A Review of Fruit Disease Detection Using Deep Learning Models: Trends, Challenges, and Future Direction

Abstract

Fruits are the important nutrition in human life. Different diseases occur in the Fruit quality that affect the economic growth. Disease detection is important for ensuring crop health, yield, and food security. Traditional methods rely on manual inspection, which is time- consuming and error-prone. Deep learning (DL) models are the powerful tool for identifying disease in various fruits. Convolutional Neural Networks (CNNs) are highly effective for detecting and classifying fruit diseases using image data, offering automated, accurate, and scalable solutions for agricultural diagnostics. Fruit disease dataset such as Kaggle for classification and roboflow dataset for identifying the disease in fruits. There are so many Challenges that include restricted data diversity, poor generalization, and lack of interpretability. Future directions for identifying fruit diseases using deep learning include explainable AI, multimodal data fusion, and real-time mobile deployment. This review aims to guide future research toward robust, scalable, and interpretable solutions.