Fruit recognition from images using deep learning applications

Smart imaging devices have been used at a rapid rate in the agriculture sector for the last few years. Fruit recognition and classification is noticed as one of the looming sectors in computer vision and image classification. A fruit classification may be adopted in the fruit market for consumers to determine the variety and grading of fruits. Fruit quality is a prerequisite property from a health viewpoint. Classification systems described so far are not adequate for fruit recognition and classification during accuracy and quantitative analysis. Deep learning models have the ability to extract the potential image features without using handcrafted features. In this paper, Type-II Fuzzy, TLBO (Teacher-learner based optimization), and deep learning Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) applications proposed to enhance, segment, recognize and classify the fruit images. Thus, the examination of new proposals for fruit recognition and classification is worthwhile. In the present time, automatic fruit recognition and classification is though a demanding task. Deep learning is a powerful state-of-the-art approach for image classification. This task incorporates deep learning models: CNN, RNN, LSTM for classification of fruits based on chosen optimal and derived features. As preliminary arises, it has been recognized that the recommended procedure has effective accuracy and quantitative analysis results. Moreover, the comparatively high computational momentum of the proposed scheme will promote in the future.