Fruit recognition from images using deep learning

By Horea-Bogdan Muresan, Mihai Oltean. Acta Universitatis Sapientiae: Informatica, 2017

440 citations recorded by the source corpus.

Abstract In this paper we introduce a new, high-quality, dataset of images containing fruits. We also present the results of some numerical experiment for training a neural network to detect fruits. We discuss the reason why we chose to use fruits in this project by proposing a few applications that could use such classifier.

Subfields: Analytical Chemistry, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/7856b3a1ad066b81a67c22c6d739e34c55cf6d89

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Thinagaran Perumal
2. Norwati Mustapha
3. Mark Harman
4. Tomas Pfister
5. Lei Ma
6. Sercan Ö. Arik
7. Raihani Mohamed
8. Yang Liu

In Ecology

1. Thinagaran Perumal
2. Norwati Mustapha
3. Tomas Pfister
4. Sercan Ö. Arik
5. Raihani Mohamed
6. Farhad Shiri

In Plant Science

1. Thinagaran Perumal
2. Norwati Mustapha
3. Tomas Pfister
4. Sercan Ö. Arik
5. Raihani Mohamed
6. Yang Liu
7. Farhad Shiri

Citations in the corpus

• Effectiveness of Arbitrary Transfer Sets for Data-free Knowledge Distillation (2020, background): “Fruits360 [16] that contains images of fruits and vegetables.”
• Machine Learning Testing: Survey, Landscapes and Horizons (2019, background): “Fruits 360 [259] Dataset with 65,429 images of 95 fruits 65,429 correctness,robustness”
• Attention-Based Prototypical Learning Towards Interpretable, Confident and Robust Deep Neural Networks (2019, background): “Large image datasets (Fruits (Muresan & Oltean, 2017) and ISIC Melanoma (ISIC, 2016)) use an extra strided convolutional layer inserted for downsampling by a factor of 2, followed by max pooling for downsampling by another factor of 2.”
• A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU (2023, methodology): “In our experimental analysis, we utilized three publicly available datasets: IMDB [ 106 ], ARAS [ 107 ], and Fruit-360 [ 108 ].”