A Semantic Segmentation Approach on Sweet Orange Leaf Diseases Detection Utilizing YOLO

TL;DR

This paper presents an AI-based approach using YOLOv8 for accurate and rapid detection of sweet orange leaf diseases, aiming to improve traditional manual inspection methods and promote sustainable agriculture.

Contribution

It introduces the application of YOLOv8 and VIT models for disease detection in sweet orange leaves, demonstrating high accuracy and addressing practical implementation challenges.

Findings

YOLOv8 achieved 80.4% accuracy during training and validation.

VIT achieved 99.12% accuracy, showing detailed feature extraction.

The approach offers a faster, more reliable alternative to manual inspection.

Abstract

This research introduces an advanced method for diagnosing diseases in sweet orange leaves by utilising advanced artificial intelligence models like YOLOv8 . Due to their significance as a vital agricultural product, sweet oranges encounter significant threats from a variety of diseases that harmfully affect both their yield and quality. Conventional methods for disease detection primarily depend on manual inspection which is ineffective and frequently leads to errors, resulting in delayed treatment and increased financial losses. In response to this challenge, the research utilized YOLOv8 , harnessing their proficiencies in detecting objects and analyzing images. YOLOv8 is recognized for its rapid and precise performance, while VIT is acknowledged for its detailed feature extraction abilities. Impressively, during both the training and validation stages, YOLOv8 exhibited a perfect accuracy of 80.4%, while VIT achieved an accuracy of 99.12%, showcasing their potential to transform disease detection in agriculture. The study comprehensively examined the practical challenges related to the implementation of AI technologies in agriculture, encompassing the computational demands and user accessibility, and offering viable solutions for broader usage. Moreover, it underscores the environmental considerations, particularly the potential for reduced pesticide usage, thereby promoting sustainable farming and environmental conservation. These findings provide encouraging insights into the application of AI in agriculture, suggesting a transition towards more effective, sustainable, and technologically advanced farming methods. This research not only highlights the efficacy of YOLOv8 within a specific agricultural domain but also lays the foundation for further studies that encompass a broader application in crop management and sustainable agricultural practices.