Machine learning algorithms for maize yield prediction with multispectral imagery: Assessing robustness across varied growing environments
Multispectral imagery acquired via Unmanned Aircraft Systems (UAS) can provide an on-demand and cost-effective approach to crop yield estimation. Traditional methods relying solely on vegetation indices (VIs) for yield prediction face challenges such as saturation in dense canopies, like those of ma...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
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| Series: | Science of Remote Sensing |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017225000732 |
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| Summary: | Multispectral imagery acquired via Unmanned Aircraft Systems (UAS) can provide an on-demand and cost-effective approach to crop yield estimation. Traditional methods relying solely on vegetation indices (VIs) for yield prediction face challenges such as saturation in dense canopies, like those of maize (Zea Mays L.), and inconsistencies due to environmental variability. This study investigates the potential use of canopy spectral reflectance, directly applied in conjunction with machine learning (ML) to address these limitations and produce more consistent and reliable yield predictions. The performance of reflectance, VI and hybrid (utilizing both reflectance and VIs) based ML models was evaluated for maize yield predictions across various crop growth phases. The research utilizes multispectral imagery and maize yield data from diverse growing environments, comprising seven maize planting dates tested across three field locations over two years. Among five ML algorithms tested, the Extra Trees Regressor (ETR) showed superior performance at predicting maize yield across most maize growth phases. Reflectance-based models consistently outperformed VI-based models at mid-vegetative (R2 = 0.71 vs. 0.50), late vegetative (R2 = 0.85 vs. 0.80), flowering (R2 = 0.83 vs. 0.60), and mid-reproductive (R2 = 0.83 vs. 0.74) growth phases. Hybrid models combining spectral bands and VIs achieved the greatest prediction accuracy but required more extensive data pre-processing compared to models using reflectance or VIs alone. Our findings indicate that direct incorporation of canopy reflectance bands into ML models, bypassing the complexities associated with VIs, can lead to more consistent, timely and reliable maize yield predictions across varied growing environments. |
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| ISSN: | 2666-0172 |