Coffee-Yield Estimation Using High-Resolution Time-Series Satellite Images and Machine Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Field Data
2.2. Satellite Data
2.3. Statistical Analysis
3. Results and Discussion
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Santana, L.S.; Ferraz, G.A.E.S.; Teodoro, A.J.d.S.; Santana, M.S.; Rossi, G.; Palchetti, E. Advances in Precision Coffee Growing Research: A Bibliometric Review. Agronomy 2021, 11, 1557. [Google Scholar] [CrossRef]
- Santinato, F. Inovações Tecnológicas Para Cafeicultura de Precisão. Ph.D. Thesis, School of Agricultural and Veterinarian Studies, São Paulo State University, Jaboticabal, Brazil, 2016. [Google Scholar]
- Chemura, A.; Mutanga, O.; Odindi, J.; Kutywayo, D. Mapping Spatial Variability of Foliar Nitrogen in Coffee (Coffea arabica L.) Plantations with Multispectral Sentinel-2 MSI Data. ISPRS J. Photogramm. Remote Sens. 2018, 138, 1–11. [Google Scholar] [CrossRef]
- Pham, Y.; Reardon-Smith, K.; Mushtaq, S.; Cockfield, G. The Impact of Climate Change and Variability on Coffee Production: A Systematic Review. Clim. Change 2019, 156, 609–630. [Google Scholar] [CrossRef]
- Marin, D.B.; Ferraz, G.A.e.S.; Guimarães, P.H.S.; Schwerz, F.; Santana, L.S.; Barbosa, B.D.S.; Barata, R.A.P.; Faria, R.d.O.; Dias, J.E.L.; Conti, L.; et al. Remotely Piloted Aircraft and Random Forest in the Evaluation of the Spatial Variability of Foliar Nitrogen in Coffee Crop. Remote Sens. 2021, 13, 1471. [Google Scholar] [CrossRef]
- Bazame, H.C.; Molin, J.P.; Althoff, D.; Martello, M. Detection, Classification, and Mapping of Coffee Fruits during Harvest with Computer Vision. Comput. Electron. Agric. 2021, 183, 106066. [Google Scholar] [CrossRef]
- Mulla, D.J. Twenty Five Years of Remote Sensing in Precision Agriculture: Key Advances and Remaining Knowledge Gaps. Biosyst. Eng. 2013, 114, 358–371. [Google Scholar] [CrossRef]
- Kayad, A.; Sozzi, M.; Gatto, S.; Marinello, F.; Pirotti, F. Monitoring Within-Field Variability of Corn Yield Using Sentinel-2 and Machine Learning Techniques. Remote Sens. 2019, 11, 2873. [Google Scholar] [CrossRef] [Green Version]
- Damian, J.M.; Pias, O.H.d.C.; Cherubin, M.R.; da Fonseca, A.Z.; Fornari, E.Z.; Santi, A.L. Applying the NDVI from Satellite Images in Delimiting Management Zones for Annual Crops. Sci. Agric. 2020, 77, e20180055. [Google Scholar] [CrossRef]
- El-Ghany, N.M.A.; El-Aziz, S.E.A.; Marei, S.S. A Review: Application of Remote Sensing as a Promising Strategy for Insect Pests and Diseases Management. Environ. Sci. Pollut. Res. 2020, 27, 33503–33515. [Google Scholar] [CrossRef]
- Karthikeyan, L.; Chawla, I.; Mishra, A.K. A Review of Remote Sensing Applications in Agriculture for Food Security: Crop Growth and Yield, Irrigation, and Crop Losses. J. Hydrol. 2020, 586, 124905. [Google Scholar] [CrossRef]
- Saraiva, M.; Protas, É.; Salgado, M.; Souza, C. Automatic Mapping of Center Pivot Irrigation Systems from Satellite Images Using Deep Learning. Remote Sens. 2020, 12, 558. [Google Scholar] [CrossRef] [Green Version]
- Fabbri, C.; Mancini, M.; Marta, A.D.; Orlandini, S.; Napoli, M. Integrating Satellite Data with a Nitrogen Nutrition Curve for Precision Top-Dress Fertilization of Durum Wheat. Eur. J. Agron. 2020, 120, 126148. [Google Scholar] [CrossRef]
- Lobell, D.B.; Thau, D.; Seifert, C.; Engle, E.; Little, B. A Scalable Satellite-Based Crop Yield Mapper. Remote Sens. Environ. 2015, 164, 324–333. [Google Scholar] [CrossRef]
- Colaço, A.F.; Bramley, R.G.V. Do Crop Sensors Promote Improved Nitrogen Management in Grain Crops? Field Crops Res. 2018, 218, 126–140. [Google Scholar] [CrossRef]
- Bramley, R.G.V.; Ouzman, J.; Gobbett, D.L. Regional Scale Application of the Precision Agriculture Thought Process to Promote Improved Fertilizer Management in the Australian Sugar Industry. Precis. Agric. 2019, 20, 362–378. [Google Scholar] [CrossRef]
- Luo, X.; Ye, Z.; Xu, H.; Zhang, D.; Bai, S.; Ying, Y. Robustness Improvement of NIR-Based Determination of Soluble Solids in Apple Fruit by Local Calibration. Postharvest Biol. Technol. 2018, 139, 82–90. [Google Scholar] [CrossRef]
- Lawes, R.A.; Oliver, Y.M.; Huth, N.I. Optimal Nitrogen Rate Can Be Predicted Using Average Yield and Estimates of Soil Water and Leaf Nitrogen with Infield Experimentation. Agron. J. 2019, 111, 1155–1164. [Google Scholar] [CrossRef]
- Padarian, J.; Minasny, B.; McBratney, A.B. Transfer Learning to Localise a Continental Soil Vis-NIR Calibration Model. Geoderma 2019, 340, 279–288. [Google Scholar] [CrossRef]
- Vega, A.; Córdoba, M.; Castro-Franco, M.; Balzarini, M. Protocol for Automating Error Removal from Yield Maps. Precis. Agric. 2019, 20, 1030–1044. [Google Scholar] [CrossRef]
- Jeffries, G.R.; Griffin, T.S.; Fleisher, D.H.; Naumova, E.N.; Koch, M.; Wardlow, B.D. Mapping Sub-Field Maize Yields in Nebraska, USA by Combining Remote Sensing Imagery, Crop Simulation Models, and Machine Learning. Precis. Agric. 2020, 21, 678–694. [Google Scholar] [CrossRef]
- Momin, M.A.; Grift, T.E.; Valente, D.S.; Hansen, A.C. Sugarcane Yield Mapping Based on Vehicle Tracking. Precis. Agric. 2019, 20, 896–910. [Google Scholar] [CrossRef]
- De Silva, F.M.; de Souza, Z.M.; de Figueiredo, C.A.P.; Vieira, L.H.S.; de Oliveira, E. Variabilidade Espacial de Atributos Químicos e Produtividade Da Cultura Do Café Em Duas Safras Agrícolas. Ciência Agrotecnologia 2008, 32, 231–241. [Google Scholar] [CrossRef] [Green Version]
- Ferraz, G.; da Silva, F.; Carvalho, L.C.C.; Alves, M.D.C.; Franco, B.C. Spatial and Temporal Variability of Phosphorous, Potassium and of the Yield of a Coffee Field. Eng. Agríc. Jaboticabal 2012, 32, 140–150. [Google Scholar] [CrossRef] [Green Version]
- Carvalho, L.C.C.; da Silva, F.M.; Ferraz, G.A.E.S.; Stracieri, J.; Ferraz, P.F.P.; Ambrosano, L. Geostatistical Analysis of Arabic Coffee Yield in Two Crop Seasons. Rev. Bras. Eng. Agric. Ambient. 2017, 21, 410–414. [Google Scholar] [CrossRef]
- Sartori, S.; Fava, J.F.M.; Domingues, E.L.; Ribeiro Filho, A.C.; Shiraisi, L.E. Mapping the Spatial Variability of Coffee Yield with Mechanical Harvester; American Society of Agricultural and Biological Engineers: Saint Joseph, MI, USA, 2013. [Google Scholar]
- Martello, M.; Molin, J.P.; Bazame, H.C. Obtaining and Validating High-Density Coffee Yield Data. Horticulturae 2022, 8, 421. [Google Scholar] [CrossRef]
- Rahnemoonfar, M.; Sheppard, C. Deep Count: Fruit Counting Based on Deep Simulated Learning. Sensors 2017, 17, 905. [Google Scholar] [CrossRef] [Green Version]
- Molin, J.P.; Motomiya, A.V.d.A.; Frasson, F.R.; Faulin, G.D.C.; Tosta, W. Test Procedure for Variable Rate Fertilizer on Coffee. Acta Scientiarum. Acta Sci. Agron. 2010, 32, 569–575. [Google Scholar] [CrossRef] [Green Version]
- Bernardes, T.; Moreira, M.A.; Adami, M.; Giarolla, A.; Rudorff, B.F.T. Monitoring Biennial Bearing Effect on Coffee Yield Using MODIS Remote Sensing Imagery. Remote Sens. 2012, 4, 2492. [Google Scholar] [CrossRef] [Green Version]
- Nogueira, S.M.C.; Moreira, M.A.; Volpato, M.M.L. Relationship between Coffee Crop Yield and Vegetation Indexes Derived from Oli/Landsat-8 Sensor Data with and without Topographic Correction. Eng. Agric. 2018, 38, 387–394. [Google Scholar] [CrossRef]
- Thao, N.T.T.; Khoi, D.N.; Denis, A.; Viet, L.V.; Wellens, J.; Tychon, B. Early Prediction of Coffee Yield in the Central Highlands of Vietnam Using a Statistical Approach and Satellite Remote Sensing Vegetation Biophysical Variables. Remote Sens. 2022, 14, 2975. [Google Scholar] [CrossRef]
- Silva, P.A.D.A.; Alves, M.d.C.; da Silva, F.M.; Figueiredo, V.C. Coffee Yield Estimation by Landsat-8 Imagery Considering Shading Effects of Planting Row’s Orientation in Center Pivot. Remote Sens. Appl. Soc. Environ. 2021, 24, 100613. [Google Scholar] [CrossRef]
- Canata, T.F.; Wei, M.C.F.; Maldaner, L.F.; Molin, J.P. Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique. Remote Sens. 2021, 13, 232. [Google Scholar] [CrossRef]
- Hunt, D.A.; Tabor, K.; Hewson, J.H.; Wood, M.A.; Reymondin, L.; Koenig, K.; Schmitt-Harsh, M.; Follett, F. Review of Remote Sensing Methods to Map Coffee Production Systems. Remote Sens. 2020, 12, 2041. [Google Scholar] [CrossRef]
- Jeong, J.H.; Resop, J.P.; Mueller, N.D.; Fleisher, D.H.; Yun, K.; Butler, E.E.; Timlin, D.J.; Shim, K.M.; Gerber, J.S.; Reddy, V.R.; et al. Random Forests for Global and Regional Crop Yield Predictions. PLoS ONE 2016, 11, e0156571. [Google Scholar] [CrossRef] [Green Version]
- Hochachka, W.M.; Caruana, R.; Fink, D.; Munson, A.; Riedewald, M.; Sorokina, D.; Kelling, S. Data-Mining Discovery of Pattern and Process in Ecological Systems. J. Wildl. Manag. 2007, 71, 2427. [Google Scholar] [CrossRef]
- Alvares, C.A.; Stape, J.L.; Sentelhas, P.C.; de Moraes Gonçalves, J.L.; Sparovek, G. Köppen’s Climate Classification Map for Brazil. Meteorol. Z. 2013, 22, 711–728. [Google Scholar] [CrossRef]
- INMET. Instituto Nacional de Meteorologia: Brazil Climate Normals 1991–2020; INMET: Brasília, Brazil, 2022. Available online: https://portal.inmet.gov.br/uploads/normais/NORMAISCLIMATOLOGICAS.pdf (accessed on 15 July 2022).
- Maldaner, L.F.; Molin, J.P. Data Processing within Rows for Sugarcane Yield Mapping. Sci. Agric. 2020, 77, e20180391. [Google Scholar] [CrossRef]
- Minasny, B.; McBratney, A.B.; Whelan, B.M. VESPER, Version 1.62; University of Sydney: Sydney, Australia, 2006. [Google Scholar]
- Planet Team. Planet Application Program Interface: In Space for Life on Earth. San Francisco, CA, USA. 2017. Available online: https://api.planet.com (accessed on 15 July 2022).
- Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring Vegetation Systems in the Great Plains with ERTS; NASA Special Publication; NASA: Washington, DC, USA, 1974; p. 24. [Google Scholar]
- Rodríguez-López, L.; Duran-Llacer, I.; González-Rodríguez, L.; Abarca-Del-Rio, R.; Cárdenas, R.; Parra, O.; Martínez-Retureta, R.; Urrutia, R. Spectral analysis using LANDSAT images to monitor the chlorophyll-a concentration in Lake Laja in Chile. Ecol. Inform. 2020, 60, 101183. [Google Scholar] [CrossRef]
- Gitelson, A.A.; Kaufman, Y.J.; Merzlyak, M.N. Use of a Green Channel in Remote Sensing of Global Vegetation from EOS- MODIS. Remote Sens. Environ. 1996, 58, 289–298. [Google Scholar] [CrossRef]
- Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef]
- Liaw, A.; Wiener, M. Classification and Regression by RandomForest. R News 2002, 2, 18–22. [Google Scholar]
- Olive, D.J. Multivariate Linear Regression. In Linear Regression; Olive, D.J., Ed.; Springer: Chan, Switzerland, 2017; pp. 17–83. [Google Scholar]
- QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. 2014. Available online: http://Qgis.Osgeo.Org (accessed on 15 July 2022).
- De Oliveira, R.R.; Cesarino, I.; Mazzafera, P.; Dornelas, M.C. Flower Development in Coffea Arabica L.: New Insights into MADS-Box Genes. Plant Reprod. 2014, 27, 79–94. [Google Scholar] [CrossRef]
- De Camargo, Â.P.; de Camargo, M.B.P. Definition and Outline for the Phenological Phases of Arabic Coffee under Brazilian Tropical Conditions. Bragantia 2001, 60, 65–68. [Google Scholar] [CrossRef] [Green Version]
- Lima, A.A.; Santos, I.S.; Torres, M.E.L.; Cardon, C.H.; Caldeira, C.F.; Lima, R.R.; Davies, W.J.; Dodd, I.C.; Chalfun-Junior, A. Drought and Re-Watering Modify Ethylene Production and Sensitivity, and Are Associated with Coffee Anthesis. Environ. Exp. Bot. 2021, 181, 104289. [Google Scholar] [CrossRef]
- Rena, A.B.; Maestri, M. Fisiologia Do Cafeeiro. Cultura Do Cafeeiro: Fatores Que Afetam a Produtividad; Associação Brasileira para Pesquisa da Potassa e do Fosfato: Piracicaba, Brazil, 1986. [Google Scholar]
- Pereira, S.P.; Bartholo, G.F.; Baliza, D.P.; Sobreira, F.M.; Guimarães, R.J. Growth, Yield and Bienniality of Coffee Plants According to Cultivation Spacing|Crescimento, Produtividade e Bienalidade Do Cafeeiro Em Função Do Espaçamento de Cultivo. Pesqui. Agropecu. Bras. 2011, 46, 152–160. [Google Scholar] [CrossRef]
- De Gaspari-Pezzopane, C.; Medina Filho, H.P.; Bordignon, R.; Siqueira, W.J.; Ambrósio, L.A.; Mazzafera, P. Environmental Influences on the Intrinsic Outturn of Coffee. Bragantia 2005, 64, 39–50. [Google Scholar]
- Wei, M.C.F.; Maldaner, L.F.; Ottoni, P.M.N.; Molin, J.P. Carrot Yield Mapping: A Precision Agriculture Approach Based on Machine Learning. AI 2020, 1, 15. [Google Scholar] [CrossRef]
- Angnes, G.; Martello, M.; Faulin, G.D.C.; Molin, J.P.; Romanelli, T.L. Energy Efficiency of Variable Rate Fertilizer Application in Coffee Production in Brazil. AgriEngineering 2021, 3, 51. [Google Scholar] [CrossRef]
- Skakun, S.; Brown, M.G.L.; Roger, J.C.; Vermote, E. Capturing Corn and Soybean Yield Variability at Field Scale Using Very High Spatial Resolution Satellite Data. In Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Virtual, 26 September–2 October 2020. [Google Scholar]
- Gava, R.; Santana, D.C.; Cotrim, M.F.; Rossi, F.S.; Teodoro, L.P.R.; da Silva Junior, C.A.; Teodoro, P.E. Soybean Cultivars Identification Using Remotely Sensed Image and Machine Learning Models. Sustainability 2022, 14, 7125. [Google Scholar] [CrossRef]
- Rodrigues, W.N.; Brinate, S.V.B.; Martins, L.D.; Colodetti, T.V.; Tomaz, M.A. Genetic Variability and Expression of Agro-Morphological Traits among Genotypes of Coffea Arabica Being Promoted by Supplementary Irrigation. Genet. Mol. Res. 2017, 16, gmr16029563. [Google Scholar] [CrossRef]
- Miranda, F.R.; Drumond, L.C.D.; Ronchi, C.P. Synchronizing Coffee Blossoming and Fruit Ripening in Irrigated Crops of the Brazilian Cerrado Mineiro Region. Aust. J. Crop Sci. 2020, 14, 605–613. [Google Scholar] [CrossRef]
- International Society of Precision Agriculture (ISPA). Precision Ag Definition. Available online: https://www.ispag.org/about/definition (accessed on 15 July 2022).
- DaMatta, F.M.; Ronchi, C.P.; Maestri, M.; Barros, R.S. Ecophysiology of Coffee Growth and Production. Braz. J. Plant Physiol. 2007, 19, 485–510. [Google Scholar] [CrossRef]
Harvest 1st (2018/19) | Harvest 2nd (2019/20) | Harvest 3rd (2020/21) |
---|---|---|
30 July 18 | 28 July 19 | 27 July 20 |
31 August 18 | 31 August 19 | 31 August 20 |
29 September 18 | 22 September 19 | 28 September 20 |
27 October 18 | 14 October 19 | 26 October 20 |
21 December 18 | 31 December 19 | 18 December 20 |
30 January 19 | 14 January 20 | 30 January 21 |
25 February 19 | 21 February 20 | 1 February 21 |
15 March 19 | 19 March 20 | 21 March 21 |
27 April 19 | 25 April 20 | 28 April 21 |
28 May 19 | 28 May 20 | 25 May 21 |
30 June 19 | 28 June 20 | 28 June 21 |
Variable | Model | Hs | Mt | Training Dataset (2/3) | Test Dataset (1/3) | Full Dataset (3/3) | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
RMSE | R2 | MAE | RMSE | R2 | MAE | RMSE | R2 | MAE | ||||
Spectral Bands | RF | 1 | 11 a | 0.04 | 0.99 | 0.03 | 0.09 | 0.91 | 0.07 | 0.06 | 0.96 | 0.04 |
2 | 11 a | 0.05 | 0.99 | 0.04 | 0.13 | 0.93 | 0.10 | 0.09 | 0.97 | 0.06 | ||
3 | 11 a | 0.05 | 0.99 | 0.03 | 0.12 | 0.93 | 0.09 | 0.08 | 0.97 | 0.05 | ||
NDVI | 1 | 11 a | 0.04 | 0.98 | 0.03 | 0.10 | 0.87 | 0.08 | 0.07 | 0.94 | 0.05 | |
2 b | 0.10 | 0.89 | 0.08 | 0.20 | 0.51 | 0.16 | 0.14 | 0.76 | 0.11 | |||
2 | 11 a | 0.06 | 0.99 | 0.05 | 0.15 | 0.91 | 0.11 | 0.10 | 0.96 | 0.07 | ||
2 b | 0.10 | 0.96 | 0.08 | 0.21 | 0.81 | 0.16 | 0.15 | 0.91 | 0.11 | |||
3 | 11 a | 0.07 | 0.98 | 0.05 | 0.16 | 0.86 | 0.12 | 0.11 | 0.94 | 0.08 | ||
2 b | 0.12 | 0.93 | 0.09 | 0.25 | 0.68 | 0.19 | 0.17 | 0.84 | 0.13 | |||
GNDVI | 1 | 11 a | 0.05 | 0.97 | 0.04 | 0.12 | 0.83 | 0.09 | 0.08 | 0.93 | 0.05 | |
2 b | 0.10 | 0.90 | 0.08 | 0.19 | 0.53 | 0.16 | 0.14 | 0.77 | 0.11 | |||
2 | 11 a | 0.06 | 0.98 | 0.05 | 0.15 | 0.90 | 0.12 | 0.10 | 0.96 | 0.07 | ||
2 b | 0.10 | 0.96 | 0.08 | 0.21 | 0.82 | 0.16 | 0.15 | 0.91 | 0.11 | |||
3 | 11 a | 0.07 | 0.97 | 0.06 | 0.17 | 0.84 | 0.14 | 0.12 | 0.93 | 0.08 | ||
2 b | 0.12 | 0.93 | 0.09 | 0.24 | 0.69 | 0.19 | 0.17 | 0.85 | 0.12 | |||
Spectral Bands | MLR | 1 | 11 a | 0.12 | 0.81 | 0.10 | 0.12 | 0.81 | 0.10 | 0.12 | 0.81 | 0.10 |
2 | 11 a | 0.17 | 0.88 | 0.13 | 0.17 | 0.88 | 0.14 | 0.17 | 0.88 | 0.14 | ||
3 | 11 a | 0.16 | 0.86 | 0.13 | 0.16 | 0.86 | 0.13 | 0.16 | 0.86 | 0.13 | ||
NDVI | 1 | 11 a | 0.14 | 0.77 | 0.11 | 0.14 | 0.77 | 0.11 | 0.14 | 0.77 | 0.11 | |
2 b | 0.20 | 0.49 | 0.17 | 0.20 | 0.50 | 0.16 | 0.20 | 0.50 | 0.17 | |||
2 | 11 a | 0.19 | 0.86 | 0.15 | 0.19 | 0.86 | 0.15 | 0.19 | 0.86 | 0.15 | ||
2 b | 0.21 | 0.83 | 0.16 | 0.21 | 0.82 | 0.17 | 0.21 | 0.82 | 0.16 | |||
3 | 11 a | 0.21 | 0.77 | 0.17 | 0.21 | 0.76 | 0.16 | 0.21 | 0.76 | 0.17 | ||
2 b | 0.24 | 0.70 | 0.19 | 0.23 | 0.70 | 0.19 | 0.24 | 0.70 | 0.19 | |||
GNDVI | 1 | 11 a | 0.14 | 0.74 | 0.11 | 0.14 | 0.74 | 0.12 | 0.14 | 0.74 | 0.11 | |
2 b | 0.20 | 0.51 | 0.16 | 0.20 | 0.52 | 0.16 | 0.20 | 0.51 | 0.16 | |||
2 | 11 a | 0.18 | 0.87 | 0.14 | 0.19 | 0.86 | 0.15 | 0.18 | 0.86 | 0.15 | ||
2 b | 0.21 | 0.82 | 0.17 | 0.21 | 0.82 | 0.17 | 0.21 | 0.82 | 0.17 | |||
3 | 11 a | 0.22 | 0.75 | 0.17 | 0.22 | 0.75 | 0.17 | 0.22 | 0.75 | 0.17 | ||
2 b | 0.24 | 0.70 | 0.19 | 0.23 | 0.70 | 0.19 | 0.24 | 0.70 | 0.19 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 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 (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Martello, M.; Molin, J.P.; Wei, M.C.F.; Canal Filho, R.; Nicoletti, J.V.M. Coffee-Yield Estimation Using High-Resolution Time-Series Satellite Images and Machine Learning. AgriEngineering 2022, 4, 888-902. https://doi.org/10.3390/agriengineering4040057
Martello M, Molin JP, Wei MCF, Canal Filho R, Nicoletti JVM. Coffee-Yield Estimation Using High-Resolution Time-Series Satellite Images and Machine Learning. AgriEngineering. 2022; 4(4):888-902. https://doi.org/10.3390/agriengineering4040057
Chicago/Turabian StyleMartello, Maurício, José Paulo Molin, Marcelo Chan Fu Wei, Ricardo Canal Filho, and João Vitor Moreira Nicoletti. 2022. "Coffee-Yield Estimation Using High-Resolution Time-Series Satellite Images and Machine Learning" AgriEngineering 4, no. 4: 888-902. https://doi.org/10.3390/agriengineering4040057