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Applications of Smartphone Cameras in Agriculture, Environment, and Food: A review

  • Kwon, Ojun (Department of Bio-Industrial Machinery Engineering, Kyungpook National University) ;
  • Park, Tusan (Department of Bio-Industrial Machinery Engineering, Kyungpook National University)
  • Received : 2017.09.26
  • Accepted : 2017.10.19
  • Published : 2017.12.01

Abstract

Purpose: The smartphone is actively being used in many research fields, primarily in medical and diagnostic applications. However, there are cases in which smartphone-based systems have been developed for agriculture, environment, and food applications. The purpose of this review is to summarize the research cases using smartphone cameras in agriculture, environment, and food. Methods: This review introduces seventeen research cases which used smartphone cameras in agriculture, food, water, and soil applications. These were classified as systems involving "smartphone-camera-alone" and "smartphone camera with optical accessories". Results: Detecting food-borne pathogens, analyzing the quality of foods, monitoring water quality and safety, gathering information regarding plant growth or damage, identifying weeds, and measuring soil loss after rain were presented for the smartphone-camera-alone system. Measuring food and water quality and safety, phenotyping seeds, and soil classifications were presented for the smartphone camera with optical accessories. Conclusions: Smartphone cameras were applied in various areas for several purposes. The use of smartphone cameras has advantages regarding high-resolution imaging, manual or auto exposure and focus control, ease of use, portability, image storage, and most importantly, programmability. The studies discussed were achieved by sensitivity improvements of CCDs (charge-coupled devices) and CMOS (complementary metal-oxide-semiconductor) on smartphone cameras and improved computing power of the smartphone, respectively. A smartphone camera-based system can be used with ease, low cost, in near-real-time, and on-site. This review article presents the applications and potential of the smartphone and the smartphone camera used for various purposes in agriculture, environment, and food.

Keywords

References

  1. Abbate, S., M. Avvenuti, F. Bonatesta, G. Cola, P. Corsini, and A. Vecchio. 2012. A smartphone-based fall detection system. Pervasive and Mobile Computing 8:883-899. https://doi.org/10.1016/j.pmcj.2012.08.003
  2. Belz, M., W. J. Boyle, K. F. Klein and K. T. Grattan. 1997. Smart-sensor approach for a fibre-optic-based residual chlorine monitor. Sensors and Actuators B: Chemical 39(1-3):380-385. https://doi.org/10.1016/S0925-4005(97)80238-9
  3. Coskun, A. F., J. Wong, D. Khodadadi, R. Nagi, A. Tey and A. Ozcan. 2013. A personalized food allergen testing platform on a cellphone. Lab on a Chip 13:636-640. https://doi.org/10.1039/C2LC41152K
  4. Cruz-Fernandez, M., M. J. Luque-Cobija, M. L. Cervera, A. Morales-Rubio and M. de la Guardia. 2017. Smartphone determination of fat in cured meat products. Microchemical Journal 132:8-14. https://doi.org/10.1016/j.microc.2016.12.020
  5. Dutta, S., D. Sibasish and P. Nath. 2015. Ground and river water quality monitoring using a smartphone-based pH sensor. AIP Advances 5(5):057151. https://doi.org/10.1063/1.4921835
  6. Franco, M. D. O. K., W. T. Suarez, M. V. Maja and V. B. dos Santos. 2017. Smartphone Application for Methanol Determination in Sugar Cane Spirits Employing Digital Image-Based Method. Food Analytical Methods 10(6):2102-2109. https://doi.org/10.1007/s12161-016-0777-y
  7. Fang, J., X. Qiu, Z. Wan, Q. Zou, K. Su, N. Hu and P. Wang. 2016. A sensing smartphone and its portable accessory for on-site rapid biochemical detection of marine toxins. Analytical Methods 8(38):6895-6902. https://doi.org/10.1039/C6AY01384H
  8. Han, P., D. Dong, X. Zhao, L. Jiao and Y. Lang. 2016. A smartphone-based soil color sensor: For soil type classification. Computers and Electronics in Agriculture 123:232-241. https://doi.org/10.1016/j.compag.2016.02.024
  9. Hernandez-Hernandez, J. L., J. Ruiz-Hernandez, G. Garcia-Mateos, J. M. Gonzalez-Esquiva, A. Ruiz-Canales and J. M. Molina-Martinez. 2017. A new portable application for automatic segmentation of plants in agriculture. Agricultural Water Management 183:146-157. https://doi.org/10.1016/j.agwat.2016.08.013
  10. Hussain, I., M. Das, K. U. Ahamad and P. Nath. 2017. Water salinity detection using a smartphone. Sensors and Actuators B: Chemical 239:1042-1050. https://doi.org/10.1016/j.snb.2016.08.102
  11. Jeong, Y. C. 2016. KISDI STAT Report, 16-06. Available at: www.kisdi.re.kr/kisdi/common/premium?file=1%7C13858 (In Korean).
  12. Li, Z., Z. Li, D. Zhao, F. Wen, J. Jiang and D. Xu. 2017. Smartphone-based visualized microarray detection for multiplexed harmful substances in milk. Biosensors and Bioelectronics 87:874-880. https://doi.org/10.1016/j.bios.2016.09.046
  13. Liang, P . S ., T . S . Park and J . Y. Yoon. 2014. Rapid and reagentless detection of microbial contamination within meat utilizing a smartphone based biosensor. Scientific Reports 4:5953.
  14. Machado, B. B., J. P. Orue, M. S. Arruda, C. V. Santos, D. S. Sarath, W. N. Goncalves, G. G. Silva, H. Pistori, A. R. Roel and J. F. Rodrigues-Jr. 2016. BioLeaf: A professional mobile application to measure foliar damage caused by insect herbivory. Computers and Electronics in Agriculture 129:44-55. https://doi.org/10.1016/j.compag.2016.09.007
  15. Mudanyali, O., S. Dimitrov, U. Sikora, S. Padmanabhan, I. Navruz and A. Ozcan. 2012. Integrated rapid-diagnostictest reader platform on a cellphone. Lab on a Chip 12:2678-2686. https://doi.org/10.1039/c2lc40235a
  16. Oncescu, V., D. O'Dell and D. Erickson. 2013. Smartphone based health accessory for colorimetric detection of biomarkers in sweat and saliva. Lab on a Chip 13:3232-3238. https://doi.org/10.1039/c3lc50431j
  17. Oresko, J. J., Z. Jin, J. Cheng, S. Huang, Y. Sun, H. Duschl and A. C. Cheng. 2010. A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing. IEEE Transactions on information technology in biomedicine 14(3):734-740. https://doi.org/10.1109/TITB.2010.2047865
  18. Park, T. S., W. Li, K. E. McCracken and J. Y. Yoon. 2013. Smartphone quantifies Salmonella from paper microfluidics. Lab on a Chip 13(24):4832-4840. https://doi.org/10.1039/c3lc50976a
  19. Park, T. S. and J. Y. Yoon. 2015. Smartphone detection of Escherichia coli from field water samples on paper microfluidics. IEEE Sensors 15(3):1902-1907. https://doi.org/10.1109/JSEN.2014.2367039
  20. Poushter, J. 2016. Smartphone ownership and internet usage continues to climb in emerging economies. Pew Research Center. Available at: http://www.pewglobal.org/2016/02/22/smartphone-ownership-and-internet-usage-continues-to-climb-in-emerging-economies.
  21. Prosdocimi, M., M. Burguet, S. Di Prima, G. Sofia, E. Terol, J. R. Comino, A. Cerda and P. Tarolli. 2017. Rainfall simulation and Structure-from-Motion photogrammetry for the analysis of soil water erosion in Mediterranean vineyards. Science of the Total Environment 574:204-215. https://doi.org/10.1016/j.scitotenv.2016.09.036
  22. Rahman, M., B. Blackwell, N. Banerjee and D. Saraswat. 2015. Smartphone-based hierarchical crowdsourcing for weed identification. Computers and Electronics in Agriculture 113:14-23. https://doi.org/10.1016/j.compag.2014.12.012
  23. Statistics Korea. 2015. available at: http://kostat.go.kr/portal/korea/kor_nw/2/7/2/index.board?bmode=download&bSeq=&aSeq=356324&ord=7 (In Korean).
  24. Sumriddetchkajorn, S., K. Chaitavon and Y. Intaravanne. 2013. Mobile device-based self-referencing colorimeter for monitoring chlorine concentration in water. Sensors and Actuators B: Chemical 182:592-597. https://doi.org/10.1016/j.snb.2013.03.080
  25. Vesali, F., M. Omid, A. Kaleita and H. Mobli. 2015. Development of an android app to estimate chlorophyll content of corn leaves based on contact imaging. Computers and Electronics in Agriculture 116:211-220. https://doi.org/10.1016/j.compag.2015.06.012
  26. Wang, Y., Y. Li, X. Bao, J. Han, J. Xia, X. Tian and L. Ni. 2016. A smartphone-based colorimetric reader coupled with a remote server for rapid on-site catechols analysis. Talanta 160:194-204. https://doi.org/10.1016/j.talanta.2016.07.012
  27. You, D. J., T. S. Park and J.-Y. Yoon. 2013. Cell-phone-based measurement of TSH using Mie scatter optimized lateral flow assays. Biosensors and Bioelectronics 40:180-185. https://doi.org/10.1016/j.bios.2012.07.014
  28. Yu, L., Z. S hi, C. Fang, Y . Zhang, Y . Liu and C. Li. 2015. Disposable lateral flow-through strip for smartphone-camera to quantitatively detect alkaline phosphatase activity in milk. Biosensors and Bioelectronics 69:307-315. https://doi.org/10.1016/j.bios.2015.02.035
  29. Zhihong, M., M. Yuhan, G. Liang and L. Chengliang. 2016. Smartphone-Based Visual Measurement and Portable Instrumentation for Crop Seed Phenotyping. IFAC-PapersOnLine 49(16):259-264.
  30. Zhu, H., I. Sencan, J. Wong, S. Dimitrov, D. Tseng, K. Nagashima and A. Ozcan. 2013. Cost-effective and rapid blood analysis on a cell-phone. Lab on a Chip 13:1282-1288. https://doi.org/10.1039/c3lc41408f
  31. Zhu, H., U. Sikora and A. Ozcan. 2012. Quantum dot enabled detection of Escherichia coli using a cell-phone. Analyst 137:2541-2544. https://doi.org/10.1039/c2an35071h