Robust Convolutional Neural Network Model For Recognition of Fruits

Abstract

Objectives: To develop a model for the automatic recognition of fruits utilizing deep learning techniques. Methods: We have designed a fruit classification and recognition Model using Convolutional Neural Networks (CNN). We have used excellent quality ImageNet dataset of fruit images for evaluation purpose. It contains 9,130 images of 11 different categories. The classification is challenging as the images comprise different fruits of the same color and shape, overlapped fruits, the background is not homogenous, and with different light effects etc. Findings: We have achieved a validation accuracy of 91.28 % and the testing accuracy of 100%. The same model is trained on the fruits-360 dataset with 92 categories of fruits with 47,526 images. The Model gives validation accuracy of 100% and testing accuracy of 100%. This study also compares the results obtained using transfer learning by training the EfficientNet-b0 architecture with the ImageNet and fruits-360 dataset. The validation accuracy is 96.77% and the testing accuracy is 100%. Again, the validation accuracy and testing accuracy for fruits-360 dataset is 100% and 99.9% respectively. Applications: Recognition of fruit is required in agricultural problems like robot harvesting and fruit counting and many more applications. Moreover, it can be used in the retail business, as a self-service system to recognize the fruits. It can be used in human-robot interactions. Novelty: The model once trained can achieve state-of-art accuracy for the recognition of any type of fruit with any background. It sometimes exceeds human-level performance. Hence, the Model is Robust enough to recognize the fruits. Keywords: Deep learning; Convolutional Neural Networks; Fruit recognition; ImageNet; Transfer learning; Machine learning; fruits-360 dataset; EfficientNet-b0 model