Robust Fall Army Worm detection in maize using multimodal RGB and thermal image fusion.

ABSTRACT: Effective pest and disease detection plays a crucial role in minimizing crop losses and improving decision-making in precision agriculture. Among the most destructive pests affecting maize crops globally is the Fall Army Worm (FAW), known for its rapid spread and high impact on yield. Existing detection practices often rely on manual scouting, which can be inefficient, labour intensive and prone to human error. This study proposes a novel deep learning based framework for the automatic classification of FAW infested and healthy maize crops by integrating RGB and thermal image modalities. The core objective is to enhance detection accuracy through multimodal image fusion. A hybrid DNN-ViT model is introduced, combining two complimentary pipelines: (i) feature-level fusion, where CNN extract

SUBMITTER: Sandhya P 

PROVIDER: S-EPMC12775024 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature