Citizen science has emerged as a valuable resource for scientific research, providing large volumes of data for training deep learning models.However, the quality and accuracy of crowd-sourced data pose significant challenges for supervised learning tasks such as plant trait detection.This study investigates the application of AI techniques to address these issues within natural science.We explore the potential of multi-modal data analysis and ensemble methods to improve the accuracy of plant trait classification using citizen science data.Additionally, we examine the effectiveness of transfer learning from authoritative datasets like PlantVillage to enhance model performance on openaccess platforms such as iNaturalist.By analysing the strengths and limitations of AI-driven approaches in this context, we aim to contribute to developing robust and reliable methods for utilising citizen science data in natural science.