• Mahalakshmi.C
• Saravanan. S

Orange fruit diseases pose a significant challenge globally, severely affecting fruit quality, yield, and market value. This, in turn, impacts agricultural productivity and food supply chains. Early and accurate detection of diseases in orange fruits is essential for effective disease control and management. Recently, machine learning (ML) and deep learning (DL) techniques have shown great potential in automating and enhancing the detection of fruit diseases. This paper aims to provide a comprehensive review of existing research on the detection and classification of orange fruit diseases using ML and DL algorithms. It begins by emphasizing the importance of early disease identification and the limitations associated with traditional inspection methods. The study further examines the major challenges and requirements of automated fruit disease detection systems. Additionally, it reviews various research works that apply ML and DL techniques for identifying diseases in orange fruits. Evaluation methods, performance metrics, and commonly used datasets are also discussed to assess the effectiveness of these approaches. Furthermore, emerging trends and recent advancements—such as image augmentation, transfer learning, and ensemble learning techniques—are highlighted as promising directions in this field.

Orange fruit disease, Fruit disease detection, Machine Learning, Deep Learning, Computer Vision.

"Advancements in Automated Orange Fruit Disease Detection and Classification: A Review of Machine Learning and Deep Learning Approaches", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 3, page no.e421-e434, March-2026, Available :http://www.jetir.org/papers/JETIR2603448.pdf

"Advancements in Automated Orange Fruit Disease Detection and Classification: A Review of Machine Learning and Deep Learning Approaches", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 3, page no. ppe421-e434, March-2026, Available at : http://www.jetir.org/papers/JETIR2603448.pdf