Plant-traits: how citizen science and artificial intelligence can impact natural science

Ignesti G., Moroni D., Martinelli M.

Transfer Learning  Citizen science  Artificial Intelligence  Domain Adaptation  Enviromental Science  AI  Natural science  Citizen Science  Deep Learning  Plant Traits 

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 open- access 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.

Source: ANNALS OF COMPUTER SCIENCE AND INFORMATION SYSTEMS, vol. 39, pp. 625-630. Belgrade, Serbia, 9-11/09/2024

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