Papers using Fruits-360 dataset

About Fruits-360 dataset

https://github.com/fruits-360

How to add papers

If your paper is not here, please add it by:

• updating this repository or,

• filling Fruits-360 Google Form or,

• send it to me, by email at [email protected].

Please upload pdf in the papers-pdf folder. Please rename the file to have the same name as the paper.

Only papers using Fruits-360 dataset in the numerical experiments are accepted in this repository.

Last update: 2025.12.28.0

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2018 2019 2020 2021 2022 2023 2024 2025

2018

1. Horea Mureșan, Mihai Oltean, Fruit recognition from images using deep learning, Acta Univ. Sapientiae, Informatica, Vol. 10, Issue 1, pp. 26-42, 2018.

2. Ma Linjuan, Fuquan Zhang, Lin Xu, Fruit Detection Using Faster R-CNN Based, Advances in Smart Vehicular Technology, Transportation, Communication and Applications: Proceeding of the Second International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, Mount Emei, China, Part 2. Vol. 128, Springer, 2018.

3. A. Kausar, M. Sharif, J. Park and D. R. Shin, Pure-CNN: A Framework for Fruit Images Classification, 2018 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, pp. 404-408, 2018.

4. Mustaffa M. R., Yi N. X., Abdullah L. N., Nasharuddin N. A., DURIAN RECOGNITION BASED ON MULTIPLE FEATURES AND LINEAR DISCRIMINANT ANALYSIS, Malaysian Journal of Computer Science, pp. 57–72. 2018.

5. M. Buzzelli, F. Belotti, R. Schettini, Recognition of Edible Vegetables and Fruits for Smart Home Appliances, IEEE 8th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), Berlin, Germany, pp. 1-4, 2018.

6. Alshalali, Tagrid, and Darsana Josyula. Fine-tuning of pre-trained deep learning models with extreme learning machine, 2018 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 469-473. IEEE, 2018.

7. Acosta, Daniel Jaime, Edgar Francisco Duque-Vazquez, Ilse Milena Villalba-Mantilla, and Jonathan Cepeda-Negrete. "Implementación de algoritmos de visión computacional para la automatización de sistemas agroindustriales." JÓVENES EN LA CIENCIA 4, no. 1 (2018): 2614-2619.

8. Gad, Ahmed Fawzy, Ahmed Fawzy Gad, and Suresh John, Practical computer vision applications using deep learning with CNNs, Berkeley: Apress, 2018.

9. Slavescu, Radu Razvan, and László Szakács. Towards Improving Location Identification by Deep Learning on Images, IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 345-351. IEEE, 2018.

2019

1. Dang Thi Phuong Chung, Dinh Van Tai, A fruits recognition system based on a modern deep learning technique, Journal of Physics: Conference Series, Volume 1327, V International Conference on Innovations in Non-Destructive Testing SibTest, 2019. DOI: 10.1088/1742-6596/1327/1/012050

2. Shadman Sakib, Zahidun Ashrafi, Md. Abu Bakr Siddique, Implementation of Fruits Recognition Classifier using Convolutional Neural Network Algorithm for Observation of Accuracies for Various Hidden Layers, 2019. DOI: 10.48550/arXiv.1904.00783

3. Turayki, Latifah Abdullah Bin, and Nirase Fathima Abubacker, SUFID: Sliced and Unsliced Fruits Images Dataset, International Visual Informatics Conference, pp. 237-244. Cham: Springer International Publishing, 2019.

4. Alzubaidi, Laith, Omran Al-Shamma, Mohammed A. Fadhel, Zinah Mohsin Arkah, and Fouad H. Awad. A deep convolutional neural network model for multi-class fruits classification, International Conference on Intelligent Systems Design and Applications, pp. 90-99. Cham: Springer International Publishing, 2019.

5. Siddiqi, Raheel. Effectiveness of transfer learning and fine tuning in automated fruit image classification, Proceedings of the 2019 3rd international conference on deep learning technologies, pp. 91-100. 2019.

6. Zhu, Dongmei, Min Wang, Qin Zou, Dingcai Shen, and Jiamei Luo. Research on fruit category classification based on convolution neural network and data augmentation., IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID), pp. 46-50. IEEE, 2019.

7. Huang, Ziliang, Yan Cao, and Tianbao Wang. Transfer learning with efficient convolutional neural networks for fruit recognition., IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 358-362. IEEE, 2019.

8. Biswas, Biswajit, Swarup Kr Ghosh, and Anupam Ghosh. A robust multi-label fruit classification based on deep convolution neural network,Computational Intelligence in Pattern Recognition: Proceedings of CIPR 2019, pp. 105-115. Singapore: Springer Singapore, 2019.

9. dos Santos, Fernando Pereira, and Moacir Antonelli Ponti. Alignment of local and global features from multiple layers of convolutional neural network for image classification, 32nd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 241-248. IEEE, 2019.

10. Zamzami, Nuha, and Nizar Bouguila. Hybrid generative discriminative approaches based on multinomial scaled dirichlet mixture models, Applied Intelligence 49, no. 11 (2019): 3783-3800.

11. Jones, Barett, Comparing Image Classification with Feature Extraction. The 20th Winona Computer Science Undergraduate Research Symposium, p. 10. 2019.

12. Khan, Rafflesia, and Rameswar Debnath. Multi class fruit classification using efficient object detection and recognition techniques, Int. J. Image Graph. Signal Process 11, no. 1 (2019).

13. Sharma, Abhinav, Jan N. Van Rijn, Frank Hutter, and Andreas Müller, Hyperparameter importance for image classification by residual neural networks, International conference on discovery science, pp. 112-126. Cham: Springer International Publishing, 2019.

14. Januário, Jailson, Elloá Guedes, and Fabio de Silva, Classificação do Índice Glicêmico a partir de Imagens de Alimentos com Redes Neurais Convolucionais, Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), pp. 493-502. SBC, 2019.

15. Desai, Maithili. A Fruit Recognition Approach for Refrigerator Inventory Management, International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), vol. 1, pp. 1-6. IEEE, 2019.

16. Widiasri, Monica. Image based indonesian fruit recognition using MPEG-7 colorstructure descriptor and k-nearest neighbor, Universitas Surabaya, 2019.

17. Rosa, Ayrton Lima da. Classificação de imagens de frutas utilizando aprendizado de máquina, 2019.

18. Славіта, С. Ю. Мобільний додаток визначення харчових продуктів рослинного походження на основі штучного інтелекту, 2019.

19. Bates, Christopher J., and Robert A. Jacobs. Efficient Data Compression Leads to Categorical Bias inPerception and Perceptual Memory, Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 41. 2019.

20. Bao, Yuxuan. A Study OF METHODS FOR TRAINING WITH DIFFERENT DATASETS IN IMAGE CLASSIFICATION, 2019.

21. Shadman Sakib, Md. Nazim Uddin, DIGITAL IMAGE RESTORATION AND IMAGE CLASSIFICATION USING DEEP LEARNING, PhD diss., International University of Business Agriculture and Technology, 2019.

2020

1. S. Ghosh, M. J. Mondal, S. Sen, S. Chatterjee, N. Kar Roy, S. Patnaik, A novel approach to detect and classify fruits using ShuffleNet V2, IEEE Applied Signal Processing Conference (ASPCON), Kolkata, India, pp. 163-167, 2020. DOI: 10.1109/ASPCON49795.2020.9276669.

2. Ghazanfar Latif, Batool Alsalem, Wejdan Mubarky, Nazeeruddin Mohammad, Jaafar Alghazo, Automatic Fruits Calories Estimation through Convolutional Neural Networks, In Proceedings of the 2020 6th International Conference on Computer and Technology Applications (ICCTA '20). Association for Computing Machinery, New York, NY, USA, pp. 17–21, 2020. DOI: 10.1145/3397125.3397154

3. Seda Postalcıoğlu, Performance Analysis of Different Optimizers for Deep Learning-Based Image Recognition, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 34, No. 02, 2020. DOI: 10.1142/S0218001420510039

4. S. Yu, K. Wickstrøm, R. Jenssen, J. C. Príncipe, Understanding Convolutional Neural Networks With Information Theory: An Initial Exploration, in IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, No. 1, pp. 435-442, Jan. 2021, DOI: 10.1109/TNNLS.2020.2968509.

5. Pritom Sarker, Mehenag Khatun, Forhad Ali, Nakib Aman Turzo, Julker Nine, Fruits Classification using Convolutional Neural Network, Global Research and Development Journal For Engineering, Vol. 58: pp. 1-6, 2020.

6. Huang, Calvin, and Zhengbo Wang. Final Report on Common Fruits Identification, 2020.

7. Munir, Khadija, Arif Iqbal Umar, and Waqas Yousaf. Automatic fruits classification system based on deep neural network, NUST Journal of Engineering Sciences 13, no. 1 (2020): 37-44.

8. Postalcıoğlu, Seda. "Performance analysis of different optimizers for deep learning-based image recognition." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 02 (2020): 2051003.

9. Kodors, Sergejs. Pear and apple recognition using deep learning and mobile, 19th International Scientific Conference Engineering for Rural Development Proceedings. 2020.

10. Siddiqi, Raheel. "Comparative performance of various deep learning based models in fruit image classification." In Proceedings of the 11th International Conference on Advances in Information Technology, pp. 1-9. 2020.

11. Yu, Shujian, Kristoffer Wickstrøm, Robert Jenssen, and Jose C. Principe. Understanding convolutional neural networks with information theory: An initial exploration, IEEE transactions on neural networks and learning systems 32, no. 1 (2020): 435-442.

12. Martin, Jörg, and Clemens Elster. Inspecting adversarial examples using the fisher information, Neurocomputing 382 (2020): 80-86.

13. Barreto, Fabian, Sushilkumar Yadav, Suprava Patnaik, and Jignesh Sarvaiya. "Sift features for deep and variational autoencoders: A performance comparison." In 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), pp. 652-655. IEEE, 2020.

14. Ünal, Haci Bayram, Ebru Vural, Burcu Kir Savaş, and Yaşar Becerikli. Fruit recognition and classification with deep learning support on embedded system (fruitnet), Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 1-5. IEEE, 2020.

15. Srivastava, Swapnil, Tripti Singh, Sakshi Sharma, Anil Verma, and A. Abdul. "A fruit recognition system based on modern deep learning technique." Int. J. Eng. Res. Technol 9 (2020): 896-898.

16. Duong, Linh T., Phuong T. Nguyen, Claudio Di Sipio, and Davide Di Ruscio. Automated fruit recognition using EfficientNet and MixNet, Computers and Electronics in Agriculture 171 (2020): 105326.

17. Himabindu, D. Dakshayani, and S. Praveen Kumar. "A comprehensive analytic scheme for classification of novel models." In 2020 3rd international conference on intelligent sustainable systems (ICISS), pp. 564-569. IEEE, 2020.

18. Orquia, John Jowil D. Automated fruit classification using deep convolutional neural network, Philippine Social Science Journal 3, no. 2 (2020): 177-178.

19. Tomar, Hanshu. Multi-class image classification of fruits and vegetables using transfer learning techniques, PhD diss., Dublin Business School, 2020.

20. Naidu, Himanshu, S. Rajkumar, K. C. Santosh, and P. V. S. S. R. Chandra Mouli. "A fast and efficient convolutional neural network for fruit recognition and classification." In International Conference on Recent Trends in Image Processing and Pattern Recognition, pp. 148-157. Singapore: Springer Singapore, 2020.

21. Iyer, Krithika. GAN generated images of fruit decay, 2020.

22. Ummapure, Suryakanth B., and Shashikiran M. Hanchinal. Multi Features based Fruit Classification Using different Classifiers, Journal of University of Shanghai for Science and Technology 22, no. 12 (2020): 1344-1356.

23. Dipu Kabir, H. M., Moloud Abdar, Seyed Mohammad Jafar Jalali, Abbas Khosravi, Amir F. Atiya, Saeid Nahavandi, and Dipti Srinivasan. "Spinalnet: Deep neural network with gradual input." arXiv e-prints (2020): arXiv-2007.

24. Amol, Mutha Sahil, and Shah Akshat Mayur. Maturity Detection of Tomatoes using Deep learning, Department of Information Technology (2020): 121.

25. Kathepuri, Shubham. Recognition and classification of fruits using deep learning techniques, PhD diss., Dublin, National College of Ireland, 2020.

26. Rojas-Aranda, Jose Luis, Jose Ignacio Nunez-Varela, Juan Carlos Cuevas-Tello, and Gabriela Rangel-Ramirez. "Fruit classification for retail stores using deep learning." In Mexican Conference on Pattern Recognition, pp. 3-13. Cham: Springer International Publishing, 2020.

27. Muhathir, Muhathir, Wahyu Hidayah, and Dian Ifantiska. "Utilization of Support Vector Machine and Speeded up Robust Features Extraction in Classifying Fruit Imagery." Computer Engineering & Applications Journal 9, no. 3 (2020).

28. Muhathir, Muhathir, Muhammad Hamdani Santoso, and Rizki Muliono. "Analysis naã ve bayes in classifying fruit by utilizing hog feature extraction." Journal of Informatics and Telecommunication Engineering 4, no. 1 (2020): 151-160.

29. Ramadevi, Kotagiri, and A. Poongodai. "Fruit Detection Using Recurrent Convolutional Neural Network (RCNN)." In ICCCE 2020: Proceedings of the 3rd International Conference on Communications and Cyber Physical Engineering, pp. 1241-1248. Singapore: Springer Nature Singapore, 2020.

30. Wan, Shaohua, and Sotirios Goudos. "Faster R-CNN for multi-class fruit detection using a robotic vision system." Computer Networks 168 (2020): 107036.

31. Dehkordi, Amirhoshang Hoseinpour, Majid Alizadeh, and Ali Movaghar. "Meet MASKS: A novel Multi-Classifier's verification approach." arXiv preprint arXiv:2007.10090 (2020).

32. Busson, Antonio José G., Paulo Renato C. Mendes, Daniel de S. Moraes, Álvaro Mário G. da Veiga, Sérgio Colcher, and Álan Lívio V. Guedes. "Decoder-Side Quality Enhancement of JPEG Images Using Deep Learning-Based Prediction Models for Quantized DCT Coefficients." In Proceedings of the Brazilian Symposium on Multimedia and the Web, pp. 129-136. 2020.

33. Yohannes, Yohannes, Muhammad Rizky Pribadi, and Leo Chandra. "Klasifikasi jenis buah dan sayuran menggunakan SVM dengan fitur Saliency-HOG dan color moments." Elkha 12, no. 2 (2020): 125-131.

2021

1. Mihai Oltean, Fruits-360 dataset: new research directions, Technical report, 2021.

2. Wang Shijie, Guiling Sun, Bowen Zheng, Yawen Du, A Crop Image Segmentation and Extraction Algorithm Based on Mask RCNN, Entropy, Vol. 23, No. 9: 1160. 2021. DOI: https://doi.org/10.3390/e23091160

2022

1. Horea Mureşan, An Automated Algorithm for Fruit Image Dataset Building, 17th Conference on Computer Science and Intelligence Systems (FedCSIS), Sofia, Bulgaria, pp. 103-107, 2022. DOI: 10.15439/2022F58

2. Aherwadi Nagnath, Usha Mittal, Jimmy Singla, N. Z. Jhanjhi, Abdulsalam Yassine, M. Shamim Hossain, Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms, Electronics, Vol. 11, Issue 24, 4100, 2022. https://doi.org/10.3390/electronics11244100

2023

1. Salim Farsana, Faisal Saeed, Shadi Basurra, Sultan Noman Qasem, Tawfik Al-Hadhrami, DenseNet-201 and Xception Pre-Trained Deep Learning Models for Fruit Recognition, Electronics, Vol. 12, No. 14: 3132. 2023. DOI: https://doi.org/10.3390/electronics12143132

2. Achanta Jyothi Prakash, P. Prakasam, An intelligent fruits classification in precision agriculture using bilinear pooling convolutional neural networks, The Visual Computer, Vol. 39, pp. 1765–1781, 2023. DOI: https://doi.org/10.1007/s00371-022-02443-z

2024

1. Ciapas B., Treigys P., Centre-loss—A preferred class verification approach over sample-to-sample in self-checkout products datasets, IET Computer Vision, Vol. 18, Issue 7, pp. 1004–1016, 2024. DOI: https://doi.org/10.1049/cvi2.12302

2. Yonis Gulzar, Zeynep Ünal, Shahnawaz Ayoub, Faheem Ahmad Reegu, Alhanouf Altulihan, Adaptability of deep learning: datasets and strategies in fruit classification, BIO Web Conf. 85 01020, 2024. DOI: 10.1051/bioconf/20248501020

3. Chunyang Zhu, Lei Wang, Weihua Zhao, Heng Lian, Image classification based on tensor network DenseNet model, Appl Intell Vol. 54, pp. 6624–6636, 2024. DOI: https://doi.org/10.1007/s10489-024-05472-4

4. Xu, Qinghui, Chen Luo, Dou Wen, Runhua Yang, Yilin Wan, Zongqi Ge, RESEARCH ON FRUIT IMAGE PROCESSING CLASSIFICANTION AND RECOGNITION BASED ON RESNET50 NEURAL NETWORK, Scientific Innovation in Asia, Vol. 2, no. 1, pp. 1-8, 2024.

5. Zárate, Víctor, Danilo Cáceres Hernández, Simplified Deep Learning for Accessible Fruit Quality Assessment in Small Agricultural Operations, Applied Sciences, Vol. 14, Issue 18, 8243, 2024.

2025

1. Sathyadhas G.N., Gladston A., Nehemiah K.H., LwSANet: Light weight Self-Attention Network model to recognize fruits from images, Traitement du Signal, Vol. 42, No. 1, pp. 183-200, 2025.

2. Panhwar Ali Orangzeb, Anwar Ali Sathio, Nadim Manzoor Shah, Sumaira Memon, A Scheme Based on Deep Learning for Fruit Classification, Mehran University Research Journal Of Engineering & Technology, vol. 44, no. 1, Mehran University of Engineering & Technology, 2025, pp. 8–19, 2025. DOI: https://doi.org/10.22581/muet1982.2742

3. Mansouri Merieme, Chaouni Samia Benabdellah, Andaloussi Said Jai, Ouchetto Ouail, Azbeg Kebira, Estimating glycemic index in a specific dataset: The case of Moroccan cuisine, Journal of Intelligent Systems, Vol. 34, No. 1, pp. 20240122, 2025. DOI: https://doi.org/10.1515/jisys-2024-0122

4. Sadique P.M.A., Aswiga R.V., Automatic summarization of cooking videos using transfer learning and transformer-based models, Discov Artif Intell Vol. 5, Issue 7, 2025. https://doi.org/10.1007/s44163-025-00230-y

5. Robi Wariyanto Abdullah, Rajnaparamitha Kusumastuti ,Handoko, IMAGE FEATURE EXTRACTION PROCESSING ANALYSIS FOR APPLE TYPE CLASSIFICATION USING SCIKIT-LEARN WITH THE K-NEAREST NEIGHBOR ALGORITHM, Jurnal Teknologi Informasi Dan Ilmu Komputer, Vol. 12, Issue 1, pp. 165-174, 2025.

6. Cao, Xuan, Vu Nguyen, and Tham Vo. FruitNet-121: An Intelligent Fruit Classification System Based on DenseNet121. International Conference on Data Analytics & Management, pp. 519-529. Cham: Springer Nature, 2025.

7. Erşahin, Rukiye Ertürk, and Fesih Keskin, A Comparative Analysis of Deep Learning Architectures for Robust Fruit Detection, 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA), pp. 1-6. IEEE, 2025.

8. Mim, Tasauf, Md Mahbubur Rahman, Jahanur Biswas, Ahmad Shafkat, and Khandaker Mohammad Mohi Uddin, FruitsMultiNet: A Deep Neural Network Approach to Identify Fruits through Multi-scale Feature Fusion using Mobile Interface Journal of Agriculture and Food Research, 102083, 2025.

9. Vasilevschi, Ana-Mihaela, and Daniela Faur, Exploring Image Dataset Structure through Deep Feature Extraction and Dimensionality Reduction, 25th International Conference on Control Systems and Computer Science (CSCS), pp. 341-348. IEEE, 2025.

10. Park, Woohyun, Gimun Kim, Hyojeong Chae, Seungjun Lee, and Sungjun Kim, HfAlOx-based optical ferroelectric memristor with transparent electrode for RGB color image classification via physical reservoir, Nano Energy, 111190, 2025.

11. Roman, Ali, Md Mostafizer Rahman, Sajjad Ali Haider, Tallha Akram, and Syed Rameez Naqvi, Integrating feature selection and deep learning: a hybrid approach for smart agriculture applications, Algorithms 18, no. 4, 222, MDPI, 2025.