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

• Autorzy: Ignesti Giacomo , Moroni Davide , Martinelli Massimo

Identyfikatory

DOI

10.15439/2024F8703

• Konferencja: Federated Conference on Computer Science and Information Systems (19 ; 08-11.09.2024 ; Belgrade, Serbia)
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

training; deep learning; plant diseases; accuracy; federated learning; transfer learning; supervised learning; reliability; ensemble learning; artificial intelligence

PL

szkolenie; uczenie głębokie; choroba roślin; dokładność; uczenie federacyjne; uczenie transferowe; uczenie nadzorowane; niezawodność; uczenie zespołowe; sztuczna inteligencja

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2024
• Tom: Vol. 39
• Strony: 625--630
• Opis fizyczny: Bibliogr. 24 poz.,il., tab., wykr.

Twórcy

autor

Ignesti Giacomo

• CNR-ISTI, Via Giuseppe Moruzzi, 1, 56124 Pisa, Italy University of Pisa, Second Floor, Largo Bruno Pontecorvo, 3, 56127 Pisa, Italy

• https://orcid.org/0000-0003-2389-3086

autor

Moroni Davide

• CNR-ISTI, Via Giuseppe Moruzzi, 1, 56124 Pisa, Italy

• https://orcid.org/0000-0002-5175-5126

autor

Martinelli Massimo

• CNR-ISTI, Via Giuseppe Moruzzi, 1, 56124 Pisa, Italy

• https://orcid.org/0000-0001-7419-5099

Bibliografia

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Uwagi

Thematic Sessions: Short Papers