Towards an Automated, High-Throughput Identification of the Greenness and Biomass of Rice Crops
Plant phenotyping is a vital process that helps farmers and researchers assess the growth, health, and development of a plant. In the Philippines, phenotyping is done manually, with each plant specimen measured and assessed one by one. However, this process is laborious, time-consuming, and prone to human error. Automated phenotyping systems have attempted to address this problem through the use of cameras and image processing, but these systems are proprietary and designed for plants and crops which are not commonly found in the Philippines. In order to alleviate this problem, research was conducted to develop an automated, high-throughput phenotyping system that automates the identification of plant greenness and plant biomass of rice. The system was developed in order to provide an efficient way of phenotyping rice by automating the process. It implements various image processing techniques and was tested in a screen house setup containing numerous rice variants. The system’s design was finalized in consultation with and tested by rice researchers. The respondents were pleased with the system’s usability and remarked that it would be beneficial to their current process if used. To evaluate the system’s accuracy, the generated greenness and biomass values were compared with the values obtained through the manual process. The greenness module registered a 21.9792% mean percent error in comparison to using the Leaf Color Chart. On the other hand, the biomass module yielded 206.0700% mean percent error using compressed girth measurements.
KeywordsAutomated phenotyping Image processing Greenness Biomass Rice Research optimization
The authors would like to thank Mr. Briane Samson, Dr. Florante Salvador and Dr. Joel Ilao from the College of Computer Studies of De La Salle University for their guidance throughout the duration of writing the research. They would also like to thank Mr. Alexis Pantola, for providing the Internet Protocol camera used in the development and testing of the system. Finally, they would also like extend their gratitude to the International Rice Research Institute especially C4 Rice Researcher Mr. Albert De Luna for working with us through consultations about plant phenotyping and the C4 Rice project, as well as giving feedback in the development of the Luntian system.
- 1.Helmert, M., and H. Lasinger. 2010. The scanalyzer domain: Greenhouse logistics as a planning problem. In International Conference on Automated Planning and Scheduling, May 2010.Google Scholar
- 2.Finkel, E. 2009. With ‘Phenomics,’ plant scientists hope to shift breeding into overdrive. Science 325 (5939): 380–381.Google Scholar
- 3.Eberius, M. 2014. Lemnatec HTS bonit: Image analysis for the quantification of rice in 2-D and 3-D assays. Lemnatec.Google Scholar
- 4.Tsaftaris, S., and C. Noustos. 2009. Plant phenotyping with low cost digital and image analytics. In Information Technologies in Environmental Engineering: Proceedings of the 4th International ICSC Symposium, vol. 4, p 239.Google Scholar
- 5.Granier, C., L. Aguirrezabal, K. Chenu, S.J. Cookson, M. Dauzat, P. Hamard, J.-J. Thioux, G. Rolland, S. Bouchier-Combaud, A. Lebaudy, B. Muller, T. Simonneau, and F. Tardieu. 2006. Phenopsis, an automated platform for reproducible phenotyping of plant responses to soil water deficit in arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit. New Phytologist 169 (3): 623–635.Google Scholar
- 6.Hartmann, A., T. Czauderna, R. Hoffman, N. Stein, and F. Schreiber. 2011. Htpheno: An image analysis pipeline for high-throughput plant phenotyping. BMC Bioinformatics 12 (1): 148.Google Scholar
- 7.Arvidsson, S., P. Perez-Rodriguez, and B. Mueller-Roeber. 2011. A growth phenotyping pipeline for arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects. New Phytologist 191 (3): 895–907.Google Scholar