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

Vol. 55 No. 3 (2024)
Published: 8 May 2024
Digital image processing technique, deep learning, agricultural production, harvest efficiency
Abstract Views: 245
PDF: 177
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Authors

Alina Evelyn Badillo-Márquez
Division of Research and Postgraduate Studies, Tecnológico Nacional de México; Instituto Tecnologico de Orizaba, Mexico.
Jonathan J. Cid-Galiot
Division of Research and Postgraduate Studies, Tecnológico Nacional de México; Instituto Tecnologico de Orizaba, Mexico.
Rubén Posada-Gómez
Division of Research and Postgraduate Studies, Tecnológico Nacional de México; Instituto Tecnologico de Orizaba, Mexico.
Alberto Alfonso Aguilar-Lasserre
albertoaal@hotmail.com
Division of Research and Postgraduate Studies, Tecnológico Nacional de México; Instituto Tecnologico de Orizaba, Mexico.

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.

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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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