https://doi.org/10.4081/jae.2024.1581
Automated system for the detection of risk in agricultural sugarcane harvesting using digital image processing and deep learning
Published: 8 May 2024
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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
In recent years, significant changes have been presented in the climatological trends due to climatic change, originating negative impacts on the agricultural production, diminishing mainly the harvest efficiency. The following research proposes the optimization of the agricultural risk identification method for the prediction of the variables: temperature and precipitation; the risk identification method was developed through the Digital Image Processing technique (DIP) and Deep Learning (DL); Subsequently, with the processed images, Convolutional Neural Networks (CNN's) were developed for the detection of areas where there is a potential risk in the sugar cane crop harvest in the southeast of Veracruz in Mexico. The efficiency of CNN detects temperatures over 38ºC and the levels of precipitation under 70 millimeters. The efficiency of network detection is 0.9716 and 0.9948 for predicting the temperatures and precipitation variables, which represent a solid basis for detecting zones that depict a risk for the sugarcane harvest.
Aparicio Martín de Loeches, A, Fernández Guzmán, L. 2015. [Reconocimiento óptico de caracteres en imágenes digitales de contadores de gas].[in Spanish]. Universidad Complutense de Madrid. Available from: https://docta.ucm.es/entities/publication/2073b262-9392-4619-88a3-b6ccdcff15e0
Araus, J.L., Slafer, G., Royo, C., Serret, M.D. 2008. Breeding for yield potential and stress adaptation in cereals. Crit. Rev. Plant. Sci. 27:377-412.
Badillo-Márquez, A.E., Aguilar-Lasserre, A.A., Miranda-Ackerman, M.A., Sandoval-González, O.O., Villanueva-Vásquez, D., Posada-Gómez, R. 2021. An agent-based model-driven decision support system for assessment of agricultural vulnerability of sugarcane facing climatic change. Mathematics 9:3061.
Barnabás, B., Jäger, K., Fehér, A. 2008. The effect of drought and heat stress on reproductive processes in cereals. Plant. Cell Environ. 31:11-38.
Beer, T. 2018. The impact of extreme weather events on food security, p. 121-33. In: S. Mal, R. Singh and C. Huggel (eds.), Climate change, extreme events and disaster risk reduction. Sustainable Development Goals Series. Cham, Springer.
Cardona Arboleda, O.D., Barbat, Á.H. 1992. [Vulnerabilidad y el riesgo desde una perspectiva holística].[in Spanish]. Universitat Politècnica de Catalunya.
CONADESUCA. 2018. [Programa Nacional de La Agroindustria de La Caña de Azúcar].[in Spanish]. Available from: https://www.gob.mx/conadesuca/documentos/temas-destacados
CONAGUA. 2020. [Agroclimas].[in Spanish]. Available from: https://smn.conagua.gob.mx/es/animacion-imagenes-de-satelite?satelite=GOESEsteandnombre=Suresteandtipo=RGB
Dalal, N., Triggs, B. 2005. Histograms of oriented gradients for human detection. Proceedings 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p. 886-93.
Díez, R.P., Gómez, A.G., de Abajo Martínez, N. 2001. [Introducción a la inteligencia artificial: sistemas expertos, redes neuronales artificiales y computación evolutiva].[Book in Spanish]. Universidad de Oviedo.
de Carvalho Pinto, F.D.A., de Queiroz, D.M., Chartuni, E., Ruz, E. 2007. [Agricultura de precisión: nuevas herramientas para mejorar la gestión tecnológica en la empresa agropecuaria].[Article in Spanish]. Revista Palmas 28:29-34.
Espínola, M., Piedra-Fernández, J.A., Ayala, R., Iribarne, L., Leguizamón, S., Wang, J.Z. 2016. Simulating rainfall, water evaporation and groundwater flow in three-dimensional satellite images with cellular automata. Simul. Model. Pract. Th. 100:89-99.
Fourcade, Y. (2016). Comparing species distributions modelled from occurrence data and from expert-based range maps. Implication for predicting range shifts with climate change. Ecol. Inform. 100:8-14.
Fowler, H.J., Kilsby, C.G., O’Connell, P.E. 2003. Modeling the impacts of climatic change and variability on the reliability, resilience, and vulnerability of a water resource system. Water Resour. Res. 39:10-21.
Fredys, A.S.H., Paez, J.A., Méndez, J.C., Garrido, F.B. (2021). Identifying Robellini palm growth stages through a convolutional neuronal network. ARPN J. Engin. Appl. Sci. 16:1402-1411.
Eakin, H., García, C.G., Estrada, F., Álvarez, A.C.C. 2004. [Impactos potenciales del cambio climático en la agricultura: escenarios de producción de café para el 2050 en Veracruz (México), p. 651-60].[in Spanish]. In: Proceedings IV Congr. Asociación Española de Climatología, Santander. Universidad de Cantabria.
Horng, G.J., Liu, M.X., Chen, C.C. 2020. The smart image recognition mechanism for crop harvesting system in intelligent agriculture. IEEE Sensors J. 20:2766-2781.
IPCC. 2007. Climate Change 2007 - The Physical Science Basis: Working Group I Contribution to the Fourth Assessment Report of the IPCC. Available from: https://www.ipcc.ch/report/ar4/wg1/
Kaur, S. 2016. An automatic number plate recognition system under image processing. Int. J. Intell. Syst. Appl. 8:14-25.
Krizhevsky, A., Sutskever, I., Hinton, G.E. 2017. ImageNet classification with deep convolutional neural networks. Commun. ACM 60:84-90.
Lu, Y., Young, S. 2020. A survey of public datasets for computer vision tasks in precision agriculture. Comput. Electron. Agr. 178:105760.
Magaña-Rueda, V.O. 2013. [Guía Metodológica para la Evaluación de la Vulnerabilidad ante Cambio Climático].[in Spanish]. Mexico City, Instituto Nacional de Ecología y Cambio Climático de México (INECC).
Maguire, M.C. 2013. An analysis of specialist and non-specialist user requirements for geographic climate change information. Appl. Ergon. 44:874-885.
Mitter, H., Heumesser, C., Schmid, E. 2015. Spatial modeling of robust crop production portfolios to assess agricultural vulnerability and adaptation to climate change. Land Use Policy 46:75-90.
Monterroso Rivas, A., Gómez-Díaz, J., Lluch-Cota, S. E., Sáenz-Romero, C., Pérez-Espejo, R., Salvadeo, C.J., et al. 2015. [Sistemas de producción de alimentos y seguridad alimentaria]. In: C. Gay y Garcia, A. Cos-Gutiérrez and C.T. Peña-Ledón (Eds.), [Reporte Mexicano de Cambio Climático 2].[Report in Spanish]. Programa de Investigación en Cambio Climático.
Montoya Holguin, C., Cortés Osorio, J.A., Chaves Osorio, J.A. 2014. [Sistema automático de reconocimiento de frutas basado en visión por computador]. Ingeniare. Rev. Chil. Ingen. 224:504-516.
Nagageetha, M., Ramesh, N.V.K. 2021. A survey of agriculture crop monitoring using iot based image processing and machine learning techniques. Turk. J. Physiother. Rehabil. 32:567-571.
Paloviita, A., Kortetmäki, T., Puupponen, A., Silvasti, T. 2016. Vulnerability matrix of the food system: Operationalizing vulnerability and addressing food security. J. Clean. Prod. 100:1242-1255.
Pielke, R.A., Wilby, R., Niyogi, D., Hossain, F., Dairuku, K., Adegoke, J., et al. 2012. Dealing with complexity and extreme events using a bottom‐up, resource‐based vulnerability perspective. In: A.S. Sharma, A. Bunde, V.P. Dimri and D.N. Baker (Eds.), Extreme events and natural hazards: The complexity perspective. American Geophysical Union. pp. 345-359.
Ponce Cruz, P. 2010. [Inteligencia Artificial con aplicaciones a la ingeniería].[Book in Spanish]. Alfaomega Grupo Editor, México.
PRONAC. 2009. [Diagnóstico, Modelaje y Recomendaciones de La Fertilidad de Suelos Del Campo Cañero].[in Spanish]. Programa Nacional de la Agroindustria de la Caña de Azúcar. Available from: http://www.cndsca.gob.mx/documentoseficproductiva/8.PRONAC/PRONAC2014-2018.pdf
Pulighe, G., Lupia, F. (2016). Mapping spatial patterns of urban agriculture in Rome (Italy) using Google Earth and web-mapping services. Land Use Policy 59:49-58.t
Rosegrant, M., Koo, J., Roberton, R., Sulser, T., Zhu, T., Ringler, C., et al. 2009. [El impacto en la agricultura y los costos de adaptación].[in Spanish]. Available from: http://www.fao.org/fileadmin/user_upload/AGRO_Noticias/docs/costo%20adaptacion.pdf
SEMAR. 2015. [Estación Virtual de Imágenes Satelitáles].[in Spanish]. Available from: https://www.gob.mx/semar/acciones-y-programas/estaciones-satelitales
Semenov, M.A., Halford, N.G. 2009. Identifying target traits and molecular mechanisms for wheat breeding under a changing climate. J. Exp. Bot. 60:2791-2804.
Seminis. 2016. [Información Espacial Para El Campo Mexicano].[in Spanish]. Available from: https://www.seminis.mx/blog-informacion-geoespacial-para-el-campo-mexicano/
Sistema Nacional Meteorológico. 2020. [Agro-Climas].[in spanish]. Available from: https://cmgs.gob.mx:31/agroclimas/
Sosa Sierra, M.D.C. 2007. [Inteligencia artificial en la gestión financiera empresarial Pensamiento and Gestión].[in Spanish]. Universidad del Norte Barranquilla, Colombia. Pensamiento and Gestión 23:153-186.
Stockdale, E.A., Watson, C.A. 2009. Biological indicators of soil quality in organic farming systems. Renew. Agr. Food Syst. 24:308-318.
Temniranrat, P., Kiratiratanapruk, K., Kitvimonrat, A., Sinthupinyo, W., Patarapuwadol, S. 2021. A system for automatic rice disease detection from rice paddy images serviced via a Chatbot. Comput. Electr. Agr. 185:106156.
Torres Lima, P., Cruz Castillo, J.G., Acosta Barradas, R. 2011. [Vulnerabilidad agroambiental frente al cambio climático. Agendas de adaptación y sistemas institucionales].[Article in Spanish]. Política Cultura 36:205-232.
Vázquez, H.I.C., Ordóñez, Y.M.F., Ruiz, J.S., Maurice, M.J.E. 2015. [Enfoque metodológico para la construcción de una Geobase como apoyo a la investigación en agricultura y recursos naturales].[Article in Spanish]. Invest. Geogr. 87:39-50.
Wang, Y.Q. 2014. An analysis of the Viola-Jones face detection algorithm. Image Process. On Line 4:128-148.
Wood, E. C., Tappan, G. G., Hadj, A. 2004. Understanding the drivers of agricultural land use change in south-central Senegal. J. Arid Environ. 59:565-582.
How to Cite
Badillo-Márquez, A. E. (2024) “Automated system for the detection of risk in agricultural sugarcane harvesting using digital image processing and deep learning”, Journal of Agricultural Engineering, 55(3). doi: 10.4081/jae.2024.1581.
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