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Article

Plant Stress Detection Using a Three-Dimensional Analysis from a Single RGB Image †

by
Madaín Pérez-Patricio
1,
J. A. de Jesús Osuna-Coutiño
1,*,
German Ríos-Toledo
1,
Abiel Aguilar-González
2,
J. L. Camas-Anzueto
1,
N. A. Morales-Navarro
1,
J. Renán Velázquez-González
1 and
Luis Ángel Cundapí-López
1
1
Department of Science, Tecnológico Nacional de México/IT Tuxtla Gutiérrez, Carr. Panamericana 1080, Tuxtla Gutierrez 29050, Chiapas, Mexico
2
Department of Computer Science, Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro 1, Cholula 72840, Puebla, Mexico
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in Proceedings of the Mexican Conference on Pattern Recognition.
Sensors 2024, 24(23), 7860; https://doi.org/10.3390/s24237860
Submission received: 1 October 2024 / Revised: 7 November 2024 / Accepted: 15 November 2024 / Published: 9 December 2024
(This article belongs to the Special Issue Computer Vision and Pattern Recognition for Advanced Smart Agriculture Solutions)
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Abstract

Plant stress detection involves the process of Identification, Classification, Quantification, and Prediction (ICQP) in crop stress. Numerous approaches exist for plant stress identification; however, a majority rely on expert personnel or invasive techniques. While expert employees demonstrate proficiency across various plants, this approach demands a substantial workforce to ensure the quality of crops. Conversely, invasive techniques entail leaf dismemberment. To overcome these challenges, an alternative is to employ image processing to interpret areas where plant geometry is observable, eliminating the dependency on skilled labor or the need for crop dismemberment. However, this alternative introduces the challenge of accurately interpreting ambiguous image features. Motivated by the latter, we propose a methodology for plant stress detection using 3D reconstruction and deep learning from a single RGB image. For that, our methodology has three steps. First, the plant recognition step provides the segmentation, location, and delimitation of the crop. Second, we propose a leaf detection analysis to classify and locate the boundaries between the different leaves. Finally, we use a Deep Neural Network (DNN) and the 3D reconstruction for plant stress detection. Experimental results are encouraging, showing that our approach has high performance under real-world scenarios. Also, the proposed methodology has 22.86% higher precision, 24.05% higher recall, and 23.45% higher F1-score than the 2D classification method.
Keywords: plant stress detection; plant stress phenotyping; deep learning; visual pattern plant stress detection; plant stress phenotyping; deep learning; visual pattern

Share and Cite

MDPI and ACS Style

Pérez-Patricio, M.; Osuna-Coutiño, J.A.d.J.; Ríos-Toledo, G.; Aguilar-González, A.; Camas-Anzueto, J.L.; Morales-Navarro, N.A.; Velázquez-González, J.R.; Cundapí-López, L.Á. Plant Stress Detection Using a Three-Dimensional Analysis from a Single RGB Image. Sensors 2024, 24, 7860. https://doi.org/10.3390/s24237860

AMA Style

Pérez-Patricio M, Osuna-Coutiño JAdJ, Ríos-Toledo G, Aguilar-González A, Camas-Anzueto JL, Morales-Navarro NA, Velázquez-González JR, Cundapí-López LÁ. Plant Stress Detection Using a Three-Dimensional Analysis from a Single RGB Image. Sensors. 2024; 24(23):7860. https://doi.org/10.3390/s24237860

Chicago/Turabian Style

Pérez-Patricio, Madaín, J. A. de Jesús Osuna-Coutiño, German Ríos-Toledo, Abiel Aguilar-González, J. L. Camas-Anzueto, N. A. Morales-Navarro, J. Renán Velázquez-González, and Luis Ángel Cundapí-López. 2024. "Plant Stress Detection Using a Three-Dimensional Analysis from a Single RGB Image" Sensors 24, no. 23: 7860. https://doi.org/10.3390/s24237860

APA Style

Pérez-Patricio, M., Osuna-Coutiño, J. A. d. J., Ríos-Toledo, G., Aguilar-González, A., Camas-Anzueto, J. L., Morales-Navarro, N. A., Velázquez-González, J. R., & Cundapí-López, L. Á. (2024). Plant Stress Detection Using a Three-Dimensional Analysis from a Single RGB Image. Sensors, 24(23), 7860. https://doi.org/10.3390/s24237860

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