Potencialidades de los celulares inteligentes para investigaciones biológicas. Parte 3: Cámaras digitales

Yarelys Ferrer-Sánchez, Fermín L. Felipe, Daryl D. Cruz Flores, Dennis Denis

Resumen


Los modelos modernos de teléfonos inteligentes contienen potentes cámaras digitales con sensores que rivalizan con equipos profesionales pero que, al unirse a sus capacidades de procesamiento y comunicación, amplían el rango de aplicaciones potenciales en el campo de la investigación. En este trabajo se hace una revisión de las potencialidades que brindan estas cámaras y sus aplicaciones a investigaciones biológicas y biomédicas. Entre estas se encuentra que la capacidad de leer y registrar códigos de barras y de respuesta rápida puede ser aprovechada como método de marcaje. Diferentes aplicaciones pueden desarrollar análisis morfométricos avanzados a partir de las imágenes captadas, tanto para la toma de mediciones, análisis de patrones e identificación automatizada de especies o individuos. Se ha desarrollado la microscopía portable a través de dispositivos adjuntos relativamente simples. El sensor de estas provee datos de 8 o 16 bits en los canales de color primario de la luz que permiten su uso en colorimetría, fotometría y espectrometría para aplicaciones bioanalíticas en la detección y evaluación de la concentración de sustancias químicas. Además, en el monitoreo de la calidad del aire, turbidez del agua y para caracterizar el color del suelo. También se han usado en el monitoreo de la radiación ultravioleta. La activación automática por movimiento y la toma de videos permiten su uso en estudios conductuales. Toda esta gama de aplicaciones y potencialidades hacen que el teléfono inteligente se pueda convertir en un importante y versátil instrumento de investigación.

Citación: Ferrer-Sánchez, Y., Felipe, F.L., Cruz, D.D. & Denis, D. 2022. Potencialidades de los celulares inteligentes para investigaciones biológicas. Parte 3: Cámaras digitales. Revista Jard. Bot. Nac. Univ. Habana 43: 15-31.

Recibido: 01 de octubre de 2021. Aceptado: 25 de enero de 2022. Publicado en línea: 20 de abril de 2022. Editor encargado: Luis Manuel Leyva.


Palabras clave


análisis de imágenes; mediciones digitales; sensores CMOS; Sistemas de Identificación Automatizada


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Referencias


Agarwal, A. & Dutta, S. 2018. Assessment of spinach seedling health status and chlorophyll content by multivariate data analysis and multiple linear regression of leaf image features. Comput. Electron. Agric. 152: 281-289. https://doi.org/10.1016/j.compag.2018.06.048

AI Timelines. 2017. AI Impacts. https://aiimpacts.org/recent-trend-in-the-cost-ofcomputing/. 30 de septiembre 2020.

Alafeef, M. & Fraiwan, M. 2020. Smartphone based respiratory rate estimation using photoplethysmographic imaging and discrete wavelet transform. J. Ambient. Intell. Human. Comput. 11: 693-703. https://doi.org/10.1007/s12652-019-01339-6

Aquino, A., Barrio, I., Diago, M.P., Millan, B. & Tardaguila, J. 2018. vitisBerry: An Android-smartphone application to early evaluate the number of grapevine berries by means of image analysis. Comput. Electron. Agric. 148: 19-28. https://doi.org/10.1016/j.compag.2018.02.021

Aquino, A., Millan, B., Gaston, D., Diago, M.P. & Tardaguila, J. 2015. vitisFlower: development and testing of a novel Android-smartphone application for assessing the number of grapevine flowers per inflorescence using artificial vision techniques. Sensors 15: 21204-21218. https://doi.org/10.3390/s150921204

Aydindogan, E., Ceylan, A.E. & Timur, S. 2019. Paper-based colorime- tric spot test utilizing smartphone sensing for detection of biomarkers. Talanta 208: 120446 https://doi.org/10.1016/j.talanta.2019.120446

Bailon, R., Sornmo, L. & Laguna, P. 2006. A robust method for ecg-based estimation of the respiratory frequency during stress testing. IEEE Trans. Biomed. Eng. 53(7): 1273-1285. https://doi.org/10.1109/TBME.2006.871888

Banerjee, S., Hoch, E.G., Kaplan, P.D. & Dumont, E.L. 2017. A comparative study of wearable ultraviolet radiometers. Pp. 9-12. En: 2017 IEEE Life Sciences Conference (LSC). https://doi.org/10.1109/LSC.2017.8268131

Baresel, J.P., Rischbeck, P., Hu, Y., Kipp, S., Hu, Y., Barmeier, G. & Mistele, B. 2017. Use of a digital camera as alternative method for non-destructive detection of the leaf chlorophyll content and the nitrogen nutrition status in wheat. Comput. Electron. Agric. 140: 25-33. https://doi.org/10.1016/j.compag.2017.05.032

Bauer, J., Siegmann, B., Jarmer, T. & Aschenbruck, N. 2016. Smart fLAIr: a smartphone application for fast LAI retrieval using ambient light sensors. Pp. 1-6. En: 2016 IEEE Sensors Applications Symposium (SAS). https://doi.org/10.1109/SAS.2016.7479880

Bergman, T.J. & Beehner, J.C. 2008. A simple method for measuring colour in wild animals: validation and use on chest patch colour in geladas Theropithecus gelada. Biol. J. Linn. Soc. 94: 231-240. https://doi.org/10.1111/j.1095-8312.2008.00981.x

Bolger, D.T., Morrison, T.A., Vance, B., Lee, D. & Farid, H. 2012. A computer-assisted system for photographic mark-recapture analysis. Methods Ecol. Evol. 3: 813-822. https://doi.org/10.1111/j.2041-210X.2012.00212.x

Bonhomme, R. & Chartier, P. 1972. The interpretation and automatica measurement of hemispherical photographs to obtain sunlit foliage area and gap frequency. Israel J. Agric. Res. 22: 53-61.

Brandoli, B., Orue, J.P.M., Arruda, M.S., Santos, C.V., Sarath, D.S., Goncalves, W.N., Silva, G.G., Pistori, H., Railda, A. & Rodrigues Jr, J.F. 2016. BioLeaf: a professional mobile application to measure foliar damage caused by insect herbivory. Comp. Electr. Agric. 129: 44–55. https://doi.org/10.1016/j.compag.2016.09.007

Braz-Sousa, L., Fricker, S.R., Doherty, S.S., Webb, C.E., Baldock, K.L. & Williams, C.R. 2019. Citizen science and smartphone e-entomology enables low-cost upscaling of mosquito surveillance. Sci. Total Env. 704: 135349. https://doi.org/10.1016/j.scitotenv.2019.135349

Calabria, D., Caliceti, C., Zangheri, M., Mirasoli, M., Simoni, P. & Roda, A. 2017. Smartphone based enzymatic biosensor for oral fluid L-lactate detection in one minute using confined multilayer paper reflectometry. Biosens. Bioelectron. 94: 124-130. https://doi.org/10.1016/j.bios.2017.02.053

Canopy Assessment Tool 2017. %Cover. Public Interest Enterprises. AppStore. https://www.appstore.com/. 30 de septiembre 2020.

Carpenter, A.E., Jones, T.R., Lamprecht, M.R., Clarke, C., Kang, I.H. & Friman, O. 2006. Cell profiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7: R100. https://doi.org/10.1186/gb-2006-7-10-r100

Cavallo, D.P., Cefola, M., Pace, B., Logrieco, A.F. & Attolico, G. 2017. Contactless and nondestructive chlorophyll content prediction by random forest regression: A case study on fresh-cut rocket leaves. Comput. Electron. Agric. 140: 303-310. https://doi.org/10.1016/j.compag.2017.06.012

Chanu, O.R., Kapoor, A., Karthik, V., Chanu, O.R., Kapoor, A. & Karthik, V. 2020. Digital image analysis for microfluidic paper based pH sensor platform. Materials Today Proc. 40(1): S64-S68. https://doi.org/10.1016/j.matpr.2020.03.503

Chung, S., Breshears, L.E. & Yoon, J.Y. 2018. Smartphone near infrared monitoring of plant stress. Comput. Electron. Agric. 154: 93-98. https://doi.org/10.1016/j.compag.2018.08.046

Confalonieri, R., Francone, C. & Foi, M. 2014. The PocketLAI smartphone app: an alternative method for leaf area index estimation. En: Ames, D.P., Nigel W., Quinn, T. & Rizzoli, A.E. (Eds.). Proceedings of the 7th International Congress on Environmental Modelling and Software. San Diego, California, USA.

Cretikos, M. A., Bellomo, R., Hillman, K., Chen, J., Finfer, S. & Flabouris, A. 2008. Respiratory rate: the neglected vital sign. Med. J. Aust. 188(11): 657-659. https://doi.org/10.5694/j.1326-5377.2008.tb01825.x

Cruz-Fernández, M., Luque-Cobija, M.J., Cervera, M.L., Morales-Rubio, A. & de la Guardia, M. 2017. Smartphone determination of fat in cured meat products. Microchem. J. 132: 8-14. https://doi.org/10.1016/j.microc.2016.12.020

Daponte, P., De Vito, L., Picariello, F. & Riccio, M. 2013. State of the art and future developments of measurements applications on smartphones. Measurement. 46: 3291-3307. https://doi.org/10.1016/j.measurement.2013.05.006

De Bei, R., Fuentes, S., Gilliham, M., Tyerman, S., Edwards, E., Bian- chini, N., Smith, J. & Collins, C. 2016. VitiCanopy: A free computer App to estimate canopy vigor and porosity for grapevine. Sensors 16: 585. https://doi.org/10.3390/s16040585

Disbury, M., Cane, R.P. & Russell, R.C. 2008. Remote identification of exotic mosquito specimens using digital photography. Austr. J. Entomol. 47(2): 128-130. https://doi.org/10.1111/j.1440-6055.2008.00638.x

Dutta, S., Sibasish, D. & Nath, P. 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

Easlon, H.M. 2016. Canopy Cover Free. Versión 1.0.3. https://play.google.com/store/apps/details?id=com.heaslon.canopycover. 29 de agosto 2020.

Easlon, H.M. & Bloom, A.J. 2014. Easy Leaf Area: automated digital image analysis for rapid and accurate measurement of leaf area. Appl. Plant Sci. 2: 1400033. https://doi.org/10.3732/apps.1400033

Endler, J.A. 1990. On the measurement and classification of color in studies of animal color patterns. Biol. J. Linn. Soc. 41: 315-352. https://doi.org/10.1111/j.1095-8312.1990.tb00839.x

Evans, G.C. & Coombe, D.E. 1959. Hemispherical and woodland canopy photography and the light climate. J. Ecol. 47(1): 103-113. https://doi.org/10.2307/2257250

Falzon, G., Lawson, C., Cheung, K.W., Vernes, K., Ballard, G.A., Fleming, P.J.S., Glen, A.S., Milne, H., Mather-Zardain, A. & Meek, P.D. 2020. ClassifyMe: A Field-Scouting Software for the Identification of Wildlife in Camera Trap Images. Animals 10: 58. https://doi.org/10.3390/ani10010058

Fan, Z., Herrick, J.E., Saltzman, R., Matteis, C., Yudina, A., Nocella, N. & Van Zee, J. 2017. Measurement of soil color: a comparison between smartphone camera and the munsell color charts. Soil Sci. Soc. Am. J. 81(5): 1139-1146. https://doi.org/10.2136/sssaj2017.01.0009

Franco, M.D.O.K., Suarez, W.T., Maja, M.V. & dos Santos, V.B. 2017. Smartphone application for methanol determination in sugar cane spirits employing digital image-based method. Food Anal. Methods 10(6): 2102-2109. https://doi.org/10.1007/s12161-016-0777-y

Fritsch, D. & Syll, M. 2015. Photogrammetric 3D reconstruction using mobile imaging. En: Mobile Devices and Multimedia: Enabling Technologies, Algorithms, and Applications 2015. International Society for Optics and Photonics. 9411: 94110C. https://doi.org/10.1117/12.2083332

Fuentes, S., Poblete-Echeverría, C., Ortega-Farias, S., Tyerman, S. & De Bei, R. 2014. Automated estimation of leaf area index from grapevine canopies using cover photography, video and computational analysis methods. Aust. J. Grape Wine Res. 20: 465-473. https://doi.org/10.1111/ajgw.12098

García, A., Erenas, M.M., Marinetto, E.D., Abad, C.A., de Orbe-Paya, I., Palma, A.J. & Capitán-Vallvey, L.F. 2011. Mobile phone platform as portable chemical analyzer. Sens. Actuators B. Chem. 156: 350-359. https://doi.org/10.1016/j.snb.2011.04.045

Geng, Z., Zhang, X., Fan, Z., Lv, X., Su, Y. & Chen, H. 2017. Recent progress in optical biosensors based on smartphone platforms. Sensors 17(11): 2449. https://doi.org/10.3390/s17112449

Gerald, M.S., Bernstein, J., Hinkson, R. & Fosbury, R.A.E. 2001. Formal method for objective assessment of primate colour. Am. J. Primatol. 53: 79-85. https://doi.org/10.1002/1098-2345(200102)53:2%3C79::AID-AJP3%3E3.0.CO;2-N

Getzin, S., Wiegand, K. & Scheoning, I. 2012. Assessing biodiversity in forests using very high-resolution images and unmanned aerial vehicles. Methods Ecol. Evol. 3: 397-404. https://doi.org/10.1111/j.2041-210X.2011.00158.x

Guan, L., Tian, J., Cao, R., Li, M., Cai, Z. & Shen, W. 2014. Barcode-Like Paper Sensor for Smartphone Diagnostics: An Application of Blood Typing. Anal. Chem. 86: 11362-11367. https://doi.org/10.1021/ac503300y

Guner, H., Ozgur, E., Kokturk, G., Celik, M., Esen, E., Topal, A.E., Ayas, S., Uludag, Y., Elbuken, C. & Dana, A. 2017. A smartphone based surface plasmon resonance imaging (SPRi) platform for on-site biodetection. Sens. Actuators B Chem. 239: 571-577. https://doi.org/10.1016/j.snb.2016.08.061

Hardin, P.J., Lulla, V., Jensen, R.R. & Jensen, J.R. 2018. Small Unmanned Aerial Systems (sUAS) for environmental remote sensing: challenges and opportunities revisited. GIScience & Rem. Sens. 56(2): 309-322. https://doi.org/10.1080/15481603.2018.1510088

Hassanijaliliana, O., Igathinathanea, C., Doetkottb, C., Bajwaa, S., Nowatzkia, J. & Haji, S.A. 2020. Chlorophyll estimation in soybean leaves infield with smartphone digital imaging and machine learning. Comp. Electr. Agric. 174: 105433. https://doi.org/10.1016/j.compag.2020.105433

Hatiboruah, D., Devi, D.Y., Namsa, N.D. & Nath, P. 2020. Turbidimetric analysis of growth kinetics of bacteria in the laboratory environment using smartphone. J. Biophotonics 13: e201960159. https://doi.org/10.1002/jbio.201960159

Hiby, L., Paterson, W.D., Redman, P., Watkins, J., Twiss, S.D. & Pomeroy, P. 2013. Analysis of photo-ID data allowing for missed matches and individuals identified from opposite sides. Methods Ecol. Evol. 4: 252-259. https://doi.org/10.1111/2041-210x.12008

Hussain, I., Das, M., Ahamad, K.U. & Nath, P. 2017. Water salinity detection using a smartphone. Sens. Actuators B Chem. 239: 1042-1050. https://doi.org/10.1016/j.snb.2016.08.102

Igoe, D., Parisi, A.V. & Carter, B. 2013. Characterisation of the UVA response of a smartphone. Photochem. Photobiol. 89: 215-218. https://doi.org/10.1111/j.1751-1097.2012.01216.x

Igoe, D.P., Parisi, A.V., Amar, A. & Rummenie, K.J. 2018. Median filters as a tool to determine dark noise thresholds in high resolution smartphone image sensors for scientific imaging. Rev. Sci. Instr. 89(1): 015003. https://doi.org/10.1063/1.5006000

Iliffe, M., Mwinami, T. & Harper, D. 2011. Counting flamingos with a mobile phone - connecting all the flamingo lakes? Bull. Flamingo Spec. Group 18: 38-41.

ImScope. 2019. Application for Android and An Experimental Tool Oriented to Visualization and Recording of Indicators of Plant Health. https://www.idoneos.com/procesamiento_de_imagenes/imscope/imscope_(english).html. 26 de septiembre 2020.

Johnsen, S. 2016. How to measure color using spectrometers and calibrated photographs. J. Exp. Biol. 219: 772-778. https://doi.org/10.1242/jeb.124008

Jonckheere, I., Fleck, S., Nackaerts, K., Muys, B., Coppin, P., Weiss, M. & Baret, F. 2004. Review of methods for in situ leaf area index determination: Part I. Theories, sensors and hemispherical photo- graphy. Agric. Forest. Meteorol. 121: 19-35. https://doi.org/10.1016/j.agrformet.2003.08.027

Ju, Y.G. 2020. Fabrication of a low-cost and high-resolution papercraft smartphone spectrometer. Phys. Educ. 55: 035005. https://doi.org/10.1088/1361-6552/ab6c3e

Kanakasabapathy, M.K., Sadasivam, M., Singh, A., Preston, C., Thirumalaraju, P., Venkataraman, M., & Shafiee, H. 2017. An automated smartphone-based diagnostic assay for point-of-care semen analysis. Sci. Transl. Med. 9(382): 1-14. https://doi.org/10.1126/scitranslmed.aai7863

Kang, H.G., Song, J-J., Lee, K., Nam, K.C., Hong, S.J. & Kim, H.C. 2016. An investigation of medical radiation detection using CMOS image sensors in smartphones. Nuclear Instr. Methods Phys. Res. Sect. A 823: 126-134. https://doi.org/10.1016/j.nima.2016.04.007

Karcher, D.E. & Richardson, M.D. 2003. Quantifying turfgrass color using digital image analysis. Crop Sci. 43: 943-951. https://doi.org/10.2135/cropsci2003.9430

Kilic¸ V., Alankus, G., Horzum, N., Mutlu A. Y., Bayram, A. & Solmaz, M.E. 2018. Single-image-referenced colorimetric water quality detection using a smartphone. ACS Omega 3(5): 5531-5536. https://doi.org/10.1021/acsomega.8b00625

Klosterman, S.T., Hufkens, K., Gray, J.M., Melaas, E., Sonnentag, O., Lavine, I., Mitchell, L., Norman, R., Friedl, M.A. & Richardson, A.D. 2014. Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences 11: 4305-4320. https://doi.org/10.5194/bg-11-4305-2014

Kolev, K., Tanskanen, P., Speciale, P. & Pollefeys, M. 2014. Turning mobile phones into 3D scanners. Pp. 3946-3953. En: 2014 IEEE Conference on Computer Vision and Pattern Recognition . https://doi.org/10.1109/CVPR.2014.504

Kress, W.J., Garcia-Robledo, C., Soares, J.V.B., Jacobs, D., Wilson, K., Lopez, I.C. & Belhumeur, P.N. 2018. Citizen science and climate change: mapping the range expansions of native and exotic plants with the mobile app Leafsnap. BioScience 68: 348-358. https://doi.org/10.1093/biosci/biy019

Kross, S.M. & Nelson, X.J. 2011. A portable low-cost remote video- graphy system for monitoring wildlife. Methods Ecol. Evol. 2: 191-196. https://doi.org/10.1111/j.2041-210X.2010.00064.x

Kumar, P., Sesham, V.M., Kumar, P., Mandaloju, S.P. & Keesara, S. 2016. A smartphone app for cephalometric analysis. J. Clinical Orthodontics. L(11): 694-699.

Kurniawan, I.S., Tapilow, F.S. & Hidayat, T. 2017. Etho-morphology using smartphone apps to identify aves. J. Phys.: Conf. Ser. 1157: 022088. https://doi.org/10.1088/1742-6596/1157/2/022088

Kwon, O. & Park, T. 2017. Applications of smartphone cameras in agriculture, environment, and food: a review. J. Biosystems Eng. 42(4): 330-338.

Lane, N.D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T. & Campbell, A.T. 2010. A survey of mobile phone sensing. IEEE Comm. Magazine 2010: 140-150. https://doi.org/10.1109/MCOM.2010.5560598

Levin, S., Krishnan, S., Rajkumar, S., Halery, N. & Balkunde, P. 2016 Monitoring of fluoride in water samples using a smartphone. Sci. Total Environ. 551: 101-107. https://doi.org/10.1016/j.scitotenv.2016.01.156

Lia, X., Lia, J., Linga, J., Wanga, C., Dinga, Y., Changa, Y., Wanga, Y. & Caia, J. 2020. A smartphone-based bacteria sensor for rapid and portable identification of forensic saliva sample. Sens. Actuators B Chem.& 320: 128303. https://doi.org/10.1016/j.snb.2020.128303

Liang, Y., Fan, H-W., Fang, Z., Miao, L., Li, W., Zhang, X., Sun, W., Wang, K., He, L. & Chen, X. 2020. OralCam: enabling self-examination and awareness of oral health using a smartphone camera. Pp. 1-13. En: CHI ‘20: CHI Conference on Human Factors in Computing Systems, Honolulu (25-30 abril). https://doi.org/

1145/3313831.3376238

Liu, J., Geng, Z., Fan, Z., Liu, J. & Chen, H. 2019. Point-of-care testing based on smartphone: the current state-of-the-art (2017-2018). Biosens. Bioelectron. 132: 17-37. https://doi.org/10.1016/j.bios.2019.01.068

Ludwig, S.K.J., Tokarski, C., Lang, S.N., van Ginkel, L.A., Zhu, H. & Ozcan, A. 2015. Calling biomarkers in milk using a protein microarray on your smartphone. PLoS One 10: e0134360. https://doi.org/10.1371/journal.pone.0134360

MacLeod, N., Benfield, M. & Culverhouse, P. 2010. Time to automate identification. Nature 467(9): 154-155. https://doi.org/10.1038/467154a

Martinez, A.W., Phillips, S.T., Carrilho, E., Thomas III, S.W., Sindi, H. & Whitesides, G.M. 2008. Simple telemedicine for developing regions: camera phones and paper-based microfluidic devices for real-time, off-site diagnosis, Anal. Chem. 80: 3699-3707. https://doi.org/10.1021/ac800112r

Martinez-Alpiste, I., Casaseca-de-la-Higuera, P., Alcaraz-Calero, J.M., Grecos, C. & Wang, Q. 2019. Smartphone-based object recognition with embedded machine learning intelligence for unmanned aerial vehicles. J. Field Robotics 2019: 1-17.

Martínez-Pérez, B., de la Torre-Díez, I. & López-Coronado, M. 2013. Mobile health applications for the most prevalent conditions by the World health organization: review and analysis, J. Med. Internet Res. 15(2013): e120. https://doi.org/10.2196/jmir.2600

Marzulli, M.I., Raumonen, P., Greco, R., Persia, M. & Tartarino, P. 2020. Estimating tree stem diameters and volume from smartphone photogrammetric point clouds. Forestry. 93: 411-429. https://doi.org/10.1093/forestry/cpz067

McGonigle, A.J.S., Wilkes, T.C., Pering, T.D., Willmott, J.R., Cook, J.M., Mims III, F.M. & Parisi, A.V. 2018. Smartphone spectrometers. Sensors 18: 223. https://doi.org/10.3390/s18010223

McKeon, S., Fenolio, D., Dreelin, R.A., Shaw, D., Kobrinsky, Z. & Meyer, C. 2020. NextGen natural history: New techniques for classical natural history questions. J. Nat. Hist. Education and Experience 14: 6-12.

Mei, Q., Jing, H., Li, Y., Yisibashaer, W., Chen, J., Li, B.N. & Zhang Y. 2016. Smartphone based visual and quantitative assays on upconversional paper sensor. Biosens. Bioelectron. 75: 427-432. https://doi.org/10.1109/JIOT.2017.2717845

Mei, B., Li, R., Cheng, W., Yu, J. & Cheng, X. 2017. Ultraviolet radiation measurement via smart devices. IEEE Internet of Things J. 4(4): 934-944. https://doi.org/10.1109/JIOT.2017.2717845

Mendes, J., Pinho, T.M., dos Santos, F.N., Sousa, J.J., Peres, E., Boaventura-Cunha, J., Cunha, M. & Morais, R. 2020. Smartphone applications targeting precision agriculture practices—a systematic review. Agronomy 10: 855. https://doi.org/10.3390/agronomy10060855

Meredith, D.J., Clifton, D., Charlton, P., Brooks, J., Pugh, C.W. & Tarassenko, L. 2012. Photoplethysmographic derivation of respiratory rate: a review of relevant physiology. J. Med. Eng. Technol. 36(1): 1-7. https://doi.org/10.3109/03091902.2011.638965

Merlin Bird ID. 2018. All About Birds Website. http://merlin.allaboutbirds.org/. 20 de septiembre 2020.

Miller, J., Morgenroth, J. & Gomez, C. 2015. 3D modelling of individual trees using a handheld camera: Accuracy of height, diameter and volume estimates. Urb. Forest. Urb. Greening 14: 932-940. https://doi.org/10.1016/j.ufug.2015.09.001

Montgomery, B.L., Shivas, M.A., Hall-Mendelin, S., Edwards, J., Hamilton, N.A., Jansen, C.C., McMahon, J.L., Warrilow, D. & van den Hurk, A.F. 2017. Rapid surveillance for vector presence (RSVP): development of a novel system for detecting Aedes aegypti and Aedes albopictus. PLoS Neglected Trop. Diseases 11(3): p.e0005505. https://doi.org/10.1371/journal.pntd.0005505

Nasahara, K.N. & Nagai, S. 2015. Review: Development of an in situ observation network for terrestrial ecological remote sensing: the Phenological Eyes Network (PEN). Ecol. Res. 30: 211-223. https://doi.org/10.1007/s11284-014-1239-x

Nijland, W., Bolton, D.K., Coops, N.C. & Stenhouse, G. 2016. Imaging phenology; scaling from camera plots to landscapes. Remote Sens. Environ. 177: 13-20. https://doi.org/10.1016/j.rse.2016.02.018

Nixon, M., Outlaw, F. & Leung, T.S.. 2020. Accurate device-independent colorimetric measurements using smartphones. PLoS ONE 15(3): e0230561. https://doi.org/10.1371/journal.pone.0230561

Nyaga, G.M. 2019. A mobile-based image recognition system for identifying bird species in Kenya. Tesis de Maestría. Facultad de Tecnología de la Información, Universidad Strathmore, Kenya.

Özreçberoğlu, N. & Kahramanoğlu, I. 2020. Mathematical models for the estimation of leaf chlorophyll content based on RGB colours of contact imaging with smartphones: A pomegranate example. Folia Hort. 32(1): 57-67. https://doi.org/10.2478/fhort-2020-0006

Palmer, J.R., Oltra, A., Collantes, F., Delgado, J.A., Lucientes, J., Delacour, S., Bengoa, M., Eritja, R. & Bartumeus, F. 2017. Citizen science provides a reliable and scalable tool to track disease-carrying mosquitoes. Nature Com. 8(1): 916. https://doi.org/10.1038/s41467-017-00914-9

Parisi, A.V., Downs, N., Igoe, D. & Turner, J. 2016. Characterisation of cloud cover with a smartphone camera. Instrum. Sci. Technol. 44: 23-34. https://doi.org/10.1080/10739149.2015.1055577

Park, T.S., Li, W., McCracken, K.E. & Yoon, J.-Y. 2013. Smartphone quantifies Salmonella from paper microfluidics. Lab. Chip 13: 4832-4840. https://doi.org/10.1039/c3lc50976a

Patel, S., Eluri, M., Boyers, L.N., Karimkhani, C. & Dellavalle, R. 2015. Update on mobile applications in dermatology. Dermat. Online J. 21(2): 1-6. https://doi.org/10.5070/D3212023610

Patrício, D.I. & Rieder, R. 2018. Computer vision and artificial intelli- gence in precision agriculture for grain crops: a systematic review. Comput. Electron. Agric. 153: 69-81. https://doi.org/10.1016/j.compag.2018.08.001

Patrignani, A. & Ochsner, T.E. 2015. Canopeo. A powerful new tool for measuring fractional green canopy cover. Agron. J. 107: 2312-2320. https://doi.org/10.2134/agronj15.0150

Pennekamp, F. & Schtickzelle, N. 2013. Implementing image analysis in laboratory-based experimental systems for ecology and evolution: a hands-on guide. Methods Ecol. Evol. 4: 483-492. https://doi.org/10.1111/2041-210X.12036

Pfaender, J., Gratton, L.M., Rosi, T., Malgieri, M. & Onorato, P. 2020. Spectral study of Na source with a smartphone and a diffraction grating. Phys. Educ. 55: 033005. https://doi.org/10.1088/1361-6552/ab7295

Pongnumkul, S., Chaovalit, P. & Surasvadi, N. 2015. Applications of smartphone-based sensors in agriculture: a systematic review of research. J. Sensors 2015: 1-18. https://doi.org/10.1155/2015/

Potash, A.D., Greene, D.U., Foursa, G.A., Mathis, V.L., Conner, L.M. & Mccleery, R.A. 2020. A comparison of animal color measurements using a commercially available digital color sensor and photograph analysis. Current Zool. 2020: 1-6. https://doi.org/10.1093/cz/zoaa016

Prilianti, K.R., Yuwono, S.P., Adhiwibawa, M.A.S., Prihastyanti, M.N.P., Limantara, L. & Brotosudarmo, T.H.P. 2014. Automatic leaf color level determination for need based fertilizer using fuzzy logic on mobile application: A model for soybean leaves. Pp. 1-6. En: 2014 6th International Conference on Information Technology and Electrical Engineering (ICITEE). https://doi.org/10.1109/ICITEED.2014.7007895

Punitha, S., Logeshwari, P., Sivaranjani, P. & Priyanka, S. 2017. Detec- tion of malarial parasite in blood using image processing. KMUTNB. 1(2): 211-213. https://doi.org/10.2139/ssrn.2942420

Rehman, T.U., Mahmud, M.S., Chang, Y.K., Jin, J. & Shin, J. 2019. Current and future applications of statistical machine learning algorithms for agricultural machine vision systems. Comput. Electron. Agric. 156: 585-605. https://doi.org/10.1016/j.compag.2018.12.006

Richardson, A.D., Braswell, B.H., Hollinger, D.Y., Jenkins, J.P. & Ollinger, S.V. 2009. Near-surface remote sensing of spatial and temporal variation in canopy phenology. Ecol. Appl. 19: 1417-1428. https://doi.org/10.1890/08-2022.1

Rigon, J., Capuani, S., Fernandes, D. & Guimarães, T. 2016. A novel method for the estimation of soybean chlorophyll content using a smartphone and image analysis. Photosynthetica 54: 559-566. https://doi.org/10.1007/s11099-016-0214-x

Rodrigues, M., Marques, M.B. & Carvalho, P.S. 2016. How to build a low cost spectrographs with tracker for teaching light spectra. Phys. Educ. 51: 014002. https://doi.org/10.1088/0031-9120/51/1/014002

Rong, Z., Wang, Q., Sun, N., Jia, X., Wang, K. & Xiao, R. 2019. Smartphone-based fluorescent lateral flow immunoassay platform for highly sensitive point-of-care detection of Zika virus nonstructural protein 1. Anal. Chim. Acta 1055(2019): 140-147. https://doi.org/10.1016/j.aca.2018.12.043

Rorie, R.L., Purcell, L.C., Karcher, D.E. & King, C.A. 2011. The assessment of leaf nitrogen in corn from digital images. Crop Sci. 51: 2174-2180. https://doi.org/10.2135/cropsci2010.12.0699

Schrader, J., Pillar, G. & Kreft, H. 2017. Leaf-IT: An Android application for measuring leaf area. Ecol. Evol. 7: 9731-9738. https://doi.org/10.1002/ece3.3485

Shahvar, A., Shamsaei, D. & Saraji, M. 2020. A portable smartphone-based colorimetric sensor for rapid determination of water content in ethanol. Measurement 150(2020): 107068. https://doi.org/10.1016/j.measurement.2019.107068

Siddiqui M.F., Kim, S., Jeon, H., Kim, T., Joo, C. & Park, S. 2018. Miniaturized sample preparation and rapid detection of arsenite in contaminated soil using a smartphone. Sensors 18: 777. https://doi.org/10.3390/s18030777

Skandarajah, A., Sunny, S.P., Gurpur, P., Reber, C.D., D’Ambrosio, M.V., Raghavan, N. & Birur, P. 2017. Mobile microscopy as a screening tool for oral cancer in India: A pilot study. PLoS ONE. 12(11): 1-20. https://doi.org/10.1371/journal.pone.0188440

Snik, F., Jeroen, H.H. Rietjens, Apituley, A., Volten, H., Mijling, B., Di Noia, A., Heikamp, S., Heinsbroek, R.C., Hasekamp, O.P., Smit, J.M., Vonk, J., Stam, D.M., van Harten, G., de Boer, J. & Keller, C.U. 2014. Mapping atmospheric aerosols with a citizen science network of smartphone spectropolarimeters. Geophys. Res. Lett. 41: 7351-7358. https://doi.org/10.1002/2014GL061462

Sonnentag, O., Hufkens, K., Teshera-Sterne, C., Young, A.M., Friedl, M., Braswell, B.H., Milliman, T., O’Keefe, J. & Richardson, A.D. 2012. Digital repeat photography for phenological research in forest ecosystems. Agric. For. Meteorol. 152: 159-177. https://doi.org/10.1016/j.agrformet.2011.09.009

Song, W., Jiang, N., Wang, H. & Vincent, J. 2019. Use of smartphone videos and pattern recognition for food authentication. Sens. Actuators B Chem. 304: 127247. https://doi.org/10.1016/j.snb.2019.127247

Staggemeier, V.G., Camargo, M.G.G., Diniz-Filho, J.A.F., Freckleton, R., Jardim, L., & Morellato, L.P.C. 2020. The circular nature of recurrent life cycle events: a test comparing tropical and temperate phenology. J. Ecol. 108(2): 393-404. https://doi.org/10.1111/1365-2745.13266

Statista, 2020. https://www.statista.com. 10 de septiembre 2020.

Steen, R. 2017. Bird monitoring using the smartphone (iOS) application Videography for motion detection. Bird Study 64(1): 62-69. https://doi.org/10.1080/00063657.2016.1271772

Stevens, M., Párraga, C.A., Cuthill, I.C., Partridge, J.C. & Troscianko, T.S. 2007. Using digital photography to study animal coloration. Biol. J. Linn. Soc. 90: 211-237. https://doi.org/10.1111/j.1095-8312.2007.00725.x

Sumriddetchkajorn, S., Chaitavon, K. & Intaravanne, Y. 2014. Mobile-phone based colorimeter for monitoring chlorine concentrations in water. Sens. Actuators B Chem. 191: 561-566. https://doi.org/10.1016/j.snb.2013.10.024

Suo, X., Liu, Z., Sun, L., Wang, J. & Zhao, Y. 2017. Aphid identification and counting based on smartphone and machine vision. J. Sens. 2017: 1-7. https://doi.org/10.1155/2017/3964376

Tang, J., Körner, C., Muraoka, H., Piao, S., Shen, M., Thackeray, S.J. & Yang, X. 2016. Emerging opportunities and challenges in phenology: a review. Ecosphere 7(8): e01436. https://doi.org/10.1002/ecs2.1436

Tao, M., Ma, X., Huang, X, Liu, C., Deng R., Liang K & Qi, L. 2020. Smartphone-based detection of leaf color levels in rice plants. Comp. Elect. Agric. 173(2020): 105431. https://doi.org/10.1016/j.compag.2020.105431

Teacher, A.G.F., Griffiths, D.J., Hodgson, D.J. & Inger, R. 2013. Smartphones in ecology and evolution: a guide for the app-rehensive. Ecol. Evol. 3(16): 5268-5278. https://doi.org/10.1002/ece3.888

The Telegraph. 2018. The evolution of the mobile phone, from the Motorola DynaTAC to the Samsung Galaxy S9.12 de agosto 2020.

Theuwissen, A. 2008. CMOS image sensors: State-of-the-art. Solid-State Electronics 52: 1401-1406. https://doi.org/10.1016/j.sse.2008.04.012

Thomas, R.L. & Fellowes, M.D.E. 2016. Effectiveness of mobile apps in teaching field-based identification skills. J. Biol. Educ. 9266: 1-8.

Troscianko, J. & Stevens, M. 2015. Image calibration and analysis toolbox: a free software suite for objectively measuring reflectance, colour and pattern. Methods Ecol. Evol. 6: 1320-1331. https://doi.org/10.1111/2041-210X.12439

Turner, J., Igoe, D., Parisi, A.V., McGonigle, A.J., Amar, A. & Wainwright, L. 2019. A review on the ability of smartphones to detect ultraviolet (UV) radiation and their potential to be used in UV research and for public education purposes. Sci. Total Env. 706: 135873. https://doi.org/10.1016/j.scitotenv.2019.135873

Uddling, J., Gelang-Alfredsson, J., Piikki, K. & Pleijel, H. 2007. Eva- luating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings. Photosynth. Res. 91: 37-46. https://doi.org/10.1007/s11120-006-9077-5

Van Horn, G., MacAodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P. & Belongie, S. 2018. Pp. 8769-8778. The iNaturalist species classification and detection dataset. En: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2018.00914

Vashist, S.K., van Oordt, T., Schneider, E.M., Zengerle, R., von Stetten, F. & Luong, J.H.T. 2015. A smartphone-based colorimeter reader for bioanalytical applications using the screen-based bottom illumination provided by gadgets. Biosens. Bioelectron. 67: 248-255. https://doi.org/10.1016/j.bios.2014.08.027

Vesali, F., Omid, M., Kaleita, A. & Mobli, H. 2015. Development of an android app to estimate chlorophyll content of corn leaves based on contact imaging. Comp. Electr. Agric. 116: 211-220. https://doi.org/10.1016/j.compag.2015.06.012

Vollmann, J., Walter, H., Sato, T. & Schweiger, P. 2011. Digital image analysis and chlorophyll metering for phenotyping the effects of nodulation in soybean. Comput. Electron. Agric. 75: 190-195. https://doi.org/10.1016/j.compag.2010.11.003

Von Wettstein, D. 1957. Chlorophyll-letale und der submikroskopis- che formwechsel der plastiden. Exp. Cell. Res. 12: 427-506. https://doi.org/10.1016/0014-4827(57)90165-9

Vrielinga, A., Meroni, B., Darvishzadeh, R., Skidmorea, A.K., Wang, T., Zurita-Milla, R., Oosterbeek, K., O’Connor, B. & Paganini, M. 2018. Vegetation phenology from Sentinel-2 and field cameras for a Dutch barrier island. Remote Sens. Envir. 215: 517-529. https://doi.org/10.1016/j.rse.2018.03.014

Walther, D. & Kampen, H. 2017. The citizen science project ‘Mueckenatlas’ helps monitor the distribution and spread of invasive mosquito species in Germany. J. Med. Entom. 54(6): 1790-1794. https://doi.org/10.1093/jme/tjx166

Wang, L.-J., Chang, Y.-C., Ge, X., Osmanson, A.T., Du, D., Lin, Y. & Lei, L. 2016. Smartphone optisensing platform using a DVD grating to detect neurotoxins. ACS Sens. 1: 366-373. https://doi.org/10.1021/acssensors.5b00204

Wang, Z., Koirala, A., Wals, K., Anderson, A. & Verma, B. 2018. In field fruit sizing using a smartphone application. Sensors 18: 3331. https://doi.org/10.3390/s18103331

Wei, Q., Nagi, R., Sadeghi, K., Feng, S., Yan, E., Jung, K.S., Caire, R., Tseng, D. & Ozcan, A. 2014. Detection and spatial mapping of mercury contamination in water samples using a smartphone. ACS Nano 8: 1121-1129. https://doi.org/10.1021/nn406571t

Wess, T. 2017. Smartphone citizen science: can a conservation hypothesis be tested using non specialist technology? Heritage Sci. 5: 35. https://doi.org/10.1186/s40494-017-0148-z

Wicks, L.C., Cairns, G.S., Melnyk, J., Bryce, S., Duncan, R.R. & Dalgarno, P.A. 2018. EnLightenment: High resolution smartphone microscopy as an educational and public engagement platform. Welcome Open Res. 2: 107. https://doi.org/10.12688/wellcomeopenres.12841.2

Wiechmann, W., Kwan, D., Bokarius, A. & Toohey, S.L. 2016. There’s an app for that? Highlighting the difficulty in finding clinically relevant smartphone applications. West. J. Emerg. Med. 17: 191-194. https://doi.org/10.5811/westjem.2015.12.28781

Witt, C., Pasuquim, J.M.C.A., Mutters, R. & Buresh, R.J. 2005. New leaf color chart for effective nitrogen management in rice. Better Crop 89: 36-39.

Wu, H.P., Li, P., Wang, Y., He, Y.W. & Li, C. 2010. Measurement for opto-electronic conversion functions (OECFs) of digital still-picture camera. En: Proceedings SPIE 7850, Optoelectronic Imaging and Multimedia Technology, Beijing (18-20 octubre), Volumen 7850. https://doi.org/10.1117/12.869364

Xu, M., Liu, R., Chen, J.M., Liu, Y., Shang, R., Ju, W., Wu, C. & Huang, W. 2019. Retrieving leaf chlorophyll content using a matrix-based vegetation index combination approach. Remote Sens. Environ. 224: 60-73. https://doi.org/10.1016/j.rse.2019.01.039

Yang, X., Sun, M., Wang, T., Wong, M.W. & Huang, D. 2018. A smartphone based portable analytical system for on-site quantification of hypochlorite and its scavenging capacity of antioxidants. Sens. Actuators B Chem. 283: 524-531. https://doi.org/10.1016/j.snb.2018.11.131

Yang, X., Wang, Y., Liu, W., Zhang, Y., Zheng, F., Wang, S., Zhang, D. & Wang, J. 2016. A portable system for on-site quantification of formaldehyde in air based on G-quadruplex halves coupled with a smartphone reader. Biosens. Bioelectron. 75: 48-54. https://doi.org/10.1016/j.bios.2015.08.020

Zhang, D. & Liu, Q. 2016. Biosensors and bioelectronics on smartphone for portable biochemical detection. Biosens. Bioelectron. 75: 273-284. https://doi.org/10.1016/j.bios.2015.08.037

Zheng, H., Cheng, T., Li, D., Yao, X., Tian, Y., Cao, W. & Zhu, Y. 2018. Combining unmanned aerial vehicle (UAV)-based multispectral imagery and ground-based hyperspectral data for plant nitrogen concentration estimation in rice. Front. Plant Sci. 9: 936. https://doi.org/10.3389/fpls.2018.00936


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