Application of instrumentation in cotton cultivation: systematic literature review
DOI:
https://doi.org/10.33448/rsd-v11i9.30581Keywords:
Agriculture; Precision Agriculture; Agricultural Instrumentation; Agricultural machinery.Abstract
The present work aims to carry out a Systematic Review of the Literature, in order to understand the use of instrumentation applied to cotton cultivation. For this purpose, a search was carried out in four databases and the StArt software was used for data analysis and selection of works. For a total of 1,914 works obtained from the databases, 30 were selected based on selection criteria for full reading. In the end, it was concluded that the works have several applications, mainly related to the classification of cotton for the industry, in addition, the work also pointed out a great possibility of investment and application of instrumentation in cotton culture at various stages of its production chain.
References
AMPA – Associação Mato-Grossense Dos Produtores De Algodão (2021). História do Algodão. Disponível em: https://ampa.com.br/historia-doalgodao/. Acesso em 24 out. 2021.
Baio, F. H. R et al (2017). Financial analysis of the investment in precision agriculture techniques on cotton crop. Engenharia Agrícola, v. 37, p. 838-847, 2017. DOI: 10.1590/1809-4430Eng.Agric.v37n4p838-847/2017
Baio, F. H. R. et al (2019). In situ remote sensing as a strategy to predict cotton seed yield. Bioscience Journal, v. 35, n. 6, 2019. DOI: 10.14393/BJ-v35n6a2019-42261
Baio, F. H. R et al (2018). Relationship between cotton productivity and variability of NDVI obtained by Landsat images. Bioscience Journal, v. 34, n. 6, 2018. DOI: 10.14393/BJ-v34n6a2018-39583
Bronson, K. F. et al (2021). Use of an ultrasonic sensor for plant height estimation in irrigated cotton. Agronomy Journal, v. 113, n. 2, p. 2175-2183, 2021. DOI: 10.1002/agj2.20552
Butler, S. et al (2020). Making the Cotton Replant Decision: A Novel and Simplistic Method to Estimate Cotton Plant Population from UAScalculated NDVI. The Journal of Cotton Science, 24:104-111, 2020.
Cao, L. et al (2017). Potential dermal and inhalation exposure to imidacloprid and risk assessment among applicators during treatment in cotton field in China. Science of the total environment, v. 624, p. 1195-1201, 2018. DOI: 10.1016/j.scitotenv.2017.12.238
CONAB – Companhia Nacional De Abastecimento (2021). Acompanhamento da safra brasileira de grãos. Safra 2020/21, 7º levantamento. Disponível em: https://www.conab.gov.br/info-agro/safras/. Acesso em 24 out. 2021.
Coêlho, J. D. (2021). Algodão: Produção e Mercados. Caderno Setorial, Banco do Nordeste. Disponível em: https://www.bnb.gov.br/s482dspace/bitstream/123456789/808/1/2021_CDS_ 166.pdf. Acesso em 24 out. 2021.
Chen, X. et al (2020). Evaluation of a new irrigation decision support system in improving cotton yield and water productivity in an arid climate. Agricultural Water Management, v. 234, p. 106139, 2020. DOI: 10.1016/j.agwat.2020.106139
Delhom, C.D. et al (2020). Engineering And Ginning. The Journal of Cotton Science, 24:189-196, 2020.
Feng, A. et al (2020). Yield estimation in cotton using UAV-based multi-sensor imagery. Biosystems Engineering, v. 193, p. 101-114, 2020. DOI: 10.1016/j.biosystemseng.2020.02.014
Fue, K. et al (2020). Autonomous Navigation of a Center-Articulated and Hydrostatic Transmission Rover using a Modified Pure Pursuit Algorithm in a Cotton Field. Sensors, v. 20, n. 16, p. 4412, 2020. DOI: 10.3390/s20164412
Gaikwad, S. V. et al (2021). An innovative IoT based system for precision farming. Computers and Electronics in Agriculture, v. 187, p. 106291, 2021. DOI: 10.1016/j.compag.2021.106291
Ibragimov, N et al (2021). Cotton irrigation scheduling improvements using wetting front detectors in Uzbekistan. Agricultural Water Management, v. 244, p. 106538, 2021. DOI: 10.1016/j.agwat.2020.106538
Larson, J. A. et al (2020). Effects of landscape, soils, and weather on yields, nitrogen use, and profitability with sensor-based variable rate nitrogen management in cotton. Agronomy, v. 10, n. 12, p. 1858, 2020. DOI: 10.3390/agronomy10121858
LV, Y et al (2020). Cotton Appearance Grade Classification Based on Machine Learning. Procedia Computer Science, v. 174, p. 729-734, 2020. DOI: 10.1016/j.procs.2020.06.149
Martin, D. E.& Latheef, M. A. (2018). Active optical sensor assessment of spider mite damage on greenhouse beans and cotton. Experimental and Applied Acarology, v. 74, n. 2, p. 147-158, 2018. DOI: 10.1007/s10493-018-0213-7
Martin, D. E.& Latheef, M. A. (2017). Remote sensing evaluation of two-spotted spider mite damage on greenhouse cotton. JoVE (Journal of Visualized Experiments), n. 122, p. e54314, 2017. DOI: 10.3791/54314
Papadopoulos, A. V. et al (2018). Weed mapping in cotton using groundbased sensors and GIS. Environmental monitoring and assessment, v. 190, n. 10, p. 1-17, 2018. DOI: 10.1007/s10661-018-6991-x
PelletieR, M. G.& Wanjura, J D.& Holt, G. A. (2019). Electronic design of a cotton harvester yield monitor calibration system. AgriEngineering, v. 1, n. 4, p. 523-538, 2019. DOI: 10.3390/agriengineering1040038
Pelletier, M. G. & Wanjura, J. D.& Holt, G. A. (2019). Embedded micro-controller software design of a cotton harvester yield monitor calibration system. AgriEngineering, v. 1, n. 4, p. 485-495, 2019. DOI: 10.3390/agriengineering1040035
Pelletier, M. G. & Wanjura, J. D.& Holt, G. A. (2019). Man-MachineInterface Software Design of a Cotton Harvester Yield Monitor Calibration System. AgriEngineering, v. 1, n. 4, p. 511-522, 2019. DOI: 10.3390/agriengineering1040037
Podestà, I. D. (2021). Valor Bruto da Produção está estimado em R$ 1,109 trilhões para este ano. Ministério da Agricultura, Pecuária e Abastecimento. Disponível em <https://www.gov.br/agricultura/pt-br/assuntos/noticias/valor-bruto-daproducao-esta- estimado-em-r-1-109-trilhao-para-este-an>. Acesso em 24 out. 2021.
Rozenstein, O. et al. Estimating cotton water consumption using a time series of Sentinel-2 imagery. Agricultural water management, v. 207, p. 44-52, 2018. DOI: 10.1016/j.agwat.2018.05.017
Rozenstein, O. et al. Validation of the cotton crop coefficient estimation model based on Sentinel-2 imagery and eddy covariance measurements. Agricultural Water Management, v. 223, p. 105715, 2019. DOI: 10.1016/j.agwat.2019.105715
Souza, H. B. & Baio, F. H. & Neves, D C. (2017). Using passive and active multispectral sensors on the correlation with the phenological indices of cotton. Engenharia Agrícola, v. 37, p. 782-789, 2017. DOI: 10.1590/1809-4430- Eng.Agric.v37n4p782-789/2017
Suassuna, N. D.& Silva, J. C. D.& Bettiol (2019), W. Uso do Trichoderma na cultura do algodão. In: Meyer, M. C.& Mazaro, S. M.& Silva, J. C. Trichoderma: Uso na agricultura. 1 ed. Brasília -DF: Embrapa, 2019, p. 361 – 380. Disponível em: https://www.researchgate.net/profile/Gabriel-Moura-Mascarin/publication/340331300_
Industrial_production_of_Trichoderma_Chapter_08__in_Portuguese/links/5e8 3fa9d299bf1 30796dc569/Industrial-production-of-Trichoderma-Chapter-08in Portuguese.pdf#page=361. Acesso em 24 out. 2021
IBGE – Instituto Brasileiro de Geografia e Estatística (2021). Produção de Algodão herbáceo. Disponível em: https://www.ibge.gov.br/explica/producao- agropecuaria/algodao-herbaceo/br. Acesso em 24 out. 2021
Thompson, Alison L. et al (2019). Comparing nadir and multi-angle view sensor technologies for measuring in-field plant height of upland cotton. Remote Sensing, v. 11, n. 6, p. 700, 2019. DOI: 10.3390/rs11060700
Thorp, Kelly R. et al (2017). Cotton irrigation scheduling using a crop growth model and FAO-56 methods: Field and simulation studies. Transactions of the ASABE, v. 60, n. 6, p. 2023- 2039, 2017. DOI: 10.13031/trans.12323
Thorp, Kelly R. et al (2019). Novel methodology to evaluate and compare evapotranspiration algorithms in an agroecosystem model. Environmental Modelling & Software, v. 119, p. 214-227, 2019. DOI: 10.1016/j.envsoft.2019.06.007
Trevisan, Rodrigo Gonçalves et al (2018). Management of Plant Growth Regulators in Cotton Using Active Crop Canopy Sensors. Agriculture, v. 8, n. 7, p. 101, 2018. DOI: 10.3390/agriculture8070101
Uddin, Jasim et al (2018). Smart automated furrow irrigation of cotton. Journal of Irrigation and Drainage Engineering, v. 144, n. 5, p. 04018005, 2018. DOI:10.1061/(ASCE)IR.1943- 4774.0001282
USDA – United States Department Of Agriculture (2021). Cotton: World Markets and Tarde. Disponível em: https://apps.fas.usda.gov/psdonline/app/index.html#/app/downloads. Acesso em 24 out. 2021.
Yu, Jianming et al (2019). Nitrogen Consumption and Productivity of Cotton under Sensor‐based Variable‐rate Nitrogen Fertilization. Agronomy Journal, v. 111, n. 6, p. 3320-3328, 2019. DOI: 10.2134/agronj2019.03.0197
Zare, Ehsan et al (2020). Two-dimensional time-lapse imaging of soil wetting and drying cycle using EM38 data across a flood irrigation cotton field. Agricultural Water Management, v. 241, p. 106383, 2020. DOI:10.1016/j.agwat.2020.106383
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Copyright (c) 2022 Natalia de Lima Batista; Thiago Santana Aranha; Kassandra Sussi Mustafé Oliveira; Mariana Matulovic da Silva Rodrigueiro; Mario Mollo Neto; Paulo Sérgio Barbosa dos Santos
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