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dc.contributor.advisor1Castoldi, Gustavo-
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/4048332757094949pt_BR
dc.contributor.advisor-co1Santos, Leonardo de Castro-
dc.contributor.advisor-co1Latteshttp://lattes.cnpq.br/5190645592661467pt_BR
dc.contributor.referee1Castoldi, Gustavo-
dc.contributor.referee1Latteshttp://lattes.cnpq.br/4048332757094949pt_BR
dc.contributor.referee2Santos, Leonardo de Castro-
dc.contributor.referee2Latteshttp://lattes.cnpq.br/5190645592661467pt_BR
dc.contributor.referee3Santos, Darliane de Castro-
dc.contributor.referee3Latteshttp://lattes.cnpq.br/3716108640056809pt_BR
dc.contributor.referee4Geraldine, Alaerson Maia-
dc.contributor.referee4Latteshttp://lattes.cnpq.br/0083813255453278pt_BR
dc.creatorSilva, Rômulo Moreira-
dc.creator.Latteshttp://lattes.cnpq.br/5173496855217845pt_BR
dc.date.accessioned2022-03-03T18:52:27Z-
dc.date.available2022-02-25-
dc.date.available2022-03-03T18:52:27Z-
dc.date.issued2021-08-31-
dc.identifier.urihttps://repositorio.ifgoiano.edu.br/handle/prefix/2366-
dc.description.abstractRevolution 4.0 is directing agribusiness to employ more technology to help manage agricultural crops. The use of drones for imaging offers a new perspective on plant health, turning possible to verity flaws and spots that are not observed in the soil. Thus, this work aimed to evaluate the ability to correlate soybean yield obtained with previous crop imaging, under the effects of different second crop alternatives prior to soybean cultivation. A drone with onboard multispectral camera was used, flying during a 2018/2019, 2019/2020 and 2020/2021 harvest at an experimental station in the city of Rio Verde, GO. The flights were carried out in four phenological stages of soybean (R3, R5, R6 and R7) and seven phenological stages of corn and other cover crops (V1, V3, V7, V8, R1, R4 and R6) at different heights in the first, second and third harvest, respectively. The experimental design was in DC, arranging treatments in strips, totaling 14 treatments and 12 replications, totaling 168 sample plots of 3 m2. The results obtained for the NDVI index (sum, mean and median) were evaluated in relation to soybean yield corrected by the Pearson linear correlation method at the level of 1 and 5% probability by the F test. The results obtained show signs of ability to use technology to predict soybean yield. However, the second harvest systems that proceed, height of flight, phenological stage, and time of flight (harvest or off-season) directly influence the response. It is concluded that there is a need of refinement in the parameters to use the images in agricultural experimentation, to obtain robust correlations through imaging.pt_BR
dc.description.resumoA revolução 4.0 está direcionando para que o agronegócio empregue mais tecnologia para auxiliar o manejo dos cultivos agrícolas. A utilização de drones para imageamento proporciona nova perspectiva da saúde das plantas, permitindo verificar falhas e manchas que não são observadas do solo. Dessa forma, este trabalho objetivou avaliar a capacidade de correlacionar a produtividade de soja obtida com o imageamento prévio da cultura, sob efeitos de diferentes alternativas de segunda safra antecedente ao cultivo de soja. Foi utilizado um drone com câmera multiespectral embarcada, realizando voos durante a safra 2018/2019 e 2021 em uma estação experimental na cidade de Rio Verde, GO. Os voos foram realizados em quatro estádios fenológicos da soja (R3, R5, R6 e R7) e sete estádios fenológicos do milho e outras plantas de cobertura (V1, V3, V7, V8, R1, R4 e R6) e a diferentes alturas no primeira, segunda e terceira safra, respectivamente. O delineamento experimental foi em faixas, dispondo os tratamentos em faixas, totalizando 14 tratamentos e 12 repetições, somando 168 parcelas amostrais de 3 m2. Os resultados obtidos para o índice de NDVI (soma, média e mediana) foram avaliados em relação a produtividade de soja corrigida pelo método da correlação linear de Pearson ao nível de 1 e 5% de probabilidade pelo teste F. Os resultados obtidos demonstram sinais de capacidade de uso da tecnologia para predizer a produtividade da soja. Entretanto, os sistemas de segunda safra que antecede, altura do voo, estádio fenológico, e momento do voo (Safra ou safrinha) influenciam diretamente na resposta. Conclui-se que, há necessidade de refinamento nos parâmetros para uso das imagens na experimentação agrícola, com propósito de obter correlações robustas por meio do imageamento.pt_BR
dc.description.provenanceSubmitted by Rômulo Moreira Silva (romulo.moreira@estudante.ifgoiano.edu.br) on 2022-02-27T03:49:53Z No. of bitstreams: 1 Dissertação_Rômulo Moreira Silva.pdf: 1054091 bytes, checksum: c7dfa308a62d588f80e684eaf222829f (MD5)en
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dc.languageporpt_BR
dc.publisherInstituto Federal Goianopt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentCampus Rio Verdept_BR
dc.publisher.programPrograma de Pós-Graduação em Bioenergia e Grãospt_BR
dc.publisher.initialsIF Goianopt_BR
dc.relation.referencesAbdi, A. M.; Carrié, R.; Sidemo-Holm, W.; Cai, Z.; Boke-Ólen, N.; Smith, H. G.; Eklundh, L. & Ekroos, J. 2021. Biodiversity decline with increasing crop productivity in agricultural fields revealed by satellite remote sensing. Ecological Indicators, 130. Agussabti, A.; Romano, R.; Rahmaddiansyah, R. & Isa, R. M. 2020. Factors affecting risk tolerance among small-scale seasonal commodity farmers and strategies for its improvement. Heliyon, 6: 12. Aisabokhae, J. & Osazuwa, I. 2021. Radiometric mapping and spectral based classification of rocks using remote sensing data analysis: The Precambrian basement complex, NW Nigeria. Remote Sensing Applications: Society and Environment. 21. Ayamg, M.; Akaba, S.; Nyaaba, A. A. 2021. Multifaceted applicability of drones: A review. Technological Forecasting and Social Change. 167. Berger, A.; Ettlin, G.; Quincke, C. & Rodríguez-Bocca, P. 2019. Predicting the Normalized Difference Vegetation Index (NDVI) by training a crop gowth model with historical data. Computers and Eletronics in Agriculture. 161: 305-311. Conab. 2021. Acompanhamento da Safra Brasileira de Grãos, Safra 2020/21 – 10º levantamento, Brasília. De La Casa, A.; Ovando, G.; Bressanini, L.; Martínez, J.; Díaz, G. & Miranda, C. 2018. Soybean crop coverage estimation from NDVI images with different spatial resolution to evaluate yield variability in a plot. ISPRS Journal of Photogrammetry and Remote Sensing. 146: 531-547. Dutta, D.; Kundu, A.; Patel, N.R. 2012. Predicting agricultural drought in eastern Rajasthan of India using NDVI and standardized precipitation index. Geocarto International, v. 28, n.3, https://doi.org/10.1080/10106049.2012.679975 Giones, F. & Brem, A. 2017. From toys to tools: The co-evolution of technological and entrepreneurial developments in the drone industry. Business Horizons. 60, n. 6. Guan, S.; Fukami, K.; Matsunaka, H.; Okami, M.; Tanaka, R.; Nakano, H.; Sakai, T.; Nakano, K.; Ohdan, H.; Takahashi, K. 2019. Assessing Correlation of High-Resolution NDVI with Fertilizer Application Level and Yield of Rice and Wheat Crops Using Small UAVs. Remote Sens. 11, 112. https://doi.org/10.3390/rs11020112 Hovhannisyan, T.; Efendyan, P. & Vardanyan, M. 2018. Creation of a digital model of fields with application of DJI phantom 3 drone and the opportunities of its utilization in agriculture. Annals of Agrarian Science. 16, n. 2. Huuskonen, J. & Oksanen, T. 2018. Soil sampling with drones and augmented reality in precision agriculture. Computers and Electronics in Agriculture. 154. Johnson, D. M. 2014. An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sensing of Environment. 141: 116-128. Klauser, F. & Pauschinger, D. 2021. Entrepreneurs of the air: Sprayer drones as mediators of volumetric agriculture. Journal of Rural Studies. 84. Kopačková-Strnadová, V.; Koucká, L.; Jelének, J.; Lhotáková, Z. & Oulehle, F. 2021. Canopy top, height and photosysnthetic pigment estimation using parrot sequoia multispectral imagery and the unmanned aerial vehicle (UAV). Remote Sensing. 13. Kumar, K.; Sharma, A. & Tripathi, S. T. 2021. Chapter 9 – Sensors and their application. Electronic Devices, Circuits, and Systems for Biomedical Applications. Challenges and Intelligent Approach. 177-195. Academic Press. Editores: Suman Lata Tripathi, Valentina E. Balas, S. K. Mohapatra, Kolla, Bhanu Prakash, Janmenjoy Nayak. Kurbanov, R. K. & Zakharova, N. I. 2020. Application of vegetation indexes to assess the condition of crops. Agricultural Machinery and Thecnologies,. 14, n. 4. Li, D.; Li, C.; Yao, Y.; Li, M. & Liu, L. 2020. Modern imaging techniques in plant nutrition analysis: A review. Computers and Electronics in Agriculture. 174. Liaghat, S. & Balasundram, S. K. 2010. A Review: The Role of Remote Sensing in Precision Agriculture. American Journal of Agricultural and Biological Sciences. 5 (1): 50-55. Mao, H.; Zhou, L.; Ying, R. Y. & Pan, D. 2021. Time preferences and green agricultural technology adoption: Field evidence from rice farmers in China. Land Use Policy. 109. Mboga, N.; Grippa, T.; Georganos, S.; Vanhuysse, S.; Smtes, B.; Dewitte, O.; Wolff, E. & Lennert, M. 2020. Fully convolutional networks for land cover classification from historical panchromatic aerial photographs. Journal of Photogrammetry and Remote Sensing. 167. Meneses, P. R & Almeida, T. 2012. Introdução ao Processamento de Imagens de Sensoriamento Remoto. Universidade de Brasília - UNB. Brasília. 138p. Mercante, E.; Lamparelli, R.A.C.; Uribe-OPazo, M.A. & Rocha, J.V. 2010. Modelos de regressão lineares para estimativa de produtividade da soja no Oeste do Paraná, utilizando dados espectrais. Engenharia Agrícola, Jaboticabal. n.3. 30: 504-517. Nguyen, T. T.; Hoang, T. D.; Pham, M. T.; Vu, T. T.; Nguyen, T. H.; Huynh, Q. & Jo, J. 2020. Monitoring agriculture areas with satellite images and deep learning. Applied Soft Computing. 95. Novo, E. M. L. M. 2010. Sensoriamento Remoto: Princípios e Aplicações. Editora Edgard Blucher Ltda. São Paulo. 388p. Olsson, P. O; VIvekar, A.; Adler, K.; Millan, V. E. G.; Koc, A.; Alamrani, M. & Eklundh, L. 2021. Radiometric correction of multispectral uas images: Evaluating the accuracy of the parrot sequoia camera and sunshine sensor. Remote Sensing. 13. Ouzemou, J.; Harti, A. E.; Lhissou, R.; Moujahid, A. E.; Bouch, N.; Ouazzani, R. E.; Bachaoui, E. M. & Ghmari, A. E. 2018. Crop type mapping from pansharpened Landsat 8 NDVI data: A case of a highly fragmented and intensive agricultural system. Remote Sensing Applications: Society and Environment. 11. Pauschinger, D. & Klauser, F. R. 2021. The introduction of digital technologies into agriculture: Space, materiality and the public-private interacting forms of authority and expertise. Journal of Rural Studies. Peres, H. F. 2015. The development of unmanned aerial vehicles in Brazil: National interests, international challenges. Conjuntura Austral Journal of the Global South. 6, n. 31. Piedallu, C.; Chéret, V.; Denux, J. P.; Perez, V.; Azcona, J. S.; Seynave, I. & Gégout, J. C. 2019. Soil and climate differently impact NDVI patterns according to the season and the stand type. Science of the Total Environment. 651, part. 2. Pinto Júnior, J. 2019. A. Aplicação do drone na engenharia de agrimensura e para o georreferenciamento de imóveis rurais: abordagem histórico legal no Brasil. 2019, 41 f. Trabalho de Conclusão de Curso (Bacharelado em Engenharia de Agrimensura) – Centro de Ciências Agrárias, Graduação em Engenharia de Agrimensura, Universidade Federal de Alagoas, Rio Largo. Prăvălie, R.; Patriche, C.; Borrelli, P.; Panagos, P.; rosca, B.; Dumitraşcu, M.; Nita, I.; Săvulescu, I.; Birsan, M. & Bandoc, G. 2021. Arable lands under the pressure of multiple land degradation processes. A global perspective. Environment Research. 194. Puniach, E.; Gruszczński, W.; Ćwjąkaⱡa, P. & Matwij, W. 2021. Application of UAV-based orthomosaics for determination of horizontal displacement caused by underground mining. Journal of Photogrammetry and Remote Sensing. 174. Raffo, J. & Morato, R. G. 2009. O nascimento do sensoriamento remoto. Revista de Geografia. Recuero, L.; Wiese, K.; Huesca, M.; Cicuéndez, V.; Litago, J.; Tarquis, A. M. & Palacios-Orueta, A. 2019. Fallowing temporal patterns assessment in rainfed agricultural areas based on NDVI time series autocorrelation values. International Journal of Applied Earth Observation and Geoinformation. 82. Seo, B.; Lee, J.; Lee, K-D.; Hong, S. & Kang, S. 2019. Improving remotely-sensed crop monitoring by NDVI-based crop phenology estimators for corn and soybeans in Iowa and Illinois, USA. Field Crops Research. 238: 113-128. Shammi, S. A. & Meng, Q. 2021. Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling. Ecological Indicators Journal. 121: 107-124. Singh, N. & Singh, A. N. 2020. Odysseys of agriculture sensors: Current challenges and forthcoming prospects. Computers and Electronics in Agriculture. 171. Souza Neto, J. M. 2019. A produção de milho no Sudoeste Goiano: Espacialização, Cadeia Produtiva e Complementariedade. 87 f. Dissertação (Mestrado em Geografia), Universidade Federal de Goiás, Goiânia, GO. Sowden, M.; Mueller, U. & Blake, D. 2019. What temporal resolution is required for remote sensing of regional aerosol concentrations using the Himawari-8 geostationary satellite. Atmospheric Environment. 216. Svensgaard, J.; Jensen, S. M.; Christensen, S. & Rasmussen, J. 2021. The importance of spectral correction of UAV-based phenotyping with RGB cameras. Field Crops Research. 269. Tao, W.; Zhao, L.; Wang, G. & Liang, R. 2021. Review of the internet of things communication technologies in smart agriculture and challenges. Computers and Electronics in Agriculture. Terneiro, T. R.; García-Vila, M.; Gómez, J. A. & Jiménez-Berni, J. A. 2021. Using NDVI for the assessment of canopy cover in agricultural crops within modelling research. Computers and Electronics in Agriculture. 182, n.106038. Vacca, A. & Onishi, H. 2017. Drone: military weapons, surveillance or mapping tools for environmental monitoring? The need for legal framework is required. Transportation Research Procedia. 25. Valle Júnior, R. F.; Siqueira, H. E.; Valera, C. A.; Oliveira, C. F.; Fernandes, L. F. S.; Moura, J. P.; Pacheco, F. A. L. 2019. Diagnosis of degraded pastures using an improved NDVI-based remote sensing approach: An application to the Environment Protection Area of Uberaba River Basin (Minas Gerais, Brazil). Remote Sensing Applications: Society and Environment. 14. Yu, Q.; Xiang, M.; Wu, W.; Tang, H. 2019. Changes in global cropland area and cereal production: An inter-country comparison. Agriculture, Ecosystems & Environment. 269. Zefri, Y.; Sebari, I,; Hajji, H. & Aniba, G. 2021. In-depth investigation of applied digital photogrammetry to imagery-based RGB and thermal infrared aerial inspection of large-scale photovoltaic installations. Remote Sensing Applications: Society and Environment. 23. Zhang, C.; Yue, P.; Tapete, D.; Jiang, L.; Shangguan, B.; Huang, L. & Liu, G. 2020. A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images. Journal of Photogrammetry and Remote Sensing. 166. Zhang, X. W.; Liu, J. F.; Qin, Z. & Qin, F. 2019. Winter wheat identification by integrating spectral and temporal information derived from multi-resolution remote sensing data. Journal of Integrative Agriculture. 18. Zhao, J.; Zhong, Y.; Hu, X.; Wei, L. & Zhang, L. 2020. A robust spectral-spatial approach to identifying heterogeneous crops using remote sensing imagery with high spectral and spatial resolutions. Remote Sensing of Environment. 239.pt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectBrachiariapt_BR
dc.subjectBrachiariapt_BR
dc.subjectCorrelaçãopt_BR
dc.subjectCorrelationpt_BR
dc.subjectCrotaláriapt_BR
dc.subjectRattleboxpt_BR
dc.subjectExperimentaçãopt_BR
dc.subjectExperimentationpt_BR
dc.subjectMilhopt_BR
dc.subjectCornpt_BR
dc.subjectGlycine maxpt_BR
dc.subjectGlycine maxpt_BR
dc.subjectNDVIpt_BR
dc.subjectNDVIpt_BR
dc.subjectSafrinhapt_BR
dc.subjectOff-seasonpt_BR
dc.subject.cnpqCIENCIAS AGRARIASpt_BR
dc.subject.cnpqCIENCIAS AGRARIAS::AGRONOMIApt_BR
dc.titleÍNDICES DE VEGETAÇÃO NA PREDIÇÃO DE PRODUTIVIDADE DE SOJApt_BR
dc.title.alternativeVEGETATION INDEXES IN THE PREDICTION OF SOYBEAN PRODUCTIVITYpt_BR
dc.typeDissertaçãopt_BR
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