Artificial intelligence and Multispectral Imaging Technologies for Assessing Climate Impact on Maize Yield: A Literature Review

Carlos R. Martínez- Martínez; Felipe Machorro- Ramos; Laura Rodríguez- Peralta

doi:10.37077/25200860.2025.38.2.38

Article Details

Artificial intelligence and Multispectral Imaging Technologies for Assessing Climate Impact on Maize Yield: A Literature Review

Authors:

Carlos R. Martínez- Martínez | Felipe Machorro- Ramos | Laura Rodríguez- Peralta

Publisher:

College of Agriculture, University of Basrah, Iraq

Main Topics:

multispectral imaging

Journal:

Basrah Journal of Agricultural Sciences Vol.38 Issue 2, pp.505-526

Document Type:

Article

Publication Date:

12/2025

Document Language:

English

Document ID (Org No.):

2001363845

pISSN:

2520-0860

DOI:

10.37077/25200860.2025.38.2.38

Keywords:

Abstract:

 Maize production in Central America and Mexico is particularly susceptible to climate variability, especially the El Niño-Southern Oscillation (ENSO), which causes drought and irregular rainfall. This systematic literature review analyzed 111 studies, focusing on AI algorithms and multispectral imaging technologies for maize health monitoring and yield prediction. Evidence indicated that Random Forest has been repeatedly used for image segmentation and the prediction of crop and climate numerical data, often outperforming other methods. Deep learning models (ResNet50 and YOLOv3) demonstrated high accuracy (>95%) in detecting maize diseases by image recognition. Ensemble approaches, including XGBoost and hybrid models, delivered highly accurate yield predictions (R² > 0.9). However, despite these advancements, challenges persist in applying them to smallholder farms due to regional climatic and agricultural constraints. This review highlights the potential of AI-driven precision agriculture to inform which approaches can be effectively used in local adaptations.

Article Details

Artificial intelligence and Multispectral Imaging Technologies for Assessing Climate Impact on Maize Yield: A Literature Review

Authors:

Carlos R. Martínez- Martínez | Felipe Machorro- Ramos | Laura Rodríguez- Peralta

Publisher:

College of Agriculture, University of Basrah, Iraq

Main Topics:

multispectral imaging

Journal:

Basrah Journal of Agricultural Sciences Vol.38 Issue 2, pp.505-526

Document Type:

Article

Publication Date:

12/2025

Document Language:

English

Document ID (Org No.):

2001363845

pISSN:

2520-0860

DOI:

10.37077/25200860.2025.38.2.38

Precision agriculture

remote-sensing

yield prediction

drought stress

deep learning

artificial intelligence

multispectral imaging

Issues

Article Details

Authors: Carlos R. Martínez- Martínez | Felipe Machorro- Ramos | Laura Rodríguez- Peralta

Publisher: College of Agriculture, University of Basrah, Iraq

Main Topics: multispectral imaging

Journal: Basrah Journal of Agricultural Sciences Vol.38 Issue 2, pp.505-526

Document Type: Article

Publication Date: 12/2025

Document Language: English

Document ID (Org No.): 2001363845

pISSN: 2520-0860

DOI: 10.37077/25200860.2025.38.2.38

Issues

Authors:

Carlos R. Martínez- Martínez | Felipe Machorro- Ramos | Laura Rodríguez- Peralta

Publisher:

College of Agriculture, University of Basrah, Iraq

Main Topics:

multispectral imaging

Journal:

Basrah Journal of Agricultural Sciences Vol.38 Issue 2, pp.505-526

Document Type:

Article

Publication Date:

12/2025

Document Language:

English

Document ID (Org No.):

2001363845

pISSN:

2520-0860

DOI:

10.37077/25200860.2025.38.2.38