Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8.

ABSTRACT: Cauliflower cultivation plays a pivotal role in the Indian Subcontinent's winter cropping landscape, contributing significantly to both agricultural output, economy and public health. However, the susceptibility of cauliflower crops to various diseases poses a threat to productivity and quality. This paper presents a novel machine vision approach employing a modified YOLOv8 model called Cauli-Det for automatic classification and localization of cauliflower diseases. The proposed system utilizes images captured through smartphones and hand-held devices, employing a finetuned pre-trained YOLOv8 architecture for disease-affected region detection and extracting spatial features for disease localization and classification. Three common cauliflower diseases, namely 'Bacterial Soft Rot', 'Downey

SUBMITTER: Uddin MS 

PROVIDER: S-EPMC11063243 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature