Monitoring of plant growth through methods of phenotyping and image analysis
DOI:
https://doi.org/10.18380/SZIE.COLUM.2023.10.1.49Keywords:
phenotyping, image-analysis, plants’ growth, agriculturalAbstract
With the rapid development of imaging technology, computing power, and algorithms, computer vision has revolutionized thoroughly plant phenotyping and is now a major tool for phenotypic analysis. Those reasons constructed the base for developing image-based plant phenotyping methods, it is a priority for the complementary or even alternative to the manual measurement. Nonetheless, the use of computer vision technology to analyze plant phenotypic traits can be affected by a lot of factors such as research environment, imaging system, and model selection. The field of plant phenotyping is developing rapidly at the moment. Image-based plant phenotyping has stated proven to be in precision agriculture, providing a quantitative basis for the description of plant-environment interactions.
References
Abdullah, A., Al Enazi, S. & Damaj, I. (2016). AgriSys: A smart and ubiquitous controlled-environment agriculture system. In 2016 3rd mec international conference on big data and smart city (icbdsc) (p. 1-6). doi: https://doi.org/10.1109/ICBDSC.2016.7460386
Castelló Ferrer, E., Rye, J., Brander, G., Savas, T., Chambers, D., England, H. & Harper, C. (2017). Personal Food Computer: A new device for controlled-environment agriculture. MIT Media Lab Open Agriculture Initiative (OpenAg) (), . Retrieved from http://hdl.handle.net/1721.1/110010
FAO. (2016). The state of food and agriculture: Climate change agriculture and food security. Rome, Italy: Food and Agriculture Organization of the United Nations.
Ferrer, E. C., Rye, J., Brander, G., Savas, T., Chambers, D., England, H. & Harper, C. (2018). Personal Food Computer: A New Device for Controlled-Environment Agriculture. In Proceedings of the future technologies conference (FTC) 2018 (p. 1077-1096). Springer International Publishing. doi: https://doi.org/10.1007/978-3-030-02683-7_79
Fox, J. L. (2006). Turning plants into protein factories. Nature Biotechnology 24(10), 1191-1193. doi: https://doi.org/10.1038/nbt1006-1191
Furbank, R. T. & Tester, M. (2011). Phenomics – technologies to relieve the phenotyping bottleneck. Trends in Plant Science 16(12), 635-644. doi: https://doi.org/10.1016/j.tplants.2011.09.005
Harper, C. & Siller, M. (2015). OpenAG: A Globally Distributed Network of Food Computing. IEEE Pervasive Computing 14(4), 24-27. doi: https://doi.org/10.1109/MPRV.2015.72
Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., . . . Ng, A. Y. (2009). ROS: an open-source Robot Operating System. In Icra workshop on open source software (Vol. 3, p. 5).
Zhou, L., Chen, N., Chen, Z. & Xing, C. (2016). ROSCC: An Efficient Remote Sensing Observation-Sharing Method Based on Cloud Computing for Soil Moisture Mapping in Precision Agriculture. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9(12), 5588-5598. doi: https://doi.org/10.1109/JSTARS.2016.2574810
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