Zayd Eliud Rangel-Nava
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Zayd Eliud Rangel-Nava has a total of 13 co-authors. Here are the top co-authors by number of shared publications:
- Alejandro Isabel Luna-Maldonado
Department of Agricultural and Food Engineering, Faculty of Agriculture, Autonomous University of Nuevo Leon, Nuevo Leon, Mexico
2 shared publications - Ajay Kumar
Department of Biosystems and Agricultural Engineering, Oklahoma State University, Stillwater, OK 74078, USA
1 shared publication - Gerardo Méndez-Zamora
Universidad Autónoma de Nuevo León, Facultad de Agronomía
1 shared publication - Horacio Abdiel Rodríguez-Garza
Department of Agricultural and Food Engineering, Faculty of Agronomy, Autonomous University of Nuevo León, Francisco Villa S/N, Ex-Hacienda El Canadá, General Escobedo 66050, Nuevo León, Mexico
1 shared publication - Héctor Flores-Breceda
Department of Agricultural and Food Engineering, Faculty of Agronomy, Autonomous University of Nuevo León, Francisco Villa S/N, Ex-Hacienda El Canadá, General Escobedo 66050, Nuevo León, Mexico
1 shared publication - Jesús Alberto Lara-Reyes
Universidad Autonóma de Nuevo León
1 shared publication
Latest Publications
- Open Access
Latent Salinity Stress Detection in Opuntia ficus-indica Using Hyperspectral Imaging and a 3D-CNN Framework
Salinity stress remains a major bottleneck for agriculture in arid regions. While Opuntia ficus-indica is known for its resilience, its young cladodes maintain a misleadingly healthy visual appearance and stable biomass even under heavy saline pressure, making traditional vegetation indices and standard statistics unreliable for early diagnosis. The objective of this study was to develop a non-destructive phenotyping framework for the early detection of latent salinity stress in young Opuntia cladodes. Controlled experiments were conducted using hyperspectral data cubes (400–1000 nm) acquired from plants exposed to six distinct salinity levels ranging from 2 to 21 dS m−1. Our methodology integrates these high-dimensional spatial–spectral data with a tailor-made 3D Convolutional Neural Network (3D-CNN). Seven physiological vegetation indices—NDVI, PRI, WI, PSRI, MCARI, SIPI, and NDRE were extracted to track sub-clinical shifts and processed as a volumetric depth dimension within the network to preserve spatial–spectral integrity. The optimized 3D-CNN framework achieved a validation accuracy of 99.7% and a weighted F1-score of 99.1%, delivering 100% precision at critical stress thresholds (13 and 21 dS m−1). Spatial confidence maps (Softmax > 0.95) further confirmed the high reliability of the diagnostic output. Requiring a training duration of approximately 8 s, this framework provides a robust basis for precision early-warning irrigation systems to sustain Opuntia cultivation in challenging environments.
Open AccessLatent Salinity Stress Detection in Opuntia ficus-indica Using Hyperspectral Imaging and a 3D-CNN Framework
Salinity stress remains a major bottleneck for agriculture in arid regions. While Opuntia ficus-indica is known for its resilience, its young cladodes maintain a misleadingly healthy visual appearance and stable biomass even under heavy saline pressure, making traditional vegetation indices and standard statistics unreliable for early diagnosis. The objective of this study was to develop a non-destructive phenotyping framework for the early detection of latent salinity stress in young Opuntia cladodes. Controlled experiments were conducted using hyperspectral data cubes (400–1000 nm) acquired from plants exposed to six distinct salinity levels ranging from 2 to 21 dS m−1. Our methodology integrates these high-dimensional spatial–spectral data with a tailor-made 3D Convolutional Neural Network (3D-CNN). Seven physiological vegetation indices—NDVI, PRI, WI, PSRI, MCARI, SIPI, and NDRE were extracted to track sub-clinical shifts and processed as a volumetric depth dimension within the network to preserve spatial–spectral integrity. The optimized 3D-CNN framework achieved a validation accuracy of 99.7% and a weighted F1-score of 99.1%, delivering 100% precision at critical stress thresholds (13 and 21 dS m−1). Spatial confidence maps (Softmax > 0.95) further confirmed the high reliability of the diagnostic output. Requiring a training duration of approximately 8 s, this framework provides a robust basis for precision early-warning irrigation systems to sustain Opuntia cultivation in challenging environments.
- Open Access
- 31 October 2021
- Published by Universidad Autonoma de Aguascalientes in Investigación y Ciencia de la Universidad Autónoma de Aguascalientes
- No. 84
Inclusión de harina y aceite de semilla de moringa como sustitutos y estabilizantes de grasa en pastas cárnicas
Esta investigación evaluó el efecto de la adición de harina con aceite (HSMcA) y sin aceite de semilla de moringa (HSMsA) y aceite de semilla de moringa (ASM) como sustitutos y estabilizantes de grasa dorsal (GD) en pastas cárnicas (PaC) no cocidas y cocidas de salchichas Frankfurt. T1: PaC control + 100% GD; T2: PaC + 50% GD + 50% HSMcA; T3: PaC + 50% GD + 50% HSMsA; T4: PaC + 0% GD + 50% HSMcA + 50% HSMsA; T5: PaC + 50% GD + 50% ASM. La pérdida por cocción, fluido total expulsado y grasa expulsada resultaron diferentes (P < 0.05) en la estabilidad de las PaC no cocidas. En PaC cocidas T3 mostró valores más altos en a*, Chroma, Hue (tonalidad), capacidad antioxidante, fuerza de corte y elasticidad; T2 obtuvo más dureza y gomosidad.
- 31 October 2021
- Published by Universidad Autonoma de Aguascalientes in Investigación y Ciencia de la Universidad Autónoma de Aguascalientes
- No. 84
Open AccessInclusión de harina y aceite de semilla de moringa como sustitutos y estabilizantes de grasa en pastas cárnicas
Esta investigación evaluó el efecto de la adición de harina con aceite (HSMcA) y sin aceite de semilla de moringa (HSMsA) y aceite de semilla de moringa (ASM) como sustitutos y estabilizantes de grasa dorsal (GD) en pastas cárnicas (PaC) no cocidas y cocidas de salchichas Frankfurt. T1: PaC control + 100% GD; T2: PaC + 50% GD + 50% HSMcA; T3: PaC + 50% GD + 50% HSMsA; T4: PaC + 0% GD + 50% HSMcA + 50% HSMsA; T5: PaC + 50% GD + 50% ASM. La pérdida por cocción, fluido total expulsado y grasa expulsada resultaron diferentes (P < 0.05) en la estabilidad de las PaC no cocidas. En PaC cocidas T3 mostró valores más altos en a*, Chroma, Hue (tonalidad), capacidad antioxidante, fuerza de corte y elasticidad; T2 obtuvo más dureza y gomosidad.