Discrimination of Tomato Plants (Solanum lycopersicum) Grown under Anaerobic Baffled Reactor Effluent, Nitrified Urine Concentrates and Commercial Hydroponic Fertilizer Regimes Using Simulated Sensor Spectral Settings
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Set-Up
2.2. Remotely Sensed Data
2.3. Discriminating Tomato Plants Grown under Different Fertilizer Regimes
2.4. Classification Accuracy Assessment
3. Results
3.1. Discriminating Tomatoes under ABR Effluent, NUC and CHFM
3.2. Performance of DA and PLS-DA Algorithms in Discriminating Tomatoes under ABR Effluent, NUC and CHFM
4. Discussion
Implications of the Study’S Findings for Horticultural Crop Production
5. Conclusions
- The forthcoming HyspIRI sensor has the potential to accurately map tomato crops under various fertilizer regimes. Landsat and Sentinel, performed comparably to HyspIRI spectral settings.
- Overall, all the sensors were able to characterise the comparable impact of HEDM in relation to that of CHFM fertilizers on the spectral characteristics of tomato plants as a proxy of their health.
- DA offers optimal accuracies in characterizing tomatoes grown under different fertilizer regimes when compared to PLS-DA.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Mabhaudhi, T.; Chibarabada, T.; Modi, A. Water-Food-Nutrition-Health Nexus: Linking Water to Improving Food, Nutrition and Health in Sub-Saharan Africa. Int. J. Environ. Res. Public Health 2016, 13, 107. [Google Scholar] [CrossRef]
- Van Ittersum, M.K.; Van Bussel, L.G.; Wolf, J.; Grassini, P.; Van Wart, J.; Guilpart, N.; Claessens, L.; de Groot, H.; Wiebe, K.; Mason-D’Croz, D.; et al. Can Sub-Saharan Africa Feed Itself? Proc. Natl. Acad. Sci. USA 2016, 113, 14964–14969. [Google Scholar] [CrossRef] [PubMed]
- The-World-Bank. Poverty & Equity Data Portal; The World Bank: Wastington, DC, USA, 2019. [Google Scholar]
- FAO. Inflation in Consumer Price Index for Food; FAO: Rome, Italy, 2019. [Google Scholar]
- Conceição, P.; Levine, S.; Lipton, M.; Warren-Rodríguez, A. Toward a Food Secure Future: Ensuring Food Security for Sustainable Human Development in Sub-Saharan Africa. Food Policy 2016, 60, 1–9. [Google Scholar] [CrossRef]
- Nordey, T.; Basset-Mens, C.; De Bon, H.; Martin, T.; Déletré, E.; Simon, S.; Parrot, L.; Despretz, H.; Huat, J.; Biard, Y.; et al. Protected Cultivation of Vegetable Crops in Sub-Saharan Africa: Limits and Prospects for Smallholders. A Review. Agron. Sustain. Dev. 2017, 37, 53. [Google Scholar] [CrossRef]
- Beecher, G.R. Nutrient Content of Tomatoes and Tomato Products. Proc. Soc. Exp. Biol. Med. 1998, 218, 98–100. [Google Scholar] [CrossRef]
- Busari, T.I.; Senzanje, A.; Odindo, A.O.; Buckley, C.A. Evaluating the Effect of Irrigation Water Management Techniques on (Taro) Madumbe (Colocasia Esculenta (L.) Schott) Grown with Anaerobic Filter (Af) Effluent at Newlands, South Africa. J. Water Reuse Desalin. 2019, 9, 203–212. [Google Scholar] [CrossRef]
- Smith, D.P.; Smith, N.T. Recovery of Wastewater Nitrogen for Solanum Lycopersicum Propagation. Waste Biomass Valorization 2019, 10, 1192–1202. [Google Scholar] [CrossRef]
- Al-Hamdan, M.; Cruise, J.; Rickman, D.; Quattrochi, D. Forest Stand Size-Species Models Using Spatial Analyses of Remotely Sensed Data. Remote Sens. 2014, 6, 9802–9828. [Google Scholar] [CrossRef] [Green Version]
- Petropoulos, G.; Srivastava, P.; Piles, M.; Pearson, S. Earth Observation-Based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management. Sustainability 2018, 10, 181. [Google Scholar] [CrossRef]
- Peng, Y.; Nguy-Robertson, A.; Arkebauer, T.; Gitelson, A. Assessment of Canopy Chlorophyll Content Retrieval in Maize and Soybean: Implications of Hysteresis on the Development of Generic Algorithms. Remote Sens. 2017, 9, 226. [Google Scholar] [CrossRef]
- Nguy-Robertson, A.L.; Peng, Y.; Gitelson, A.A.; Arkebauer, T.J.; Pimstein, A.; Herrmann, I.; Karnieli, A.; Rundquist, D.C.; Bonfil, D.J. Estimating Green Lai in Four Crops: Potential of Determining Optimal Spectral Bands for a Universal Algorithm. Agric. For. Meteorol. 2014, 192, 140–148. [Google Scholar] [CrossRef]
- Luo, S.; Wang, C.; Xi, X.; Nie, S.; Fan, X.; Chen, H.; Yang, X.; Peng, D.; Lin, Y.; Zhou, G. Combining Hyperspectral Imagery and Lidar Pseudo-Waveform for Predicting Crop Lai, Canopy Height and above-Ground Biomass. Ecol. Indic. 2019, 102, 801–812. [Google Scholar] [CrossRef]
- Zhao, T.; Koumis, A.; Niu, H.; Wang, D.; Chen, Y. Onion Irrigation Treatment Inference Using a Low-Cost Hyperspectral Scanner. In Proceedings of the Paper Presented at the Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VII, Honolulu, HI, USA, 24–26 September 2018. [Google Scholar]
- Lu, J.; Ehsani, R.; Shi, Y.; de Castro, A.I.; Wang, S. Detection of Multi-Tomato Leaf Diseases (Late Blight, Target and Bacterial Spots) in Different Stages by Using a Spectral-Based Sensor. Sci. Rep. 2018, 8, 2793. [Google Scholar] [CrossRef] [PubMed]
- Česonienė, L.; Masaitis, G.; Mozgeris, G.; Gadal, S.; Šileikienė, D.; Karklelienė, R. Visible and near-Infrared Hyperspectral Imaging to Describe Properties of Conventionally and Organically Grown Carrots. J. Elem. 2019, 24, 421–435. [Google Scholar]
- Transon, J.; d’Andrimont, R.; Maugnard, A.; Defourny, P. Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context. Remote Sens. 2018, 10, 157. [Google Scholar] [CrossRef]
- Korhonen, L.; Packalen, P.; Rautiainen, M. Comparison of Sentinel-2 and Landsat 8 in the Estimation of Boreal Forest Canopy Cover and Leaf Area Index. Remote Sens. Environ. 2017, 195, 259–274. [Google Scholar] [CrossRef]
- Dube, T.; Mutanga, O. Evaluating the Utility of the Medium-Spatial Resolution Landsat 8 Multispectral Sensor in Quantifying Aboveground Biomass in Umgeni Catchment, South Africa. ISPRS J. Photogramm. Remote Sens. 2015, 101, 36–46. [Google Scholar] [CrossRef]
- Ahmadian, N.; Ghasemi, S.; Wigneron, J.P.; Zölitz, R. Comprehensive Study of the Biophysical Parameters of Agricultural Crops Based on Assessing Landsat 8 Oli and Landsat 7 Etm+ Vegetation Indices. GISci. Remote Sens. 2016, 53, 337–359. [Google Scholar] [CrossRef]
- Shoko, C.; Mutanga, O. Examining the Strength of the Newly-Launched Sentinel 2 Msi Sensor in Detecting and Discriminating Subtle Differences between C3 and C4 Grass Species. ISPRS J. Photogramm. Remote Sens. 2017, 129, 32–40. [Google Scholar] [CrossRef]
- Guanter, L.; Kaufmann, H.; Segl, K.; Foerster, S.; Rogass, C.; Chabrillat, S.; Kuester, T.; Hollstein, A.; Rossner, G.; Chlebek, C.; et al. The Enmap Spaceborne Imaging Spectroscopy Mission for Earth Observation. Remote Sens. 2015, 7, 8830–8857. [Google Scholar] [CrossRef]
- Lee, C.M.; Cable, M.L.; Hook, S.J.; Green, R.O.; Ustin, S.L.; Mandl, D.J.; Middleton, E.M. An Introduction to the Nasa Hyperspectral Infrared Imager (Hyspiri) Mission and Preparatory Activities. Remote Sens. Environ. 2015, 167, 6–19. [Google Scholar] [CrossRef]
- Sheik, O.M.; Kabir, P.; Ilaria, G.; Onisimo, M.; Zakariyyaa, O. Detecting Canopy Damage Caused by Uromycladium Acaciae on South African Black Wattle Forest Compartments Using Moderate Resolution Satellite Imagery. S. Afr. J. Geomat. 2019, 8, 69–83. [Google Scholar]
- Huang, H.C.; Liu, S.C.; Wang, C.; Xia, K.B.; Zhang, D.J.; Wang, H.Z.; Zhan, S.Y.; Huang, H.; He, S.Y.; Liu, C.C. On-Site Visualized Classification of Transparent Hazards and Noxious Substances on a Water Surface by Multispectral Techniques. Appl. Opt. 2019, 58, 4458–4466. [Google Scholar] [CrossRef] [PubMed]
- Li, X.Y.; Zhang, F.L.; Jane, Y. Locally Weighted Discriminant Analysis for Hyperspectral Image Classification. Remote Sens. 2019, 11, 109. [Google Scholar] [CrossRef]
- Corbane, C.; Alleaume, S.; Deshayes, M. Mapping Natural Habitats Using Remote Sensing and Sparse Partial Least Square Discriminant Analysis. Int. J. Remote Sens. 2013, 34, 7625–7647. [Google Scholar] [CrossRef]
- Boulesteix, A.L. Pls Dimension Reduction for Classification with Microarray Data. Stat. Appl. Genet. Mol. Biol. 2004, 3, 1–32. [Google Scholar] [CrossRef] [PubMed]
- Bagheri, N.; Mohamadi-Monavar, H.; Azizi, A.; Ghasemi, A. Detection of Fire Blight Disease in Pear Trees by Hyperspectral Data. Eur. J. Remote Sens. 2018, 50, 1–10. [Google Scholar] [CrossRef]
- Prats-Montalbán, J.M.; Ferrer, A.; Malo, J.L.; Gorbena, J. A Comparison of Different Discriminant Analysis Techniques in a Steel Industry Welding Process. Chemom. Intell. Lab. Syst. 2006, 80, 109–119. [Google Scholar] [CrossRef]
- Julia, A. Colour Correction of Underwater Images Using Spectral Data. Ph.D. Thesis, Acta Universitatis Upsaliensis, Uppsala, Sweden, 2005. [Google Scholar]
- Springsteen, A. Standards for Reflectance Measurements. Appl. Spectrosc. A Compact. Ref. Pract. 1998, 15, 247. [Google Scholar]
- Abdel-Rahman, E.M.; Mutanga, O.; Odindi, J.; Adam, E.; Odindo, A.; Ismail, R. A Comparison of Partial Least Squares (Pls) and Sparse Pls Regressions for Predicting Yield of Swiss Chard Grown under Different Irrigation Water Sources Using Hyperspectral Data. Comput. Electron. Agric. 2014, 106, 11–19. [Google Scholar] [CrossRef]
- Curran, P.J. Imaging Spectrqmetry-Its Present and Future Rôle in Environmental Research. In Imaging Spectrometry—A Tool for Environmental Observations; Springer: Berlin, Germany, 1994; pp. 1–23. [Google Scholar]
- Prasad, S.; Lori, M.B.; Hemanth, K. Data Exploitation of Hyspiri Observations for Precision Vegetation Mapping. In Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa, 12–17 July 2009. [Google Scholar]
- Sathishkumar, S.; Prasad, S.; Bruce, M.L.; Robles, W. Nasa’s Upcoming Hyspiri Mission—Precision Vegetation Mapping with Limited Ground Truth. In Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA, 25–30 July 2010. [Google Scholar]
- Adelabu, S.; Mutanga, O.; Adam, E.; Sebego, R. Spectral Discrimination of Insect Defoliation Levels in Mopane Woodland Using Hyperspectral Data. Selected Topics in Applied Earth Observations and Remote Sensing. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 7, 177–186. [Google Scholar] [CrossRef]
- Zhang, H.; Lan, Y.; Suh, C.P.; Westbrook, J.K.; Lacey, R.; Hoffmann, W.C. Differentiation of Cotton from Other Crops at Different Growth Stages Using Spectral Properties and Discriminant Analysis. Trans. Am. Soc. Agric. Biol. Eng. 2012, 55, 1623–1630. [Google Scholar] [CrossRef]
- Manandhar, R.; Odeh, I.; Ancev, T. Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data Using Post-Classification Enhancement. Remote Sens. 2009, 1, 330–344. [Google Scholar] [CrossRef] [Green Version]
- de Leeuw, J.; Jia, H.; Yang, L.; Liu, X.; Schmidt, K.; Skidmore, A.K. Comparing Accuracy Assessments to Infer Superiority of Image Classification Methods. Int. J. Remote Sens. 2006, 27, 223–232. [Google Scholar] [CrossRef]
- Pedrero, F.; Kalavrouziotis, I.; Alarcón, J.J.; Koukoulakis, P.; Asano, T. Use of Treated Municipal Wastewater in Irrigated Agriculture—Review of Some Practices in Spain and Greece. Agric. Water Manag. 2010, 97, 1233–1241. [Google Scholar] [CrossRef]
- Maurer, M.A.; Davies, F.S.; Graetz, D.A. Reclaimed Wastewater Irrigation and Fertilization of Matureredblush’grapefruit Trees on Spodosols in Florida. J. Am. Soc. Hortic. Sci. 1995, 120, 394–402. [Google Scholar] [CrossRef]
- Zavadil, J. The Effect of Municipal Wastewater Irrigation on the Yield and Quality of Vegetables and Crops. Soil Water Res. 2009, 4, 91–103. [Google Scholar] [CrossRef]
- Al-Lahham, O.; El Assi, N.M.; Fayyad, M. Impact of Treated Wastewater Irrigation on Quality Attributes and Contamination of Tomato Fruit. Agric. Water Manag. 2003, 61, 51–62. [Google Scholar] [CrossRef]
- Viskari, E.L.; Grobler, G.; Karimäki, K.; Gorbatova, A.; Vilpas, R.; Lehtoranta, S. Nitrogen Recovery with Source Separation of Human Urine–Preliminary Results of Its Fertiliser Potential and Use in Agriculture. Front. Sustain. Food Syst. 2018, 2, 32. [Google Scholar] [CrossRef]
- Moya, B.; Parker, A.; Sakrabani, R. Challenges to the Use of Fertilisers Derived from Human Excreta: The Case of Vegetable Exports from Kenya to Europe and Influence of Certification Systems. Food Policy 2019, 85, 72–78. [Google Scholar] [CrossRef]
- Moya, B.; Parker, R.S.; Mesa, B. Evaluating the Efficacy of Fertilisers Derived from Human Excreta in Agriculture and Their Perception in Antananarivo, Madagascar. Waste Biomass Valorization 2019, 10, 941–952. [Google Scholar] [CrossRef]
- Bassegio, D.; Santos, R.F.; de Oliveira, E.; Wernecke, I.; Secco, D.; de Souza, S.N.M. Effect of Nitrogen Fertilization and Cutting Age on Yield of Tropical Forage Plants. Afr. J. Agric. Res. 2013, 8, 1427–1432. [Google Scholar]
- Clevers, J.G.; Gitelson, A.A. Remote Estimation of Crop and Grass Chlorophyll and Nitrogen Content Using Red-Edge Bands on Sentinel-2 and-3. Int. J. Appl. Earth Obs. Geoinf. 2013, 23, 344–351. [Google Scholar] [CrossRef]
- Adam, E.; Mutanga, O.; Rugege, D. Multispectral and Hyperspectral Remote Sensing for Identification and Mapping of Wetland Vegetation: A Review. Wetl. Ecol. Manag. 2010, 18, 281–296. [Google Scholar] [CrossRef]
- Mansour, K.; Mutanga, O.; Everson, T. Remote Sensing Based Indicators of Vegetation Species for Assessing Rangeland Degradation: Opportunities and Challenges. Afr. J. Agric. Res. 2012, 7, 3261–3270. [Google Scholar]
- Thenkabail, P.S.; Lyon, J.G. Hyperspectral Remote Sensing of Vegetation: Knowledge Gain and Knowledge Gap after 50 Years of Research (Conference Presentation); Paper Presented at the Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2017; CRC Press: Boca Raton, FL, USA, 2017. [Google Scholar]
- Thenkabail, P.S.; Enclona, E.A.; Ashton, M.S.; Legg, C.; De Dieu, M.J. Hyperion, Ikonos, Ali, and Etm+ Sensors in the Study of African Rainforests. Remote Sens. Environ. 2004, 90, 23–43. [Google Scholar] [CrossRef]
- Thenkabail, P.S.; Smith, R.B.; De Pauw, E. Evaluation of Narrowband and Broadband Vegetation Indices for Determining Optimal Hyperspectral Wavebands for Agricultural Crop Characterization. Photogramm. Eng. Remote Sens. 2002, 68, 607–622. [Google Scholar]
- Thenkabail, P.S.; Lyon, J.G. Hyperspectral Remote Sensing of Vegetation; CRC Press: Boca Raton, FL, USA, 2016. [Google Scholar]
- Clark, M.L. Comparison of Simulated Hyperspectral Hyspiri and Multispectral Landsat 8 and Sentinel-2 Imagery for Multi-Seasonal, Regional Land-Cover Mapping. Remote Sens. Environ. 2017, 300, 311–325. [Google Scholar] [CrossRef]
- Colkesen, I.; Kavzoglu, T. Ensemble-Based Canonical Correlation Forest (Ccf) for Land Use and Land Cover Classification Using Sentinel-2 and Landsat Oli Imagery. Remote Sens. Lett. 2017, 8, 1082–1091. [Google Scholar] [CrossRef]
- Lillesand, T.; Ralph, W.K.; Jonathan, C. Remote Sensing and Image Interpretation; John Wiley & Sons: Hoboken, NJ, USA, 2015. [Google Scholar]
- Thenkabail, P.S.; Mariotto, I.; Gumma, M.K.; Middleton, M.E.; Landis, R.D.; Huemmrich, F.K. Selection of Hyperspectral Narrowbands (Hnbs) and Composition of Hyperspectral Twoband Vegetation Indices (Hvis) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance and Hyperion/Eo-1 Data. Sel. Top. Appl. Earth Obs. Remote. Sens. IEEE J. 2013, 6, 427–439. [Google Scholar] [CrossRef]
- Mariotto, I.; Prasad, S.; Thenkabail, A.H.; Terrence Slonecker, E.; Platonov, A. Hyperspectral Versus Multispectral Crop-Productivity Modeling and Type Discrimination for the Hyspiri Mission. Remote Sens. Environ. 2013, 139, 291–305. [Google Scholar] [CrossRef]
- Pu, R.; Liu, D. Segmented Canonical Discriminant Analysis of in Situ Hyperspectral Data for Identifying 13 Urban Tree Species. Int. J. Remote. Sens. 2011, 32, 2207–2226. [Google Scholar] [CrossRef]
- Karimi Prasher, Y.S.O.; McNairn, H.; Bonnell, R.B.; Dutilleul, P.; Goel, P.K. Classification Accuracy of Discriminant Analysis, Artificial Neural Networks, and Decision Trees for Weed and Nitrogen Stress Detection in Corn. Trans. ASAE 2005, 35, 1261–1268. [Google Scholar] [CrossRef]
- Filella, I.; Serrano, L.; Serra, J.; Penuelas, J. Evaluating Wheat Nitrogen Status with Canopy Reflectance Indices and Discriminant Analysis. Crop Sci. 1995, 35, 1400–1405. [Google Scholar] [CrossRef]
Sensor | Orbital Altitude | Revisit Time (days) | Swath Width (Km) | Spectral Bands | Band Centre | Spatial Resolution |
---|---|---|---|---|---|---|
HyspIRI (VSWIR) | 626 km | 16 | 185 km | Contiguous (10 nm) | 30 | |
380–2500 nm | ||||||
Landsat 9 OLI-2 | 705 km | 16 | 185 km | 1Coastal/Aerosol | 443 | 30 |
2Blue | 482 | 30 | ||||
3Green | 562 | 30 | ||||
4Red | 655 | 30 | ||||
5NIR | 865 | 30 | ||||
6SWIR 1 | 1610 | 30 | ||||
7SWIR 2 | 2200 | 30 | ||||
8Panchromatic | 590 | 15 | ||||
9Cirrus | 1375 | 30 | ||||
10Thermal | 10,800 | 100 | ||||
11Thermal | 12,000 | 100 | ||||
Sentinel 2-MSI | 786 km | 5 | 280km | 1Coastal aerosols | 443 | 60 |
2Blue | 490 | 10 | ||||
3Green | 560 | 10 | ||||
4Yellow | 665 | 10 | ||||
5Red edge | 705 | 20 | ||||
6Red edge | 740 | 20 | ||||
7Red edge | 783 | 20 | ||||
8NIR | 842 | 10 | ||||
8aNIR | 865 | 20 | ||||
9NIR | 945 | 60 | ||||
10SWIR | 1375 | 60 | ||||
11SWIR | 1610 | 20 | ||||
12SWIR | 2190 | 20 |
Sensor | PLS-DA | DA | PLS-DA | DA | |||||
---|---|---|---|---|---|---|---|---|---|
PA | UA | PA | UA | OA | Kappa | OA | Kappa | ||
ABR | 100 | 95 | 100 | 100 | 0.97 | 0.90 | 0.99 | 0.99 | |
HyspIRI | CHFM | 92 | 100 | 100 | 100 | ||||
NUC | 100 | 94 | 100 | 100 | |||||
Sentinel 2 MSI | ABR | 91 | 100 | 100 | 100 | 0.90 | 0.69 | 0.95 | 0.85 |
CHFM | 86 | 100 | 89 | 100 | |||||
NUC | 100 | 65 | 100 | 82 | |||||
Landsat 9 OLI-2 | ABR | 91 | 95 | 95 | 100 | 0.89 | 0.63 | 0.94 | 0.79 |
CHFM | 83 | 100 | 89 | 100 | |||||
NUC | 100 | 65 | 100 | 76 |
Spectrum | Sensor | |||||
---|---|---|---|---|---|---|
HyspIRI | Sentinel | Landsat | ||||
DA | PLS-DA | DA | PLS-DA | DA | PLS-DA | |
Visible | 5 | 4 | 2 | 1 | 2 | 1 |
Red edge | 12 | 10 | 3 | 3 | ||
NIR | 10 | 10 | 2 | 2 | 1 | 1 |
SWIR | 8 | 7 | 1 | 1 | 2 | 2 |
M/FWIR | 7 | 7 | ||||
Total | 42 | 38 | 8 | 7 | 5 | 4 |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Sibanda, M.; Mutanga, O.; Magwaza, L.S.; Dube, T.; Magwaza, S.T.; Odindo, A.O.; Mditshwa, A.; Mafongoya, P.L. Discrimination of Tomato Plants (Solanum lycopersicum) Grown under Anaerobic Baffled Reactor Effluent, Nitrified Urine Concentrates and Commercial Hydroponic Fertilizer Regimes Using Simulated Sensor Spectral Settings. Agronomy 2019, 9, 373. https://doi.org/10.3390/agronomy9070373
Sibanda M, Mutanga O, Magwaza LS, Dube T, Magwaza ST, Odindo AO, Mditshwa A, Mafongoya PL. Discrimination of Tomato Plants (Solanum lycopersicum) Grown under Anaerobic Baffled Reactor Effluent, Nitrified Urine Concentrates and Commercial Hydroponic Fertilizer Regimes Using Simulated Sensor Spectral Settings. Agronomy. 2019; 9(7):373. https://doi.org/10.3390/agronomy9070373
Chicago/Turabian StyleSibanda, Mbulisi, Onisimo Mutanga, Lembe S. Magwaza, Timothy Dube, Shirly T. Magwaza, Alfred O. Odindo, Asanda Mditshwa, and Paramu L. Mafongoya. 2019. "Discrimination of Tomato Plants (Solanum lycopersicum) Grown under Anaerobic Baffled Reactor Effluent, Nitrified Urine Concentrates and Commercial Hydroponic Fertilizer Regimes Using Simulated Sensor Spectral Settings" Agronomy 9, no. 7: 373. https://doi.org/10.3390/agronomy9070373
APA StyleSibanda, M., Mutanga, O., Magwaza, L. S., Dube, T., Magwaza, S. T., Odindo, A. O., Mditshwa, A., & Mafongoya, P. L. (2019). Discrimination of Tomato Plants (Solanum lycopersicum) Grown under Anaerobic Baffled Reactor Effluent, Nitrified Urine Concentrates and Commercial Hydroponic Fertilizer Regimes Using Simulated Sensor Spectral Settings. Agronomy, 9(7), 373. https://doi.org/10.3390/agronomy9070373