Background: Consumers all throughout the world enjoy kiwifruit. After harvest, there are as much as 20-25% of kiwifruit lost along the entire industrial chain. An intelligent flexible manipulator system based on flexible tactile sensing (IFMSFTS) was created to automatically and intelligently classify kiwifruit ripeness in order to minimize loss.
Result: The flexible manipulator is coupled with the flexible tactile sensor. When kiwifruits are being gripped by the manipulator, the flexible sensor perceives their firmness, and the mapping relationship between firmness and ripeness allows for the prediction and evaluation of the kiwifruit's ripeness. Principal component analysis (PCA) is employed to minimize the dimension of the sample firmness data set. K-Nearest neighbor (KNN) and support vector machine (SVM) classifiers are utilized to train and test the data. The findings indicate that PCA-KNN's classification accuracy is 97.5% and PCA-SVM's classification accuracy is 96.24%. The first is a better fit.
Conclusion: IFMSFTS can precisely classify ripeness, effectively address the issue of fruit loss, and realize the sustainable and clean production of fruit by sensing the firmness of kiwifruit and relying on the mapping link between firmness and ripeness. © 2023 Society of Chemical Industry.
Keywords: flexible tactile sensing; intelligent manipulator system; kiwifruit; ripeness classification.
© 2023 Society of Chemical Industry.