https://doi.org/10.4081/jae.2020.1082
Real-time assessment of plant photosynthetic pigment contents with an artificial intelligence approach in a mobile application
Published: 21 December 2020
Abstract Views: 2291
PDF: 741
HTML: 164
HTML: 164
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
The assessment of the photosynthetic pigment contents in plants is a common procedure in agricultural studies and can describe plant conditions, such as their nutritional status, response to environmental changes, senescence, disease status and so forth. In this report, we show how the photosynthetic pigment contents in plant leaves can be predicted non-destructively and in real-time with an artificial intelligence approach. Using a convolutional neural network (CNN) model that was embedded in an Androidbased mobile application, a digital image of a leaf was processed to predict the three main photosynthetic pigment contents: chlorophyll, carotenoid and anthocyanin. The data representation, low sample size handling and developmental strategies of the best CNN model are discussed in this report. Our CNN model, photosynthetic pigment prediction network (P3Net), could accurately predict the chlorophyll, carotenoid and anthocyanin contents simultaneously. The prediction error for anthocyanin was ±2.93 mg/g (in the range of 0-345.45 mg/g), that for carotenoid was ±2.14 mg/g (in the range of 0-211.30 mg/g) and that for chlorophyll was ±5.75 mg/g (in the range of 0-892.25 mg/g). This is a promising result as a baseline for the future development of IoT smart devices in precision agriculture.
APHA (American Public Health Association)., 1995. Standard Methods for the Examination of Water and Wastewater, 19th Edition, Washington, D.C.
Capasso, R., Cristinzio, G., Evidente, A., Scognamiglio, F., 1992. Isolation, spectroscopy and selective phytotoxic effects of polyphenols from vegetable waste waters. Phytochemistry, 31, 4125-4128.
DOI: https://doi.org/10.1016/0031-9422(92)80426-F
Caputo, M.C., De Girolamo, A.M., Volpe, A., 2013. Soil amendment with olive mill wastes: Impact on groundwater. J. Environ Manage., 131, 216-221.
DOI: https://doi.org/10.1016/j.jenvman.2013.10.004
Colarieti, M.L., Toscano, G., Greco, G., 2006. Toxicity attenuation of olive mill wastewater in soil slurries. Environ. Chem. Lett. 4, 115-118.
DOI: https://doi.org/10.1007/s10311-006-0050-5
Comegna, A., Coppola, A., Dragonetti, G., Sommella, A., 2013a. Dielectric response of a variable saturated soil contaminated by Non-Aqueous Phase Liquids (NAPLs). Procedia Environmental Sciences, 19, 701-710.
DOI: https://doi.org/10.1016/j.proenv.2013.06.079
Comegna, A., Coppola, A., Dragonetti, G., Severino, G., Sommella A., Basile A., 2013b. Dielectric properties of a tilled sandy volcanic-vesuvian soil with moderate andic features. Soil Till. Res., 133, 93-100.
DOI: https://doi.org/10.1016/j.still.2013.06.003
Comegna, A., Coppola, A., Dragonetti, G., Sommella, A., 2016. Estimating non-aqueous phase liquid (NAPL) content in variable saturated soils using time domain reflectometry (TDR). Vadose zone J., 15, doi:10.2136/vzj2015.11.0145.
DOI: https://doi.org/10.2136/vzj2015.11.0145
Comegna, A., Coppola, A., Dragonetti, G., Severino, G., Sommella, A., 2017. Interpreting TDR Signal Propagation through Soils with Distinct Layers of Nonaqueous-Phase Liquid and Water Content. Vadose Zone J. 16(13). doi:10.2136/vzj2017.07.0141.
DOI: https://doi.org/10.2136/vzj2017.07.0141
Comegna, A., Coppola, A., Dragonetti, G., Sommella, A., 2019. A soil non-aqueous phase liquid (NAPL) flushing laboratory experiment based on measuring the dielectric properties of soil–organic mixtures via time domain reflectometry (TDR). Hydrol. Earth Syst. Sci., 23, 3593-3602, https://doi.org/10.5194/hess-23-3593-2019, 2019.
DOI: https://doi.org/10.5194/hess-23-3593-2019
Dalton, F.N., Herkelrath, W.N, Rawlins, D.S., Rhoades, J.D., 1984. Time-domain reflectometry: Simultaneous measurements of soil water content and electrical conductivity with a single probe. Science, 224, 989-990.
DOI: https://doi.org/10.1126/science.224.4652.989
Francisca, M., Montoro, M.A., 2012. Measuring the dielectric properties of soil-organic mixtures using coaxial impedance dielectric reflectometry, J. Appl. Geophys., 80, 101–109.
DOI: https://doi.org/10.1016/j.jappgeo.2012.01.011
Giese, K., Tiemann, R., 1974. Determination of the complex permittivity from thin-sample time domain reflectometry: Improved analysis of the step response waveform. Adv. Mol. Relaxation Processes, 7, 45-49.
DOI: https://doi.org/10.1016/0001-8716(75)80013-7
Goovaerts, P., AvRuskin, G., Meliker, J., Slotnick, M., Jacquez, G., Nriagu, J., 2005. Geostatistical modeling of the spatial variability of arsenic in groundwater of southeast Michigan. Water Resour. Res., 41, W07013, doi:10.1029/2004WR003705.
DOI: https://doi.org/10.1029/2004WR003705
Haridy, S.A., Persson, M., Berndtsson, R., 2004. Estimation of LNAPL saturation in fine sand using time-domain reflectometry. Hydrological Sciences, 49, 987-1000.
DOI: https://doi.org/10.1623/hysj.49.6.987.55729
Huisman, J.A., Hubbard, S.S., Redman, J.D., and Annan, A.P., 2003. Measuring soil water content with ground penetrating radar: A review. Vadose Zone J., 2, 476-491.
DOI: https://doi.org/10.2113/2.4.476
IUSS Working Group WRB, 2006. World reference base for soil resources 2006: A framework for international classification, correlation and communication. 2nd ed. World Soil Resour. Rep. 103, FAO, Rome.
Kavvadias, V., Doula, M., Theocharopoulosm S., 2014. Long-Term Effects on Soil of the Disposal of Olive Mill Waste Waters (OMW). Environmental Forensic, 15, 37-51.
DOI: https://doi.org/10.1080/15275922.2013.872713
Jung, S., Drnevich V.P., Abou Najm, M.R., 2013. New methodology for density and water content by time domain reflectometry. J. Geotech. Geoenviron. Eng., 139, 659–670. doi:10.1061/(ASCE)GT.1943-5606.0000783.
DOI: https://doi.org/10.1061/(ASCE)GT.1943-5606.0000783
IRSA-CNR, 2003. Metodi analitici per le acque. Volume Primo, pp 781-789.
Isidori, M., Lavorgna, M., Nardelli, A., Parrella, A., 2005. Model study on the effect of 15 phenolic olive mill wastewater constituents on seed germination and vibro fischeri metabolism. J. Agric. Food Chem., 53, 8414-8417.
DOI: https://doi.org/10.1021/jf0511695
Legates, D.R., McCabe Jr, G.J., 1999. Evaluating the use of "goodness-of-fit" measures in hydrologic and hydroclimatic model validation. Water Resour. Res., 35, 233-241.
DOI: https://doi.org/10.1029/1998WR900018
Mekki, A., Dhouib, A., Sayadi, S., 2006. Changes in microbial and soil properties following amendment with treated and untreated olive mill wastewater. Microbiol. Res., 161, 93-101.
DOI: https://doi.org/10.1016/j.micres.2005.06.001
Moroizumi, T., Sasaki, Y., 2006. Estimating the nonaqueous-phase liquid content in saturated sandy soil using amplitude domain reflectometry. Soil Sci. Soc. Am. J., 72, 1520-1526.
DOI: https://doi.org/10.2136/sssaj2006.0212
Or, D., Jones, S.B., VanShaar, J.R., Humphries, S., Koberstein, L., 2004. Win TDR Soil Analysis Software User Guide. Utah State University.
Piotrowska, A., Rao, M.A, Scotti, R., Gianfreda, L., 2011. Changes in soil chemical and biochemical properties following amendment with crude and dephenolized olive mill waste water (OMW). Geoderma, 161, 8-17.
DOI: https://doi.org/10.1016/j.geoderma.2010.11.011
Persson, M., Berndtsson, R., 2002. Measuring nonaqueous phase liquid saturation in soil using time domain reflectometry, Water Resour. Res, 38, doi: 10.1029/2001WR000523.
DOI: https://doi.org/10.1029/2001WR000523
Redman, J.D., De Ryck, S.M., 1994. Monitoring non-aqueous phase liquids in the subsurface with multilevel time domain reflectometry probes. Proc Symp. on Time Domain Reflectometry in Environmental, Infrastructure, and Mining Applications, Evanston, IL. Spec. Publ. SP19-94. U.S. Bur. of Mines, Washington, DC.
Rinaldi, V.A., Francisca, F.M., 2006. Removal of Immiscible Contaminants from Sandy Soils monitored by Means of Dielectric Measurements. J. Environ. Eng., 132, 931-939.
DOI: https://doi.org/10.1061/(ASCE)0733-9372(2006)132:8(931)
Robinson, D.A., Jones, S.B., Wraith, J.M., Or, D., 2003. A review of advances in dielectric and electric conductivity measurements using time domain reflectometry. Vadose Zone J., 2, 444-475.
DOI: https://doi.org/10.2136/vzj2003.4440
Sahraoui, H., Kanzari, S., Hachicha, M., Mellouli, H.J., 2015. Olive Mill Wastewater Spreading Effects On Hydraulic Soil Properties. The experiment, 30, 2002-2011, 2015.
Topp, G.C., Davis, J.L., Annan, A.P., 1980. Electromagnetic determination of soil water content: Measurement in coaxial transmission lines, Water Resour. Res., 16, 574-582.
DOI: https://doi.org/10.1029/WR016i003p00574
How to Cite
Prilianti, K. R. (2020) “Real-time assessment of plant photosynthetic pigment contents with an artificial intelligence approach in a mobile application”, Journal of Agricultural Engineering, 51(4), pp. 220–228. doi: 10.4081/jae.2020.1082.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Yiming Xiao, Jianhua Wang, Hongyi Xiong, Fangjun Xiao, Renhuan Huang, Licong Hong, Bofei Wu, Jinfeng Zhou, Yongbin Long, Yubin Lan, Lychee cultivar fine-grained image classification method based on improved ResNet-34 residual network , Journal of Agricultural Engineering: Vol. 55 No. 3 (2024)
- Francesca Piazzolla, Maria Luisa Amodio, Giancarlo Colelli, The use of hyperspectral imaging in the visible and near infrared region to discriminate between table grapes harvested at different times , Journal of Agricultural Engineering: Vol. 44 No. 2 (2013)
- Giovanni Francesco Ricci, Faouzi Zahi, Ersilia D'Ambrosio, Anna Maria De Girolamo, Giuseppe Parete, Taha-Hocine Debieche, Francesco Gentile, Evaluating flow regime alterations due to point sources in intermittent rivers: A modelling approach , Journal of Agricultural Engineering: Vol. 53 No. 2 (2022)
- Andrea Petroselli, Dario Romerio, Piero Santelli, Roberto Mariotti, Silvano Di Giacinti, Luca Casini, Carmine Testa, Assessing sprinkler systems performance with a novel experimental benchmark , Journal of Agricultural Engineering: Vol. 52 No. 3 (2021)
- Rodolfo Piscopia, Andrea Petroselli, Salvatore Grimaldi, A software package for predicting design-flood hydrographs in small and ungauged basins , Journal of Agricultural Engineering: Vol. 46 No. 2 (2015)
- Marko Milan Kostić, Nataša Ljubičić, Vladimir Aćin, Milan Mirosavljević, Maša Budjen, Miloš Rajković, Nebojša Dedović, An active-optical reflectance sensor in-field testing for the prediction of winter wheat harvest metrics , Journal of Agricultural Engineering: Vol. 55 No. 1 (2024)
- Lei Liu, Xianliang Wang, Xiaokang Zhong, Xiangcai Zhang, Yuanle Geng, Hua Zhou, Tao Chen, Design and experiment of furrow side pick-up soil blade for wheat strip-till planter using the discrete element method , Journal of Agricultural Engineering: Vol. 55 No. 1 (2024)
- Andrea Rosario Proto, Alois Skoupy, Giorgio Macri, Giuseppe Zimbalatti, Time consumption and productivity of a medium size mobile tower yarder in downhill and uphill configurations: a case study in Czech Republic , Journal of Agricultural Engineering: Vol. 47 No. 4 (2016)
- Federico Preti, Tommaso Letterio, Shallow landslide susceptibility assessment in a data-poor region of Guatemala (Comitancillo municipality) , Journal of Agricultural Engineering: Vol. 46 No. 3 (2015)
- Vincenzo Bagarello, Vito Ferro, Dennis Flanagan, Predicting plot soil loss by empirical and process-oriented approaches. A review , Journal of Agricultural Engineering: Vol. 49 No. 1 (2018)
<< < 3 4 5 6 7 8 9 10 11 12 > >>
You may also start an advanced similarity search for this article.
