A Review of Convolutional Neural Network Applied to Fruit Image Processing
By José Antonio Naranjo-Torres, Marco Mora, Ruber Hernández-García, Ricardo J. Barrientos, Claudio Fredes, Andres Valenzuela. Applied Sciences, 2020
352 citations recorded by the source corpus.
Agriculture has always been an important economic and social sector for humans. Fruit production is especially essential, with a great demand from all households. Therefore, the use of innovative technologies is of vital importance for the agri-food sector. Currently artificial intelligence is one very important technological tool widely used in modern society. Particularly, Deep Learning (DL) has several applications due to its ability to learn robust representations from images. Convolutional Neural Networks (CNN) is the main DL architecture for image classification. Based on the great attention that CNNs have had in the last years, we present a review of the use of CNN applied to different automatic processing tasks of fruit images: classification, quality control, and detection. We observe that in the last two years (2019–2020), the use of CNN for fruit recognition has greatly increased obtaining excellent results, either by using new models or with pre-trained networks for transfer learning. It is worth noting that different types of images are used in datasets according to the task performed. Besides, this article presents the fundamentals, tools, and two examples of the use of CNNs for fruit sorting and quality control.
Subfields: Analytical Chemistry, Ecology, Plant Science
View Paper: https://www.semanticscholar.org/paper/1e0971dcabafc506cc42f8791a8cbde5ded2b677
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Citations in the corpus
- Convolutional Neural Networks: A Survey (2023, neutral): “Convolutional neural networks (CNNs) [14–16] are a popular type of ML model that have been widely utilized for various tasks, including image recognition [17–20], speech recognition [21–25], and NLP [26–30].”
- Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM (2021, methodology): “The neural network models are more abstract, and we do not have the accessibility to customize the model.”