Automatic Fruits Freshness Classification Using CNN and Transfer Learning

Author:

Amin Umer1,Shahzad Muhammad Imran1,Shahzad Aamir1ORCID,Shahzad Mohsin1ORCID,Khan Uzair1ORCID,Mahmood Zahid1ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan

Abstract

Fruit Freshness categorization is crucial in the agriculture industry. A system, which precisely assess the fruits’ freshness, is required to save labor costs related to tossing out rotten fruits during the manufacturing stage. A subset of modern machine learning techniques, which are known as Deep Convolution Neural Networks (DCNN), have been used to classify images with success. There have recently been many changed CNN designs that gradually added more layers to achieve better classification accuracy. This study proposes an efficient and accurate fruit freshness classification method. The proposed method has several interconnected steps. After the fruits data is gathered, data is preprocessed using color uniforming, image resizing, augmentation, and image labelling. Later, the AlexNet model is loaded in which we use eight layers, including five convolutional layers and three fully connected layers. Meanwhile, the transfer learning and fine tuning of the CNN is performed. In the final stage, the softmax classifier is used for final classification. Detailed simulations are performed on three publicly available datasets. Our proposed model achieved highly favorable results on all three datasets in which 98.2%, 99.8%, and 99.3%, accuracy is achieved on aforesaid datasets, respectively. In addition, our developed method is also computationally efficient and consumes 8 ms on average to yield the final classification result.

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. FRESHNets: Highly Accurate and Efficient Food Freshness Assessment Based on Deep Convolutional Neural Networks;INTELIGENCIA ARTIFIC;2024

2. Computer Vision based Greenhouse Fruits and Vegetables Identification – A Review;Journal of Innovative Image Processing;2024-09

3. A System and Method for Fruit Ripeness Prediction Using Transfer Learning and CNN;2024 International Conference on Communication, Computing and Internet of Things (IC3IoT);2024-04-17

4. Rotten and Fresh Fruits Classification using Deep Learning;2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT);2024-02-09

5. Deep Learning-Based Classification of Rotten Fruits and Identification of Shelf Life;Computers, Materials & Continua;2024

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