Detection and Classification of Agave angustifolia Haw Using Deep Learning Models

Idarh Matadamas (), Erik Zamora and Teodulfo Aquino-Bolaños
Additional contact information
Idarh Matadamas: Centro Interdisciplinario de Investigación para el Desarrollo Integral Regional, Unidad Oaxaca, Instituto Politécnico Nacional, Hornos No. 1003, Colonia Noche Buena, Municipio de Santa Cruz Xococotlán 71230, Oaxaca, Mexico
Erik Zamora: Centro de Investigación en Computación, Instituto Politécnico Nacional, Avenida Juan de Dios Batiz esquina Miguel Othón de Mendizábal, Colonia Nueva Industrial Vallejo, Gustavo A. Madero, Ciudad de México 07738, Mexico
Teodulfo Aquino-Bolaños: Centro Interdisciplinario de Investigación para el Desarrollo Integral Regional, Unidad Oaxaca, Instituto Politécnico Nacional, Hornos No. 1003, Colonia Noche Buena, Municipio de Santa Cruz Xococotlán 71230, Oaxaca, Mexico

Agriculture, 2024, vol. 14, issue 12, 1-12

Abstract: In Oaxaca, Mexico, there are more than 30 species of the Agave genus, and its cultivation is of great economic and social importance. The incidence of pests, diseases, and environmental stress cause significant losses to the crop. The identification of damage through non-invasive tools based on visual information is important for reducing economic losses. The objective of this study was to evaluate and compare five deep learning models: YOLO versions 7, 7-tiny, and 8, and two from the Detectron2 library, Faster-RCNN and RetinaNet, for the detection and classification of Agave angustifolia plants in digital images. In the town of Santiago Matatlán, Oaxaca, 333 images were taken in an open-air plantation, and 1317 plants were labeled into five classes: sick, yellow, healthy, small, and spotted. Models were trained with a 70% random partition, validated with 10%, and tested with the remaining 20%. The results obtained from the models indicate that YOLOv7 is the best-performing model, in terms of the test set, with a mAP of 0.616, outperforming YOLOv7-tiny and YOLOv8, both with a mAP of 0.606 on the same set; demonstrating that artificial intelligence for the detection and classification of Agave angustifolia plants under planting conditions is feasible using digital images.

Keywords: artificial intelligence; computer vision; pattern recognition; convolutional networks; agave plants (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/12/2199/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/12/2199/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:12:p:2199-:d:1534749

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().