Tomato ripeness classification based on federated learning model

Songpo Tian; Gongwei Wang; Jiazhen He; Xin Fu; Le Han

doi:10.1117/12.3049926

8 November 2024 Tomato ripeness classification based on federated learning model

Songpo Tian, Gongwei Wang, Jiazhen He, Xin Fu, Le Han

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134161T (2024) https://doi.org/10.1117/12.3049926
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

Currently, automated tomato picking methods have improved production efficiency, but it is still unavoidable for unripe and rotten tomatoes to be mixed in during the picking process, leading to a certain degree of resource waste. Therefore, it is necessary to effectively identify the ripeness of tomatoes before picking in order to select those with appropriate maturity. But challenges arise from varying geographical conditions, diverse planting technology, and data privacy concerns of data owners. Thus, this research endeavors to devise a strong federated learning framework with the intention of addressing the data silo issue and identifying tomato ripeness across various fields and regions. In this research, we assessed the capabilities of multiple pre-trained deep learning frameworks by utilizing a dataset specifically for tomato ripeness classification. The experiment simulated a environment with varying client sizes, ranging from 3 to 9. The study emulated a federated learning setup with clients varying in number from 3 to 9. Upon analyzing the outcomes, it was discovered that InceptionNet outperformed the others, attaining a remarkable success rate of 97.85% in determining tomato ripeness levels. This investigation illustrates that federated learning has the potential to improve the precision of tomato ripeness identification, providing significant information for the enhancement of agricultural methodologies.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Songpo Tian, Gongwei Wang, Jiazhen He, Xin Fu, and Le Han "Tomato ripeness classification based on federated learning model", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134161T (8 November 2024); https://doi.org/10.1117/12.3049926

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KEYWORDS

Data modeling

Machine learning

Deep learning

Education and training

Agriculture

Data privacy

Image classification

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