Open AccessDetection and localization of cotton based on deep neural networks
Author(s) -
B R Annapoorna,
R. Ramésh
Publication year - 2021
Publication title -
international journal on recent and innovation trends in computing and communication
Language(s) - English
Resource type - Journals
ISSN - 2321-8169
DOI - 10.17762/ijritcc.v9i4.5461
Subject(s) - computer science , identification (biology) , artificial intelligence , artificial neural network , set (abstract data type) , deep learning , machine learning , pattern recognition (psychology) , biology , programming language , botany
Cotton detection is the localization and identification of the cotton in an image. It has a wide application in robot harvesting. Various modern algorithms use deep learning techniques for detection of fruits/flowers. As per the survey, the topics travelled include numerous algorithms used, and accuracy obtained on using those algorithms on their data set. The limitations and the advantages in each paper, are also discussed. This paper focuses on various fruit detection algorithms- the Faster RCNN, the RCNN, YOLO. Ultimately, a rigorous survey of many papers related to the detection of objects like fruits/flowers, analysis of the assets and faintness of each paper leads us to understanding the techniques and purpose of algorithms.