The PV-ALE Dataset: Enhancing Apple Leaf Disease Classification Through Transfer Learning with Convolutional Neural Networks

TL;DR

This paper introduces the PV-ALE dataset, an extended and more challenging apple leaf disease dataset, and evaluates transfer learning with CNNs to improve disease classification accuracy under diverse conditions.

Contribution

The study extends the PlantVillage dataset with new apple leaf disease classes, creating a more diverse benchmark for evaluating CNN models and transfer learning techniques.

Findings

Test F1 score of 99.63% on original dataset

Test F1 score of 97.87% on extended dataset

Existing models struggle with new, more complex data

Abstract

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.