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Computer Vision and Machine Learning Based Grape Fruit Cluster Detection and Yield Estimation Robot

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Title Computer Vision and Machine Learning Based Grape Fruit Cluster Detection and Yield Estimation Robot
 
Creator Chauhan, Amit
Singh, Mandeep
 
Subject Image processing
OpenCV
Random forest
Scatter plot
 
Description 866-872
Estimation and detection of fruits plays a crucial role in harvesting. Traditionally, fruit growers rely on manual methods
but nowadays they are facing problems of rapidly increasing labor costs and labour shortage. Earlier various techniques were
developed using hyper spectral cameras, 3D images, clour based segmentation where it was difficult to find and distinguish
grape bunches. In this research computer vision based novel approach is implemented using Open Source Computer Vision
Library (OpenCV) and Random Forest machine learning algorithm for counting, detecting and segmentation of blue grape
bunches. Here, fruit object segmentation is based on a binary threshold and Otsu method. For training and testing, classification
based on pixel intensities were taken by a single image related to grape and non-grape fruit. The validation of developed
technique represented by random forest algorithm achieved a good result with an accuracy score of 97.5% and F1-Score of
90.7% as compared to Support Vector Machine (SVM). The presented research pipeline for grape fruit bunch detection with
noise removal, training, segmentation and classification techniques exhibit improved accuracy.
 
Date 2022-08-03T04:50:39Z
2022-08-03T04:50:39Z
2022-08
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/60248
https://doi.org/10.56042/jsir.v81i08.57971
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.81(08) [AUG 2022]