Review: Teknologi Simple Phenotyping sebagai Database Pengembangan Robot Pendeteksi dan Pemupuk Nitrogen Padi

Mohammad Syafii, M. Haidar Rozik, Airlanggia Febi Torimania, Janan Nabilah Nur Indriana

Abstract

Rice is one of the most important staple food consumed by more than half of the world population, especially in Indonesia where majority of the people consumed rice as daily intake. Precision agriculture is needed to boost production to meet the demand. As limiting factor for plant growth, huge doses of nitrogen fertilizer are deployed, in fact the absorbtion rate of plant was only about 50%. Developing vision-control based robot for fertilizing plant need proper database so that false positive and negative could be avoided. Several previous methods that were available to assest nitrogen status in plant such as a color chart, spectrometer, SPAD-502, and kjedahl. Plant phenotyping is often used for plant breeding, but also very useful in determining nutrient status in plants. Cheap and simple phenotyping using digital camera followed by extracting data through open source platform such as ImageJ can be very useful to generate data for building the database. Here we presented the current progress on plant phenotyping for nitrogen assessment in plant as well as an ImageJ in phenomic era. We also highlight some robotic progress especially vision-based robot, who rely on proper imaging data for their training.

Keywords

image-J, camera digital, phenomics, robotik

References

Abu RLA, Basri Z, Made U. (2017). Respon pertumbuhan dan hasil tanaman padi (Oryza sativa L.) terhadap kebutuhan nitrogen menggunakan bagan warna daun. J. Agroland. 24(2): 119-127.

Ata-Ul-Karim, Cao Q, Zhu Y, Tang L, Rehmani MIA, Cao W. (2016). Non- destructive assesment of plant parameters using leaf chlorophyll measurements in rice. Front. Plant Sci. 7(1829): 1-14.

Adhikari C, Bronson KF, Panuallah GM, Regmi AP, Saha PK, Dobermann A, Olk DC, Hobbs PR, Pasuquin E. (1999). On-farm soil N supply and N nutrition in the rice–wheat system of Nepal and Bangladesh. Field Crops Res. 64:273–286.

Baresel JP, Rischbeck P, Hu Y, Kipp S, Hu Y, Barmeier G, Mistele B. (2017). Use of digital camera as alternative methods for nondestructive detection leaf chlorophyll. Computers and electronics in agriculture. 140: 25-33.

Bloch SE, Ryu M-H, Ozaydin B, Broglie R. (2020). Harnessing atmospheric nitrogen for cereal crop production. Current Opinion in Biotechnology. 62: 181-188.

BPS. (2013). Proyeksi Penduduk Indonesia 2010-2015. Jakarta. INA: Badan Pusat Statistik.

BPS. (2017). Kajian Konsumsi Bahan Pokok 2017. Jakarta. INA: Badan Pusat Statistik.

BPS. (2018). Luas Panen dan Produksi Beras 2018. Jakarta: Badan Pusat Statistik.

Chawade A, van Ham J, Blomquist H, Bagge O, Alexandersson E, Ortiz R. (2019). High-throughput field-phenotyping tools for plant breeding and precision agriculture. Agronomy. 9(258): 1-18.

Cisternas I, Velasquez I, Caro A, Rodriguez A. (2020). Systematic literature review of implementations of precision agriculture. Computers and Electronics in Agriculture. 176(105626): https://doi.org/10.1016/j.compag.2020.105626

Coskun S, Britto DT, Shi W, Kronzucker J. (2017). Nitrogen Transformation in Modern Agriculture and the role of Biological Nitrification Inhibition. Nature Plants. 3(17074): 1-10.

Costa C, Schurr U, Loreto F, Menesatti P, Carpentier S. (2019). Plant Phenotyping Research Trends, a Science Mapping Approach. Front. Plant. Sci.9(1933): 1-11.

Dimkpa, C. O., Fugice, J., Singh, U., & Lewis, T. D. (2020). Development of fertilizers for enhanced nitrogen use efficiency – Trends and perspectives. Science of The Total Environment, 139113.doi:10.1016/j.scitotenv.2020.139113

Fageria NK, Baligar VC, Jones CA. (2011). Growth and Mineral Nutrition of Field Crops. 3rd edition. Boca Raton: CRC Press.

Herrit MT, Long JC, Roybal MD, Moller Jr DC, Mockler TC, Pauli D, Thompson AL. (2021). FLIP: FLuorescence Imaging Pipline for field-based chlorophyll fluorescence images. SoftwareX. 14. https://doi.org/10.1016/j.softx.2021.100685

Hickey LT, Hafeez AN, Robinson H, Jackson SA, Leal-Bertioli SCM, Tester M, Gao C, Godwin ID, Hayes BJ, Wulff BBH. (2019). Breeding crops to feed 10 billion. Nature Biotechnology. 37: 744-754.

Kefauver SC, Vicente R, Vergara-Diaz O, Fernandez-Gallego JA, Kerfal S, Lopez A, Melichar JPE, Molins MDS, Araus JL. (2017). Comparative UAV and Field Phenotyping to Assess Yield and Nitrogen Use Efficiency in Hybrid and Conventional Barley. Front. Plant Sci. 8(1733): 1-15.

Khan MS, Koizumi N, Olds JL. (2019). Biofixation of atmospheric nitrogen in the context of world staple crop production: policy perspectives. Science of the Total Environment. https://doi: https://doi.org/10.1016/j.scitotenv.2019.134945

Lakshmi S, Sivakumar R. (2019). Plant Phenotyping Through Image Analysis Using Nature Inspired Optimization Techniques. dalam Hemanth J, Balas VE (editor). Springer. UK.

Li N, Zhang X, Zhang C, Ge L, He Y, Wu X. (2019). Review of Machine-Vision- Based Plant Detection Technologies for Robotic Weeding. Proceeding of the IEEE International Conference on Robotics and Biomimetics. Dali, China. Desember 2019.

Li Y, Li Y, Zhang H, Wang M, Chen S. (2019). Diazotrophic Paenibacillus beijingensis BJ-18 provides nitrogen for plant and promotes plant growth, nitrogen uptake and metabolism. Front. Microbiol. 10(1119): 1-18.

Mahmud K, Makaju S, Ibrahim R, Missaoui A. (2020). Current progress in nitrogen fixing plants and microbiome research. Plant. 9(97): 1-17.

Mikula, K., Izydorczyk, G., Skrzypczak, D., Mironiuk, M., Moustakas, K., Witek-Krowiak, A., & Chojnacka, K. (2019). Controlled release micronutrient fertilizers for precision agriculture – A review. Science of The Total Environment, 136365. https://doi.10.1016/j.scitotenv.2019.136365

Milagres CdC, Fontes PCR, de Abreu JAA, da Silva JM, de Figueiredo MN. (2021). Plant growth stage and leaf part to diagnose sweet corn nitrogen status using chlorophyll sensor and scanner image analysis. Journal of Plant Nutrition. https://doi.org/10.1080/01904167.2021.1921197.

Monostori I, Arendas T, Hoffman B, Galiba G, Gierzik K, Szira F. (2016). Relationship between SPAD value and grain yield can be affected by cultivar, environment and soil nitrogen content in wheat. Euphytica. 211(1): 103-112.

Munoz-Huerta RF, Guevara-Gonzalez RG, Contreras-Medina LM, Torres- Pacheco I, Prado-Olivarez J, Ocampo-Valezquez. (2013). A Review of methods fo sensing nitrogen status in plants: Advantages, disadvantages and recent advances. Sensors. 13(8): 10823-10843.

Nag P, Shriti S, Das S. (2019). Microbiological strategies for enhancing biological nitrogen fixation in nonlegumes. Journal of Applied Microbiology. 1883: 1-13.

Polder G, Blokker G, van der Heijden GWAM. (2012). An ImageJ plugin for plant variety testing. Proceedings of The ImageJ User and Developer Conference 2012. 22-26 Oktober 2012, Luxembourg. pp. 168 173.

Pratab A, Gupta S, Nair RM, Gupta SK, Schafleitner R, Basu PS, Singh CM, Prajapati U, Gupta AK, Nayyar H, Mishra AK, Baek K. (2019). Using plant phenomics to exploit the gains of genomics. Agronomy. 9(3): 1-25.

Putra BTW, Soni P. (2017). Enhanced broadband greenness in assesing chlorophyll a and b, carotenoid, and nitrogen in robusta coffe plantations using a digital camera. Precision Agric. DOI 10.1007/S11119-017-9513-x.

Putra BTW, Soni P. (2018). Dataset of chlorophyll content estimation of coffea canepora using red and near infra-red consumer grade camera. Data in Brief. 21: 736-741.

Roitsch T, Cabrera-Bosquet L, Fournier A, Ghamkhar K, Jiménez-Berni J, Pinto F, Ober ES. (2019). Review: new sensors and data-driven approaches-a path to the next generation phenomics. Plant Sciences. 282:2-10.

Rueden CT, Eliceiri KW. (2019). ImageJ for the next generation image data. Microsc. Microanal. 25(2): 142-143.

Schindelin J, Rueden CT, Hiner MC, Eliceiri KW. (2015). The ImageJ ecosystem: an open platform for biomedical image analysis. Mol. Reprod. Dev. 82: 518-529.

Soucek J, Prazan R, Velebil J. (2019). Effect of nitrogen fertilization on the color of wheat leaves as an indicator of application deficiency. 7th TAE. 17-20 September 2019. Prague, Czech Republic.pp.524-528.

Varinderpal-Singh, Bijay-Singh, Yavinder-Singh, Thind HS, Gupta RK. (2010). Need based nitrogen management using the chlorophyll meter and leaf colour chart in rice and wheat in South Asia: A review. Nutr Cycl Agroecosyst. 88: 361-380.

Walter A, Liebisch F, Hund A. (2015). Plant phenotyping: From bean weighing to image analysis. Plant Methods. 11(14): 1-11.

DOI

https://doi.org/10.21107/rekayasa.v14i2.10709

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