Image Analysis Techniques on Phenotype for Plant System
Karthiga Rani D.
Karthiga Rani, Department of Computer Science and IT, N.M.S. Sermathai Vasan College for Women, Madurai (Tamil Nadu), India.
Manuscript received on 24 November 2019 | Revised Manuscript received on 18 December 2019 | Manuscript Published on 30 December 2019 | PP: 565-568 | Volume-9 Issue-1S4 December 2019 | Retrieval Number: A11251291S419/19©BEIESP | DOI: 10.35940/ijeat.A1125.1291S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In recent years, there is a rapid advancement in computer vision technology which is much effective in extracting useful information from plant images in the field of plant phenomics. Phenomic approaches are widely used in the identification of relationship between phenotypic traits and genetic diversities among the plant species. The need for automation and precision in phenotyping have been accelerated by the significant advancement in genotyping. Regardless of its significance, the shortage of freely available research databases having plant imageries has significantly obstructed the plant image analysis advancement. There were several existing computer vision techniques employed in the analysis of plant phenotypes. Conversely, recent trends in image analysis with the use of machine learning and deep learning based approaches including convolutional neural networks have increased their expansion for providing high-efficiency phenotyping of plant species. Thus, to enhance the efficiency of phenotype analysis, various existing machine learning and deep learning algorithms have been reviewed in this paper along with their methods, advantages, and limitations.
Keywords: Phenomics, Machine Learning, Deep Learning, Phenotyping, Convolutional Neural Networks.
Scope of the Article: Image analysis and Processing