Compartmentalization of New Released and Old Wheat Cultivars (Triticum Durum &amp; Triticum Aestivum) of Gujarat Region of India by Employing Computer Vision

Raj, M. P. G.; Swaminarayan, Priya; Saini, Jatinderkumar R.

doi:10.1007/978-981-10-3433-6_1

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Наиболее часто анализируемые с использованием техники машинного обучения органы растениякорень и лист. Примерами успешного анализа листовых пластинок с использованием методов машинного обучения являются работы, выполненные на Triticum durum, Zea mays, Hordeum vulgare, Arabidopsis thaliana и некоторых других видах высших растений [11]. На базе машинного обу чения созданы технологии количественного анализа площади листьев и роста побегов [12], разработана динамическая модель развития розетки для изучения развития листа, оценен вклад генотипа в формирование фенотипа побега на ранних стадиях онтогенеза [13], выявлены закономерности изменения флуоресценции хлорофилла при воздействии важнейших патогенов [14], показана взаимосвязь между увеличением площади поверхности листа и накоплением биомассы [15] и др.…”

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Taxonomic determination and physiological analysis of ornamental plants using convolutional neural networks

Бондаренко

Барковский

Шашко

et al. 2019

Journal of the Belarusian State University. Biology

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Phenomics is an interdisciplinary scientific field, the object of research of which are phenotypes, their qualitative and quantitative parameters, as well as regularities of their formation during ontogenesis and as a result of interaction with external factors. The methodology of phenomics is a set of approaches for the phenotyping of plants, including a number of the most modern technologies of imaging, spectral analysis, biochemical, molecular and genetic analyses, and also innovative informatics techniques such as image recognition, computer vision and machine learning. The purpose of this work was to develop a phenomics application based on computer vision and methods of machine learning for taxonomic classification and determination of physiological condition of different ornamental plants. As a result of this work, the annotated databases Thuja occidentalis L., Forsythia intermedia Vahl, Heuchera micrantha Douglas ex Lindl., Syringa vulgaris L., Phalaenopsis × hybridum Blume, etc. were created and annotated. The model of a convolution neural network for taxonomic classification and determination of physiological condition of plants on the basis of RGB-images was developed. The training used images obtained in standardized conditions by high quality RGB-cameras. The neural network showed high efficiency of recognition, when analysing with taxonomic properties of decorative plants (about 90.8 %). The developed neural network also demonstrated coefficient of determination (R2 ) about 0.66 in the analysis of physiological state.

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