https://doi.org/10.4081/jae.2024.1590
Intelligent system based on a satellite image detection algorithm and a fuzzy model for evaluating sugarcane crop quality by predicting uncertain climatic parameters
Published: 9 July 2024
Abstract Views: 38
PDF: 21
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
The increase in uncertain weather affects agriculture, impacting crop yield and quality, mainly due to the interaction of climatic variables such as temperature, wind speed, and humidity. In addition, soil erosion and nutrient loss are regional problems aggravated by inadequate agricultural practices in developing sugarcane agriculture. The present research proposes an Intelligent System based on a detection algorithm and a fuzzy model to estimate the quality of the sugarcane crop and the probability of the presence of pests and diseases through the prediction of uncertain variables. Wind speed, cloudiness, humidity, and thermal amplitude were considered variables of interest because parameters out of control of these variables generate a state of thermal stress, triggering pests and diseases that reduce crop quality and sugar production. This research uses geospatial information to simplify the exchange of information through a detection algorithm using real-time satellite images and a fuzzy model to estimate crop quality and prevent climate change-related problems. The variables humidity and cloudiness determine sugarcane quality as they are related to crop phenology and the probability that the crop will develop pests and diseases. In contrast, the intelligent system showed a correlation of over 93% for predicting the variables of interest.
Badillo-Márquez, A.E., A.A. Aguilar-Lasserre, M.A. Miranda-Ackerman, O.O. Sandoval-González, D. Villanueva-Vásquez, et al. 2021. An agent-based model-driven decision support system for assessment of agricultural vulnerability of sugarcane facing climatic change. Mathematics 9(23). doi: 10.3390/math9233061.
DOI: https://doi.org/10.3390/math9233061
Canata, T.F., M.C.F. Wei, L.F. Maldaner, and J.P. Molin. 2021. Sugarcane yield mapping using high-resolution imagery data and machine learning techniques. Remote Sens (Basel) 13(2). doi: 10.3390/rs13020232.
DOI: https://doi.org/10.3390/rs13020232
CONADESUCA. 2018. Programa Nacional de la Agroindustria de la Caña de Azúcar. Programa Nacional de la Agroindustria de la Caña de Azúcar: 1–49. https://www.gob.mx/conadesuca/documentos/temas-destacados.
CONADESUCA. 2020. Comite Nacional para el Desarrollo Sustentable de la Caña de Azúcar. https://www.gob.mx/conadesuca/.
Cravero, A., and S. Sepúlveda. 2021. Use and adaptations of machine learning in big data—applications in real cases in agriculture. Electronics (Switzerland) 10(5). doi: 10.3390/electronics10050552.
DOI: https://doi.org/10.3390/electronics10050552
Gopikrishnan, S., G. Srivastava, and P. Priakanth. 2022. Improving sugarcane production in saline soils with Machine Learning and the Internet of Things. Sustainable Computing: Informatics and Systems 35. doi: 10.1016/j.suscom.2022.100743.
DOI: https://doi.org/10.1016/j.suscom.2022.100743
Grosan, C., and A. Abraham. 2011. Fuzzy Expert Systems. Intelligent Systems Reference Library. doi: 10.1007/978-3-642-21004-4_9.
DOI: https://doi.org/10.1007/978-3-642-21004-4
Hasan, S., J.C.R. Smart, R. Hay, and S. Rundle-Thiele. 2021. Changing fertilizer management practices in sugarcane production: Cane grower survey insights. Land (Basel) 10(2). doi: 10.3390/land10020098.
DOI: https://doi.org/10.3390/land10020098
INEGI. 2021. Information by entity. Climate of Veracruz. https://cuentame.inegi.org.mx/monografias/informacion/ver/territorio/clima.aspx?tema=me&e=30#:~:text=Los%20climas%20que%20predominan%20en,templado%2C%20localizado%20tambi%C3%A9n%20en%20las (accessed 15 May 2023).
INEGI. 2023. Censo Agropecuario. México.
InfoAgro. 2022a. Cane Seedling. Parts of the Cane. Need for genetic resistance. https://mexico.infoagro.com/mejora-genetica-en-variedades-de-cana/ (accessed 13 February 2023).
InfoAgro. 2022b. Interacting factors in sugarcane cultivation. Genetic improvement in sugarcane varieties. https://mexico.infoagro.com/mejora-genetica-en-variedades-de-cana/ (accessed 30 January 2024).
Ljung, G.M., J. Ledolter, and B. Abraham. 2014. George Box’s contributions to time series analysis and forecasting. Appl Stoch Models Bus Ind 30(1). doi: 10.1002/asmb.2016.
DOI: https://doi.org/10.1002/asmb.2016
Meza-Palacios, R., A.A. Aguilar-Lasserre, L.F. Morales-Mendoza, J.O. Rico-Contreras, L.H. Sánchez-Medel, et al. 2020. Decision support system for NPK fertilization: a solution method for minimizing the impact on human health, climate change, ecosystem quality and resources. J Environ Sci Health A Tox Hazard Subst Environ Eng 55(11). doi: 10.1080/10934529.2020.1787012.
DOI: https://doi.org/10.1080/10934529.2020.1787012
Nedjah, N., P.R.S.S. Sandres, and L. De Macedo Mourelle. 2014. Customizable hardware design of fuzzy controllers applied to autonomous car driving. Expert Syst Appl. doi: 10.1016/j.eswa.2014.05.032.
DOI: https://doi.org/10.1016/j.eswa.2014.05.032
Ortiz Laurel, H., S. Salgado García, M. Castelán Estrada, and S. Córdova Sánchez. 2012. Perspectivas de la cosecha de la caña de azúcar cruda en México. Rev Mex De Cienc Agric (4).
Parodi, M., L. Herrera, M. Matar, L. Barrea, M. Mechini, et al. 2014. Redes neuronales artificiales aplicadas al análisis de datos en ingeniería ambiental e impacto ambiental. Energeia 12(12).
Popke, J., S. Curtis, and D.W. Gamble. 2016. A social justice framing of climate change discourse and policy: Adaptation, resilience and vulnerability in a Jamaican agricultural landscape. Geoforum. doi: 10.1016/j.geoforum.2014.11.003.
DOI: https://doi.org/10.1016/j.geoforum.2014.11.003
PRONAC. 2009. Programa Nacional de la Agroindustria de la Caña de Azúcar. Diagnóstico, modelaje y recomendaciones de la fertilidad de suelos del campo cañero: 72. http://www.cndsca.gob.mx/documentoseficproductiva/8. PRONAC/PRONAC 2014-2018.pdf.
PRONAC, P.N. de la A. de la C. de Azúcar., S. de I.A. y Pesquera. SIAP, and C. de P. COLPOS. 2009. Digitalización del Campo Cañero en México para Alcanzar la Agricultura de Precisión de la Caña de Azúcar. Desarrollo de un Modelo Integral de Sistema de Información Geográfica y Edáfica.
Rodríguez-Aguirre, E., A.E. Badillo-Márquez, A.A. Aguilar-Lasserre, and R. Flores-Asis. 2023. An agent-based model to evaluate agricultural vulnerability and risk facing climate change in strawberry production. Crop Sci. doi: 10.1002/csc2.21100.
DOI: https://doi.org/10.1002/csc2.21100
Ruiz-Jaramillo, J.I., B. Vargas-Leitón, S. Abarca-Monge, and H.G. Hidalgo. 2019. Heat stress effect on dairy cattle production in Costa Rica. Agronomia Mesoamericana 30(3). doi: 10.15517/am.v30i3.35984.
DOI: https://doi.org/10.15517/am.v30i3.35984
SAGARPA. 2012. México: el sector agropecuario ante el desafío del cambio climático. Sagarpa I: 439.
Sandhu, H.S., M.P. Singh, R.A. Gilbert, F. Subiros-Ruiz, R.W. Rice, et al. 2017. Harvest management effects on sugarcane growth, yield and nutrient cycling in Florida and Costa Rica. Field Crops Res 214. doi: 10.1016/j.fcr.2017.09.002.
DOI: https://doi.org/10.1016/j.fcr.2017.09.002
Semenov, M.A., and N.G. Halford. 2009. Identifying target traits and molecular mechanisms for wheat breeding under a changing climate. Journal of Experimental Botany
DOI: https://doi.org/10.1093/jxb/erp164
Shokouhyar, S., S. Seifhashemi, H. Siadat, and M.M. Ahmadi. 2019. Implementing a fuzzy expert system for ensuring information technology supply chain. Expert Syst 36(1). doi: 10.1111/exsy.12339.
DOI: https://doi.org/10.1111/exsy.12339
SIAP, and SADER. 2024. Estadística de Producción Agrícola. Servicio de Información Agroalimentaria y Pesquera, Secretaria de Agricultura y Desarrollo Rural. México.
Sivanandam, S.N., S. Sumathi, and S.N. Deepa. 2007. Introduction to fuzzy logic using MATLAB.
DOI: https://doi.org/10.1007/978-3-540-35781-0
SMN, and CONAGUA. 2023a. Atmospheric Environmental Monitoring. Hazard Mapping System. https://smn.conagua.gob.mx/es/observando-el-tiempo/monitoreo-atmosferico-ambiental (accessed 9 August 2023).
SMN, and CONAGUA. 2023b. Sistema Meteorológico Nacional. https://smn.conagua.gob.mx/es/observando-el-tiempo/imagenes-de-satelite (accessed 16 August 2023).
Wang, Y.-Q. 2014. An Analysis of the Viola-Jones Face Detection Algorithm. Image Processing On Line. doi: 10.5201/ipol.2014.104.
DOI: https://doi.org/10.5201/ipol.2014.104
Zadeh, L.A. 2008. Is there a need for fuzzy logic? Inf Sci (N Y) 178(13). doi: 10.1016/j.ins.2008.02.012.
DOI: https://doi.org/10.1016/j.ins.2008.02.012
How to Cite
Badillo-Márquez, A. E., Pardo-Escandón, I., Aguilar-Lasserre, A. A., Moras-Sánchez, C. G. and Flores-Asis, R. (2024) “Intelligent system based on a satellite image detection algorithm and a fuzzy model for evaluating sugarcane crop quality by predicting uncertain climatic parameters”, Journal of Agricultural Engineering. doi: 10.4081/jae.2024.1590.
Copyright (c) 2024 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.