Title: Machine vision for characterisation of some phenomic features of plant parts in distinguishing varieties - a review

Authors: Nachiket Kotwaliwale; Karan Singh; Shyamal Kumar Chakrabarty; Monika A. Joshi; Abhimannyu Kalne; Madhvi Tiwari; K.K. Gangopadhyay; Nabarun Bhattacharya; Amitava Akuli; Divya Aggarwal; Gopinath Bej

Addresses: Indian Council of Agricultural Research, Central Institute of Agricultural Engineering, Bhopal, India ' Indian Council of Agricultural Research, Central Institute of Agricultural Engineering, Bhopal, India ' Indian Council of Agricultural Research, Indian Agricultural Research Institute, New Delhi, India ' Indian Council of Agricultural Research, Indian Agricultural Research Institute, New Delhi, India ' Centre of Excellence on Medicinal, Aromatic Plants, and Non-timber Forest Produce, Indira Gandhi Agricultural University, Raipur, India ' Indian Council of Agricultural Research, Central Institute of Agricultural Engineering, Bhopal, India ' Indian Council of Agricultural Research, National Bureau of Plant Genetic Resources, New Delhi, India ' Centre for Development of Advanced Computing, Bidhannagar, Salt Lake, Kolkata, India ' Centre for Development of Advanced Computing, Bidhannagar, Salt Lake, Kolkata, India ' Indian Council of Agricultural Research, Indian Agricultural Research Institute, New Delhi, India ' Centre for Development of Advanced Computing, Bidhannagar, Salt Lake, Kolkata, India

Abstract: Phenomic features of plant parts are important varietal traits for all crops and form part of the distinctiveness, uniformity and stability (DUS) characterisation protocols. Manual methods for measurement of these traits are expensive, less consistent and time consuming hence machine vision has been used in recent researches. The machine vision systems employed for this purpose consist of acquisition systems (hardware) and image processing and analysis system (software). The area of machine vision has developed during last few decades and there have been many improvements in the employed hardware and software. Major work has been reported on use of seed and fruit images; however, images of other plant parts like leaves, roots, flowers etc. have also been used. A variety of techniques have been reported for analysis of features in order to distinguish among different crop varieties.

Keywords: machine vision; crop variety identification; phenomic traits.

DOI: 10.1504/IJBIC.2019.103960

International Journal of Bio-Inspired Computation, 2019 Vol.14 No.4, pp.201 - 212

Received: 13 Aug 2018
Accepted: 08 Dec 2018

Published online: 27 Nov 2019 *

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