Precision coffee growing: A review
DOI:
https://doi.org/10.25186/.v17i.2007Abstract
Precision Agriculture (PA) technologies introduction in coffee-growing is becoming essential to advances in sustainable cultivation and increase in output. Applications that involve PA techniques in coffee production are defined now as Precision Coffee growing (PC). Systematically explored, studies on the subject contribute to improvements in the area, relating soil variability to its impacts on plants. The PC's scientific approach offers new forms of management and more security in coffee production. Aimed at reducing pesticides application and nutrients to the soil, contributing to sustainable development in coffee production. Initially, the research on coffee production had dealt with soil spatial variability, highlighting the geostatistical methods and specific ways to sample the soil. With technological advances in agriculture, new ways of monitoring spatial variability are available. In this context, studies are arising on spatial variability related to the plant, applying terrestrial, aerial and orbital sensors, possibly creating perspectives for monitoring and mapping coffee production. Artificial intelligence, Remotely Piloted Aircraft (ARP) products, harvesting yield sensors, automatic grain classifiers, and remote sensing stand out as new technologies under development in coffee production. These applications in PC involving multidisciplinary research demonstrate new relevant ways of improving crop managing and sustainability guaranteeing.
Key words: Digital agriculture; spatial variability; sustainability of cultivation; remote sensing; Sensors.
References
ABER, J. S. et al. Challenge of infrared kite aerial photography: A digital update. Transactions of the Kansas Academy of Science, 112 (1/2): 31-39, 2009.
ALMEIDA, L. DA S.; GUIMARÃES, E. C. Geostatistics and exploratory factor analysis for spacial representation of soil chemical attribute in coffee growing. Coffee Science, 11(2):195-203, 2016.
ALMEIDA, L. S. da.; GUIMARÃES, E. C. Geostatistics for determination and modeling of spatial variability space of micronutrients in cultivated soil with coffee tree in the municipality of Araguari-MG. Cultura Agronômica, 26(2):58-66, 2017.
ALMEIDA, T. S.; SEDIYAMA, G. C.; DE ALENCAR, L. P. Estimativa da produtividade de cafeeiros irrigados pelo método zona agroecológica espectral. Revista Engenharia Na Agricultura - Reveng, 25(1):1-11, 2017.
ALVES, M. C. de. et al. Geostatistical analysis of the spatial variation of the berry borer and leaf miner in a coffee agroecosystem. Precision Agriculture, 12(1):18-31, 2011.
ALVES, E. A.; QUEIROZ, D. M.; PINTO, F. A. C. Cafeicultura de precisão. In: ZAMBOLIM, L. (Ed.). Boas práticas agrícolas na produção de café. Viçosa MG: Editora UFV, 2006. p. 190.
ALVES, M. C. et al. Modeling spatial variability and pattern of rust and brown eye spot in coffee agroecosystem. Journal of Pest Science, 82(2):137-148, 2009.
ANDRADE, A. D. et al. Spatial variability of soil penetration resistanacnder in coffee growing. Coffee Science, 13(3):341-348, 2018.
ANDRADE, A. D. et al. Technical and economic evaluation of different operating modes for mechanized fertilizer application in coffee plantations. Coffee Science, 15e151748, 2020.
ANGNES, G. et al. Energy efficiency of variable rate fertilizer application in coffee production in Brazil. AgriEngineering, 3(4):815-826, 2021.
ATZBERGER, C. Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sensing, 5(2):949-981, 2013.
AVENDANO, J.; RAMOS, P. J.; PRIETO, F. A. A system for classifying vegetative structures on coffee branches based on videos recorded in the field by a mobile device. Expert Systems with Applications, 88:178-192, 2017.
BAESSO, M. M. et al. Variabilidade espacial da qualidade do café cereja. Revista Brasileira de Engenharia de Biossistemas, 13(2): 109-115, 2019.
BARBOSA, B. D. S. et al. Application of rgb images obtained by uav in coffee farming. Remote Sensing, 13(12):2397, 2021.
BARROS, M. M. de. et al. Use of classifier to determine coffee harvest time by detachment force. Revista Brasileira de Engenharia Agrícola e Ambiental, 22(5):366-370, 2018.
BARROS, M. M. et al. Avaliação de um sistema de aplicação de fertilizantes a taxa variável adaptado à cultura cafeeira. Coffee Science, 10(2):223-232, 2015.
BARRY, P.; COAKLEY, R. Field accuracy test of RPAS photogrammetry. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(1):27-31, 2013.
BAZAME, H. C. et al. Detection, classification, and mapping of coffee fruits during harvest with computer vision. Computers and Electronics in Agriculture, 183:106066, 2021.
BENNUR, P. J.; TAYLOR, R. K. Evaluating the response time of a rate controller used with a sensor‐based, variable rate application system. Applied Engineering in Agriculture, 26(6):1069-1076, 2010.
BERETA, F. et al. Topographic modelling using UAVs compared with traditional survey methods in mining. Mining, 71(3):463-470, 2018.
BERNARDES, T. et al. Monitoring biennial bearing effect on coffee yield using MODIS remote sensing imagery. Remote Sensing, 4(9):2492-2509, 2012.
BHATTI, A. U.; MULLA, D. J.; FRAZIER, B. E. Estimation of soil properties and wheat yields on complex eroded hills using geostatistics and thematic mapper images. Remote Sensing of Environment, 37(3):181-191, 1991.
BOTTEGA, E. L. et al. Spatial variability of soil attributes in no a no-tillage system with crop rotation in the brazilian savannah. Revista Ciencia Agronomica, 44(1):1-9, 2013.
BURAK, D. L.; SANTOS, D. A.; PASSOS, R. R. Spatial variability of physical properties: relationship with relief, organic matter and productivity in conilon coffee. Coffee Science, 11(4):455-466, 2016.
CAMBARDELLA, C. A. et al. Field-scale variability of soil properties in central iowa soils. Soil Science Society of America Journal, 58(5):1501-1511, 1994.
CANDIAGO, S. et al. Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sensing, 7(4):4026-4047, 2015.
CARVALHO, J. R. P. de.; SILVEIRA, P. M. da.; VIEIRA, S. R. Geoestatística na determinação da variabilidade espacial de características químicas do solo sob diferentes preparos. Pesquisa Agropecuária Brasileira, 37(8):1151-1159, 2002.
CARVALHO, L. C. C. et al. Variabilidade espacial de atributos físicos do solo e características agronômicas da cultura do café. Coffee Science, 8(3):265-275, 2013.
CARVALHO, L. C. C. et al. Space analysis of clay content and humidity gravimetric, in different depth of layers in a red latosol dystrophic. Nucleus, 11(2):379-384, 2014.
CARVALHO, L. C. C. et al. Geostatistical analysis of arabic coffee yield in two crop seasons [Análise geoestatística da produtividade do cafeeiro arábica em duas safras]. Revista Brasileira de Engenharia Agricola e Ambiental, 21(6):410-414, 2017.
CHEMURA, A. et al. Mapping spatial variability of foliar nitrogen in coffee (Coffea arabica L.) plantations with multispectral Sentinel-2 MSI data. ISPRS Journal of Photogrammetry and Remote Sensing, 138:1-11, 2018.
CHEMURA, A.; MUTANGA, O.; DUBE, T. Integrating age in the detection and mapping of incongruous patches in coffee (Coffea arabica ) plantations using multi-temporal Landsat 8 NDVI anomalies. International Journal of Applied Earth Observations and Geoinformation, 57:1-13, 2017a.
CHEMURA, A.; MUTANGA, O.; DUBE, T. Remote sensing leaf water stress in coffee (Coffea arabica) using secondary effects of water absorption and random forests. Physics and Chemistry of the Earth, 100:317-324, 2017b.
CHLINGARYAN, A.; SUKKARIEH, S.; WHELAN, B. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and Electronics in Agriculture, 151:61-69, 2018.
CORA, J. E. et al. Assessment of spatial variability of soil attributes as a basis for the adoption of precision agriculture in sugarcane plantations. Revista Brasileira de Ciencia do Solo, 28(6):1013-1021, 2004.
COSTA, F. P.; LIMA, J. S. S. de. Cokrigagem na distribuição espacial do cálcio em um latossolo cultivado com café conilon. Nucleus, 8(1):269-276, 2011.
CUNHA, J. P. A. R.; NETO, M. A. S.; HURTADO, S. M. C. Estimating vegetation volume of coffee crops using images from unmanned aerial vehicles system , digital image tree crops, such as arabica coffee (Coffea arabica L .), present enormous technical challenges in terms. Precision Agriculture, 4430:41-47, 2019.
DEVI, G. M. S.; KUMAR, K. S. A. Remote sensing and GIS application for land quality assessment for coffee growing areas of Karnataka. Journal of the Indian Society of Remote Sensing, 36(1):89-97, 2008.
FARIA, R. O. de. et al. Technical and economic viability of manual harvesting coffee yield maps. Coffee Science, 15:e151674, 2020.
FERRAZ, G. A. E. S. et al. Viabilidade econômica do sistema de adubação diferenciado comparado ao sistema de adubação convencional em lavoura cafeeira: Um estudo de caso. Engenharia Agricola, 31(5):906-915, 2011.
FERRAZ, G. A. E. S. et al. Variabilidade espacial e temporal do fósforo, potássio e da produtividade de uma lavoura cafeeira. Engenharia Agricola, 32:(1):140-150, 2012a.
FERRAZ, G. A. E. S. et al. Geostatistical analysis of fruit yield and detachment force in coffee. Precision Agriculture,13(1):76-89, 2012b.
FERRAZ, G. A. S. et al. Variabilidade espacial dos atributos da planta de uma lavoura cafeeira. Revista Ciencia Agronomica, 48(1):81-91, 2017c.
FERRAZ, G. A. E. S. et al. Spatial variability of the dosage of P2O5 and K2O to fertilize in a variable rate and in a conventional way in a coffee field. Coffee Science, 10(3):346-356, 2015.
FERRAZ, G. A. E. S. et al. Metodología para determinar la cuadrícula de muestreo del suelo para la agricultura de precisión en un campo de café. DYNA (Colombia), 84(200):316-325, 2017a.
FERRAZ, G. A. E. S. et al. Comparison between the soil chemical atributes sampled comventionaly and in grid. Coffee Science, 12(1):17-29, 2017b.
FERRAZ, G. A. E S. et al. Spatial variability of the detachment force of coffee fruit. Engenharia Agricola, 34(6):1210-1223, 2014.
FERRAZ, G. A. S. et al. Spatial variability of soil fertility attributes and productivity in a coffee crop farm. Agronomy Research, 17(4):1630-1638, 2019.
FERREIRA JÚNIOR, L. G. de. et al. Recomendação para colheita mecânica do café baseado no comportamento de vibração das hastes derriçadoras. Ciencia Rural, 46(2):273-278, 2016.
FERREIRA JÚNIOR, L. G. de. et al. Characterization of the coffee fruit detachment force in crop subjected to mechanized harvesting. Coffee Science, 13(1):71-79, 2018.
FERREIRA JÚNIOR, L. G. de. et al. Dynamic behavior of coffee tree branches during mechanical harvest. Computers and Electronics in Agriculture, 173:105415, 2020.
FIGUEIREDO, V. C. et al. Analysis of spatial variability of force detachment of coffee fruits in central pivot. Coffee Science, 12(2):84-92, 2017.
FIGUEIREDO, V. C. et al. Development of a methodology to determine the best grid sampling precision coffee growing. Coffee Science, 13(3):312-323, 2018.
FURFARO, R. et al. Neural network algorithm for coffee ripeness evaluation using airborne images. Applied Engineering in Agriculture, 23(3):379-387, 2007.
GAERTNER, J. Vegetation classification of Coffea on Hawaii Island using WorldView-2 satellite imagery. Journal of Applied Remote Sensing, 11(4):046005, 2017.
GARCIA-FREITES, S. et al. The potential of coffee stems gasification to provide bioenergy for coffee farms: a case study in the Colombian coffee sector. Biomass Conversion and Biorefinery, 10(4):1137-1152, 2020.
GILES, D. Use of remotely piloted aircraft for pesticide applications: Issues and outlook. Outlooks on Pest Management, 27(5):196-197, 2016.
GREGO, C. R.; VIEIRA, S. R. Variabilidade espacial de propriedades físicas do solo em uma parcela experimental. Revista Brasileira de Ciência do Solo, 29(2):169-177, 2005.
HABOUDANE, D. et al. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81:416-426, 2002.
HARDIN, P. J.; HARDIN, T. J. Small-scale remotely piloted vehicles in environmental research. Geography Compass, 4(9):1297-1311, 2010.
HASSLER, S. C.; BAYSAL-GUREL, F. Unmanned aircraft system (UAS) technology and applications in agriculture. Agronomy, 9(10):618, 2019.
HERWITZ, S. R. et al. Imaging from an unmanned aerial vehicle: Agricultural surveillance and decision support. Computers and Electronics in Agriculture, 44(1):49-61, 2004.
HONKAVAARA, E. et al. Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture. Remote Sensing, 5(10):5006-5039, 2013.
HOUBORG, R.; FISCHER, J. B.; SKIDMORE, A. K. Advances in remote sensing of vegetation function and traits. International Journal of Applied Earth Observation and Geoinformation, 43:1-6, 2015.
HUANG, Y. et al. Agricultural remote sensing big data: Management and applications. Journal of Integrative Agriculture, 17(9):1915-1931, 2018.
INAMASU, R. Y. et al. Strategies for the implementation, management and operation of the Precision Agriculture Network. In: INAMASU, R. Y. et al. Agricultura de precisão: Um novo olhar. São Carlos, SP: Embrapa Instrumentação, p. 31-40, 2009.
INOUE, Y. et al. Assessing land-use and carbon stock in slash-and-burn ecosystems in tropical mountain of laos based on time-series satellite images. International Journal of Applied Earth Observation and Geoinformation, 12(4):287-297, 2010.
JACINTHO, J. L. et al. Management zones in coffee cultivation. Revista Brasileira de Engenharia Agricola e Ambiental, 21(2):94-99, 2017.
JARAMILLO-GIRALDO, C. et al. Relationship between spatio-temporal leaf area index and crop coefficient when monitoring coffee plots in Brazil using the Sentine-2. Journal of Agricultural Science, 11(15):187-199, 2019.
JOHNSON, L. F. et al. Feasibility of monitoring coffee field ripeness with airborne multispectral imagery. Applied Engineering in Agriculture, 20(6):845-850, 2004.
JORGE, R. F. et al. Spatial variability in fertigated coffee yields and plant nutrients in soil saturation extracts. International Journal for Innovation Education and Research, 7(11):347-360, 2019.
JÚNIOR, P. C. M. et al. Spatial variability of physical attributes of the soil in amazonian black soil under coffee cultivation. Coffee Science, 12(2):260-271, 2017.
KAMIMURA, K. M. et al. Variabilidade espacial de atributos físicos de um latossolo vermelho-amarelo, sob lavoura cafeeira. Revista Brasileira de Ciencia do Solo, 37(4):877-888, 2013.
KATSUHAMA, N. et al. Discrimination of areas infected with coffee leaf rust using a vegetation index. Remote Sensing Letters, 9(12):1168-1194, 2018.
KAWAKUBO, F. S.; PÉREZ MACHADO, R. P. Mapping coffee crops in southeastern Brazil using spectral mixture analysis and data mining classification. International Journal of Remote Sensing, 37(14):3414-3436, 2016.
KAZAMA, E. H. et al. Methodology for selective coffee harvesting in management zones of yield and maturation. Precision Agriculture, 22(3):711-733, 2021.
KELLEY, L. C.; PITCHER, L.; BACON, C. Using google earth engine to map complex shade-grown coffee landscapes in northern Nicaragua. Remote Sensing, 10(6):952, 2018.
LALIBERTE, A.; RANGO, A. Image processing and classification procedures for analysis of sub-decimeter imagery acquired with an unmanned aircraft over arid rangelands. GIScience and Remote Sensing, 48(1):4-23, 2011.
LAN, Y. et al. Current status and future directions of precision aerial application for site-specific crop management in the USA. Computers and Electronics in Agriculture, 74(1):34-38, 2010.
LELONG, C. C. D. et al. Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors, 8(5):3557-3585, 2008.
LIMA, J. S. S. de. et al. Estimativa da produtividade de café conilon utilizando técnicas de cokrigagem. Revista Ceres, 63(1):54-61, 2016.
LIMA, J. S. S. de.; DE OLIVEIRA, R. B.; SILVA, S. A. de. Spatial variability of particle size fractions of an Oxisol cultivated with conilon coffee. Revista Ceres, 59(6):867-872, 2012.
LIMA, J. S. S. de.; SILVA, S. A. de.; SILVA, J. M. da. Variabilidade espacial de atributos químicos de um latossolo vermelho-amarelo cultivado em plantio direto. Revista Ciência Agronômica, 44(1):16-23, 2013.
LÓPEZ-GRANADOS, F. et al. Early season weed mapping in sunflower using UAV technology: Variability of herbicide treatment maps against weed thresholds. Precision Agriculture, 17(2):183-199, 2016.
MARIN, D. B. et al. Sensoriamento remoto multiespectral na identificação e mapeamento das variáveis bióticas e abióticas do cafeeiro. Revista Ceres, 66(2):142-153, 2019a.
MARIN, D. B. et al. Multispectral radiometric monitoring of bacterial blight of coffee. Precision Agriculture, 20(5):959-982, 2019b.
MARIN, D. B. et al. Remotely piloted aircraft and random forest in the evaluation of the spatial variability of foliar nitrogen in coffee crop. Remote Sensing, 13(8):1471, 2021a.
MARIN, D. B. et al. Detecting coffee leaf rust with UAV-based vegetation indices and decision tree machine learning models. Computers and Electronics in Agriculture, 190:106476, 2021b.
MARTELLO, M.; MOLIN, J. P.; BAZAME, H. C. Obtaining and validating high-density coffee yield data. Horticulturae, 8(5):421, 2022.
MARTINS, G. D.; GALO, M. D. L. B. T.; VIEIRA, B. S. Detecting and mapping root-knot nematode infection in coffee crop using remote sensing measurements. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(12):5395-5403, 2017.
MATEEN, A.; ZHU, Q. Weed detection in wheat crop using uav for precision agriculture. Pakistan Journal of Agricultural Sciences, 56(3):775-784, 2019.
MELO, B. M. R. de. et al. Zonas de manejo em função de propriedades de solo, relevo e produtividade da lavoura cafeeira. Revista Agrogeoambiental, 9(2):49-59, 2017.
MIRANDA, J. M.; REINATO, R. A. O.; DA SILVA, A. B. Mathematical model for predicting coffee yield. Revista Brasileira de Engenharia Agricola e Ambiental, 18(4):353-361, 2014.
MOLIN, J. P. Agricultura de precisão aprimora gerenciamento. Visão Agrícola, p. 115-118, 2006.
MOLIN, J. P. et al. Método para avaliação de aplicaç ão de fertilizantes em taxa variávelem café. Acta Scientiarum - Agronomy, 32(4):569-575, 2010.
MORIYA, E. A. S. et al. Mapping mosaic virus in sugarcane based on hyperspectral images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(2):740-748, 2017.
NETO, P. H. W. et al. Um estudo da variabilidade espacial do conteúdo de areia do solo, utilizando diferentes métodos de interpolação. Publicatio UEPG: Ciências Exatas e da Terra, Agrárias e Engenharias, 12 (1):41-49, 2006.
NOGUEIRA, S. M. C.; MOREIRA, M. A.; VOLPATO, M. M. L. Evaluating precipitation estimates from Eta, TRMM and CHRIPS data in the south-southeast region of Minas Gerais state-Brazil. Remote Sensing, 10(2):313, 2018.
OLIVEIRA, A. R. de. et al. Geoestatistical interpolation applied in the spatialization of chemical attributes of a red-yellow latosol. Journal of Physical Therapy Science, 9(1):1-11, 2018.
OLIVEIRA, H. C. et al. Failure Detection in Row Crops from UAV Images Using Morphological Operators. IEEE Geoscience and Remote Sensing Letters, v. 15, n. 7, p. 991–995, 2018.
OLIVEIRA, W. J. C. das. et al. Estimativa do custo horário de equipamentos e serviços em diferentes níveis de tecnificação em agricultura de precisão. Revista de Ciências Agrarias - Amazon Journal of Agricultural and Environmental Sciences, 59(4):378-385, 2016.
ORTEGA-HUERTA, M. A. et al. Mapping coffee plantations with land sat imagery: An example from El Salvador. International Journal of Remote Sensing, 33(1):220-242, 2012.
MINCATO, R. L. et al. Using unmanned aerial vehicle and machine learning algorithm to monitor leaf nitrogen in coffee. Coffee Science, 15:e151736, 2020.
PEÑA, J. M. et al. Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) Images. PLoS ONE, 8(10):1-11, 2013.
PEREIRA, C. P. et al. Coffee crop’s biomass and carbon stock estimation with usage of high resolution satellites images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(3):1786-1795, 2013.
PIRES, M. S. O. de.; ALVESA, M. C. de.; POZZA, E. A. Multispectral radiometric characterization of coffee rust epidemic in different irrigation management systems. International Journal of Applied Earth Observation and Geoinformation, 86:102016, 2020.
PRADO, E. V. do.; MACHADO, T. D. A.; PRADO, F. M. T. Geração e correlação de zonas de manejo usando sensor Spad e condutividade elétrica aparente do solo para a cafeicultura irrigada na zona da mata mineira. Revista Científica Eletrônica de Agronomia, (27):1-7, 2015.
PUGH, S. et al. Spatiotemporal lagged models for variable rate irrigation in agriculture. Journal of Agricultural, Biological, and Environmental Statistics, 24(4):634-650, 2019.
PUTRA, B. T. W. et al. Using information from images for plantation monitoring: A review of solutions for smallholders. Information Processing in Agriculture, 7(1):109-119, 2020.
QUEIROZ, D. M. D. et al. Sensors applied to digital agriculture: A review. Revista Ciência Agronômica, 51:e20207751, 2021.
RAMIREZ, G. M.; JÚNIOR, J. Z. Estimation of biophysical parameters of coffee fields based on high-resolution satellite images. Engenharia Agricola, 30(3):468-479, 2010.
ROCHA, H. G. et al. Coffee productivity mapping from mathematical models for prediction of harvest. Coffee Science, 11(1):108-116, 2016.
SANCHEZ, M. G. B. et al. Delineation of specific management areas for coffee cultivation based on the soil-relief relationship and numerical classification. Precision Agriculture, 14(2):201-214, 2013.
SANCHEZ, R. B. et al. Variabilidade espacial de propriedades de Latossolo e da produção de café em diferentes superfícies geomórficas. Revista Brasileira de Engenharia Agrícola e Ambiental, 9(4):489-495, 2005.
SANTANA, L. S. et al. Vegetative vigor of maize crop obtained through vegetation indexes in orbital and aerial sensors images. Brazilian Journal of Biosystems Engineering, 13(3):195-206, 2019.
SANTANA, L. S. et al. Influence of flight altitude and control points in the georeferencing of images obtained by unmanned aerial vehicle. European Journal of Remote Sensing, 54(1):59-71, 2021a.
SANTANA, L. S. et al. Monitoring errors of semi-mechanized coffee planting by remotely piloted aircraft. Agronomy, 11(6):1224, 2021b.
SANTINATO, F. et al. Número de operações mecanizadas na colheita do café. Ciencia Rural, 45(10):1809-1814, 2015.
SANTOS, E. O. J. de.; GONTIJO, I.; SILVA, M. B. da. Spatial variability of soil acidity attributes and liming requirement for conilon. Coffee Science, 9(2):275-283, 2014.
SANTOS, E. O. J. de. et al. Variabilidade espacial de macronutrientes em uma lavoura de café conilon no Norte do Espírito Santo. Revista Ciencia Agronomica, 46(3):469-476, 2015.
SANTOS, E. O. J. de. et al. Sampling design of soil physical properties in a Conilon coffee field. Revista Brasileira de Ciencia do Solo, 41:e0160426, 2017.
SANTOS, L. M. dos. et al. Use of remotely piloted aircraft in precision agriculture: A review. DYNA 86(210):284-291, 2019a.
SANTOS, L. M. dos. et al. Analysis of flight parameters and georeferencing of images with different control points obtained by RPA. Agronomy Research, 17(5):2054-2063, 2019b.
SANTOS, F. F. L. dos. et al. Quality assessment of coffee beans through computer vision and machine learning algorithms. Coffee Science, 15:e151752, 2020.
SANTOS, L. M. dos. et al. Determining the leaf area index and percentage of area covered by coffee crops using uav rgb images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13:6401-6409, 2020a.
SANTOS, L. M. et al. Biophysical parameters of coffee crop estimated by UAV RGB images. Precision Agriculture, v. 21, n. 6, p. 1227–1241, 2020b.
SARMIENTO, C. M. et al. Comparação de classificadores supervisionados na discriminação de áreas cafeeiras em Campos Gerais - Minas Gerais. Coffee Science, 9(4):546-557, 2014.
SEELAN, S. K. et al . Remote sensing applications for precision agriculture: A learning community approach. Remote sensing of environment, 88(1-2), 157-169, 2003.
SIEBERT, S.; TEIZER, J. Mobile 3D mapping for surveying earthwork projects using an unmanned aerial vehicle (UAV) system. Automation in Construction, 41:1-14, 2014.
SILVA, A. et al. Mapas de fertilidade de solo em área manejada com agricultura de precisão cultivada com café. Brazilian Geographical Journal - Geosciences and Humanities Research Medium, 5(1):194-204, 2014.
SILVA, A. F. da. et al. Variabilidade espacial de atributos químicos do solo cultivado com café arábica (Coffea arabica L.) sob diferentes manejos. Coffee Science, 5(2):173-182, 2010.
SILVA, F. M. da. et al. Variabilidade espacial de atributos químicos e de produtividade na cultura do café. Ciência Rural, 37(2):401-407, 2007.
SILVA, F. M. da. et al. Efeitos da colheita manual na bienalidade do cafeeiro em Ijaci, Minas Gerais. Ciência e Agrotecnologia, 34(3):625-632, 2010.
SILVA, F. M. et al. Spatial variability of chemical attributes and coffee productivity in two harvests. Ciencia e Agrotecnologia, 32(1):231-241, 2008.
SILVA, S. A. de. et al. Spatial variability of chemical attributes of an oxisol under coffee cultivation. Revista Brasileira de Ciência do Solo, 34(1):16-23, 2010.
SILVA, S. A. de.; LIMA, J. S. de. Avaliação da variabilidade do estado nutricional e produtividade de café por meio da análise de componentes principais e geoestatística. Revista Ceres, 59(2):271-277, 2012.
SILVA, S. A. de.; LIMA, J. S. S. Physical attributes of soil and its relation with space productivity of Coffee Arabic. Coffee Science, 8(4):395-403, 2013.
SIMÕES, W. L. et al. Variabilidade espacial de atributos físicos de um latossolo vermelho distroférrico, submetido a diferentes manejos. Revista Brasileira de Ciência do Solo, 30(6):1061-1068, 2006.
SOELA, D. M. et al. Control spraying process statistics using uniproved air vehicle in conilon coffee culture. Brazilian Journal of Production Engineering, 6(4):52-63, 2020.
SOTT, M. K. et al. Precision techniques and agriculture 4.0 technologies to promote sustainability in the coffee sector: State of the art, challenges and future trends. IEEE Access, 8:149854-149867, 2020.
SOUZA, C. G. et al. Multitemporal variables for the mapping of coffee cultivation areas. Pesquisa Agropecuaria Brasileira, 54:e00017, 2019.
STOLT, M. H. et al. Spatial variability in palustrine wetlands. Soil Science Society of America Journal, 65(2):527-535, 2001.
SWANN, L. Unmanned Aircraft Systems (UAS), Quebec, Canada, 2016. 54p.
TAKAHASHI, R.; TODO, Y. Coffee certification and forest quality: Evidence from a wild coffee Forest in Ethiopia. World Development, 92(25):158-166, 2017.
TEKIN, A. B. Variable rate fertilizer application in Turkish wheat agriculture: Economic assessment. African Journal of Agricultural Research, 5(8):647-652, 2010.
TOKEKAR, P. et al. Sensor planning for a symbiotic UAV and UGV system for precision agriculture. IEEE Transactions on Robotics, 32(6):1498-1511, 2016.
TORRADO, J. O. E.; JIMÉNEZ, J. J. C.; DÍAZ, H. P. Ortomosaicos y modelos digitales de elevación generados a partir de imágenes tomadas con sistemas UAV. Tecnura, 20(50):119-140, 2016.
TRABAQUINI, K. et al. Uso da Geotenologia para caracterizar os cafezais no municipio de Londrina-PR, em relação à altimetria, declividade e tipo de solo. Engenharia Agrícola, 30(6):1136-1147, 2010.
USTIN, S. L.; GAMON, J. A. Remote sensing of plant functional types. New Phytologist, 186(4):795-816, 2010.
UYSAL, M.; TOPRAK, A. S.; POLAT, N. DEM generation with UAV Photogrammetry and accuracy analysis in Sahitler hill. Measurement: Journal of the International Measurement Confederation, 73:539-543, 2015.
VALENTE, D. S. M. et al. Definition of management zones in coffee production fields based on apparent soil electrical conductivity. Scientia Agricola, 69(3):173-179, 2012.
VELÁSQUEZ, D. et al. A method for detecting coffee leaf rust through wireless sensor networks, remote sensing, and deep learning: Case study of the Caturra variety in Colombia. Applied Sciences (Switzerland), 10(2):1-27, 2020.
VERICAT, D. et al. Accuracy assessment of aerial photographs acquired using lighter-than-air blimps: Low-cost tools for mapping river corridors. River Research and Applications, 25:985-1000, 2009.
VIEIRA, S. R. et al. Spatial variability of soil chemical properties after coffee tree removal. Revista Brasileira de Ciencia do Solo, 33(5):1507-1514, 2009.
VIERLING, L. A. et al. The Short wave aerostat-mounted imager (SWAMI): A novel platform for acquiring remotely sensed data from a tethered balloon. Remote Sensing of Environment, 103(3):255-264, 2006.
VIRK, S. S. et al. Row-crop planter performance to support variable-rate seeding of maize. Precision Agriculture, 21(3):603-619, 2020.
WALKLATE, P. J.; CROSS, J. V.; PERGHER, G. Support system for efficient dosage of orchard and vineyard spraying products. Computers and Electronics in Agriculture, 75(2):355-362, 2011.
WANG, L. et al. Vision-based adaptive variable rate spraying approach for unmanned aerial vehicles. International Journal of Agricultural and Biological Engineering, 12(3):18-26, 2019.
WEISS, M.; JACOB, F.; DUVEILLER, G. Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment, 236:111402, 2020.
WEST, G. H.; KOVACS, K. Addressing groundwater declines with precision agriculture: An economic comparison of monitoring methods for variable-rate irrigation. Water, 9(1):28, 2017.
WHITEHEAD, K. et al. Remote sensing of the environment with small unmanned aircraft systems (Uass), part 2: Scientific and commercial applications. Journal of Unmanned Vehicle Systems,
(3):86-102, 2014.
WOLFERT, S. et al. Big data in smart farming - A review. Agricultural Systems, 153:69-80, 2017.
XIANG, H.; TIAN, L. Method for automatic georeferencing aerial remote sensing (RS) images from an unmanned aerial vehicle (UAV) platform. Biosystems Engineering, 108(2):104-113, 2011.
ZAJKOWSKI, T. J. et al. Evaluation and use of remotely piloted aircraft systems for operations and research - RxCADRE 2012. International Journal of Wildland Fire, 25(1):114-128, 2016.
ZANELLA, M. A. et al. Spatial correlation between the chlorophyll index and foliar npk levels in coffee crop. Coffee Science, 15:e151765, 2020.
ZHANG, C.; KOVACS, J. M. The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13(6):693-712, 2012.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Coffee Science - ISSN 1984-3909Os direitos autorais dos artigos publicados nesta revista pertencem aos autores, com os primeiros direitos de publicação pertencentes à revista. Como os artigos aparecem nesta revista com acesso aberto, eles podem ser usados livremente, com as devidas atribuições, em aplicativos educacionais e não comerciais.
