Tomato ripeness classification based on federated learning model

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.