Informed-Learning-Guided Visual Question Answering Model of Crop Disease.

ABSTRACT: In contemporary agriculture, experts develop preventative and remedial strategies for various disease stages in diverse crops. Decision-making regarding the stages of disease occurrence exceeds the capabilities of single-image tasks, such as image classification and object detection. Consequently, research now focuses on training visual question answering (VQA) models. However, existing studies concentrate on identifying disease species rather than formulating questions that encompass crucial multiattributes. Additionally, model performance is susceptible to the model structure and dataset biases. To address these challenges, we construct the informed-learning-guided VQA model of crop disease (ILCD). ILCD improves model performance by integrating coattention, a multimodal fusion model (MUT

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC11649200 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature