enhancing Apple Leaf Disease Classification Through Transfer Learning with Convolutional Neural Networks
This work addresses crop disease diagnosis for global food security by providing a more challenging benchmark, though it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of apple leaf disease classification by extending the PlantVillage dataset with additional classes, achieving test F1 scores of 99.63% on the original and 97.87% on the extended dataset.
As the global food security landscape continues to evolve, the need for accurate and reliable crop disease diagnosis has never been more pressing. To address global food security concerns, we extend the widely used PlantVillage dataset with additional apple leaf disease classes, enhancing diversity and complexity. Experimental evaluations on both original and extended datasets reveal that existing models struggle with the new additions, highlighting the need for more robust and generalizable computer vision models. Test F1 scores of 99.63% and 97.87% were obtained on the original and extended datasets, respectively. Our study provides a more challenging and diverse benchmark, paving the way for the development of accurate and reliable models for identifying apple leaf diseases under varying imaging conditions. The expanded dataset is available at https://www.kaggle.com/datasets/akinyemijoseph/apple-leaf-disease-dataset-6-classes-v2 enabling future research to build upon our findings.