Abstract
One of the applications based on computer vision is plant phenotyping. It is an analytical process of assessment of external features of the plants. Rice husk (panicle covering) extracts the natural and sustainable biomass source for silica and afterward uses it for manufacturing value-based, silicon-added materials. Rice husk is directly used for burning to produce electricity on power generation plants by using rice husk ash. Rice husk shows the matured stage of plant growth. Plant growth can be measured by its height and panicle counts. Computer vision-based tools and techniques are discussed in this paper to measure the height and count the panicles of a rice crop. The objective of this paper is to survey the trends applied in the direction of computer vision based plant phenotyping by using image processing techniques, machine learning and deep learning. However, for applications in the plant phenotyping field, where available datasets are often small, it has been found through the study that the costs associated with generating new data are high. Therefore, synthetic crop image datasets are used by some researchers to make a model, but it is still not very useful because sometimes training datasets and testing datasets will differ exponentially. Table 10.1 describes time and cost effective convolutional neural network method for its significant use. This paper provides a recent study done on plant phenotyping, rice husk by using machine learning and deep learning. It is also found that deep learning outperforms other mechanism for real time data analysis.
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Acknowledgments
Authors would like to thank the National Institute of Technology, Raipur, for providing necessary infrastructure and facility for the research. Special thanks to Indira Gandhi Krishi Vishwavidyalaya, Raipur, for helping us in this research work.
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Patel, B., Sharaff, A. (2019). Research Trends and Systematic Review of Plant Phenotyping. In: Sinha, G. (eds) Advances in Biometrics. Springer, Cham. https://doi.org/10.1007/978-3-030-30436-2_10
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DOI: https://doi.org/10.1007/978-3-030-30436-2_10
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