An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification

2023/04/10 by Md. Hamjajul Ashmafee, Ashmafee, Md. Hamjajul, Tasnim Ahmed +9 · 3 citations
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Plant Disease Management Techniques #Plant Pathogens and Fungal Diseases #Smart Agriculture and AI

paper · pdf · doi:10.48550/arxiv.2304.06520

openalex publication_date 2023/04/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Correct identification and categorization of plant diseases are crucial for ensuring the safety of the global food supply and the overall financial success of stakeholders. In this regard, a wide range of solutions has been made available by introducing deep learning-based classification systems for different staple crops. Despite being one of the most important commercial crops in many parts of the globe, research proposing a smart solution for automatically classifying apple leaf diseases remains relatively unexplored. This study presents a technique for identifying apple leaf diseases based on transfer learning. The system extracts features using a pretrained EfficientNetV2S architecture and passes to a classifier block for effective prediction. The class imbalance issues are tackled by utilizing runtime data augmentation. The effect of various hyperparameters, such as input resolution, learning rate, number of epochs, etc., has been investigated carefully. The competence of the proposed pipeline has been evaluated on the apple leaf disease subset from the publicly available `PlantVillage' dataset, where it achieved an accuracy of 99.21%, outperforming the existing works.

Cited by

• DExNet: Combining Observations of Domain Adapted Critics for Leaf Disease Classification with Limited Data
• AQUA20: A Benchmark Dataset for Underwater Species Classification under Challenging Conditions
• MangoLeafViT: Leveraging Lightweight Vision Transformer with Runtime Augmentation for Efficient Mango Leaf Disease Classification

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