• Hemanth Chandra N
• Thriveni J

Agriculture serves as one of the pillars of the world economy. Hence the responsibility to safeguard the crops in agriculture is important and should be taken seriously. Early identification of the diseases in crops is the first step in that direction. This paper focuses on detection and classification of apple leaf diseases using the YOLOv8 model. YOLOv8 augmented along with the background removal processing step gives a model which can predict the diseases on apple leaves with high accuracy. This model is trained on the dataset Plant Village which is available on the public domain. Diseases covered for apple leaf include Scab, Black rot and Cedar rust. The motive of this proposed model is to find a fast and efficient way to detect diseases in apple leaves and thus take appropriate measures in tackling them. This model can have its usage in portable devices where the user can take pictures and use them to detect the disease in the leaf.

Apple leaf, Plant disease detection, YOLOv8, Convolutional Neural Networks, Python, Machine Learning, Deep Learning.

"Optimizing Apple Orchard Health with YOLOv8-Driven Leaf Disease Identification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 1, page no.b669-b674, January-2026, Available :http://www.jetir.org/papers/JETIR2601200.pdf

"Optimizing Apple Orchard Health with YOLOv8-Driven Leaf Disease Identification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 1, page no. ppb669-b674, January-2026, Available at : http://www.jetir.org/papers/JETIR2601200.pdf