Fruit Detection and Identification from Realtime Video Sequences: A Review

DOI

10.2991/978-94-6463-136-4_83How to use a DOI?

Keywords

Real time video sequences; Fruit detection; Support Vector Machine; k-means clustering; Convolution Neural Network; Deep learning

Abstract

Object detection in real-time video sequence is still a big challenge as the images are captured with different modalities such as occlusion, clustering, scale, size, color, illumination changes due to complex environment, low quality camera, trees shake and noise. To overcome these challenges, researchers have used a variety of image processing techniques and algorithms to develop successful fruit detection system in order to achieve high accuracy. This paper presents a review of research work done on fruit detection and recognition systems. Also summarizes the features, image segmentation and classification methods used in the existing fruit detection systems such as Convolution Neural Network (CNN), k-means clustering, Support Vector Machine (SVM), deep learning etc. Finally, the future challenges, scope and proposed system for fruit detection and identification from realtime video sequences is discussed. The proposed fruit detection system will make use of deep learning algorithms along with Convolution Neural Network (CNN) in order to increase the classification and recognition accuracy.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Shriram D. Raut
AU  - Jyoti V. Mashalkar
PY  - 2023
DA  - 2023/05/01
TI  - Fruit Detection and Identification from Realtime Video Sequences: A Review
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 943
EP  - 952
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_83
DO  - 10.2991/978-94-6463-136-4_83
ID  - Raut2023
ER  -