Detection of citrus diseases in complex backgrounds based on image-text multimodal fusion and knowledge assistance.

ABSTRACT: Diseases pose a significant threat to the citrus industry, and the accurate detection of these diseases represent key factors for their early diagnosis and precise control. Existing diagnostic methods primarily rely on image models trained on vast datasets and limited their applicability due to singular backgrounds. To devise a more accurate, robust, and versatile model for citrus disease classification, this study focused on data diversity, knowledge assistance, and modal fusion. Leaves from healthy plants and plants infected with 10 prevalent diseases (citrus greening, citrus canker, anthracnose, scab, greasy spot, melanose, sooty mold, nitrogen deficiency, magnesium deficiency, and iron deficiency) were used as materials. Initially, three datasets with white, natural, and mixed backgrou

SUBMITTER: Qiu X 

PROVIDER: S-EPMC10711083 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature