Authors - Ashutosh Patil, Gayatri Bhangle, Sejal Kadam, Poonam Vetal, Prajakta Shinde Abstract - Advances in deep learning have enabled effective applications in agriculture, including fruit disease detection. Accurate identification of diseases in fruits such as Annonaceae and Rutaceae families is crucial for yield and quality improvements. Many studies employ deep learning models like CNN, ResNet, VGG, and DenseNet for disease detection across fruits such as apple, orange, guava, and grapes. This article reviews recent research on deep learning for fruit disease detection and classification, focusing on model performance, data utilization, and visualization techniques. We analyze existing studies to identify optimal strategies for fruit species and other underrepresented crops, outlining challenges and areas for future research on various types of fruit species and their family.
