Optimal machine learning algorithms and UAV multispectral imagery for crop phenotypic trait estimation: a comprehensive review and meta-analysis
The rapid development of unmanned aerial vehicles (UAVs) and imaging technologies has opened new research avenues for precision agriculture, particularly in the context of plant phenotyping where their utilization has been intensive over the last decade. This review focuses on the interplay of machi...
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IOP Publishing
2025-01-01
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| Series: | Environmental Research Communications |
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| Online Access: | https://doi.org/10.1088/2515-7620/ade84f |
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| author | Adama Ndour Gerald Blasch João Valente Bisrat Haile Gebrekidan Tesfaye Shiferaw Sida |
| author_facet | Adama Ndour Gerald Blasch João Valente Bisrat Haile Gebrekidan Tesfaye Shiferaw Sida |
| author_sort | Adama Ndour |
| collection | DOAJ |
| description | The rapid development of unmanned aerial vehicles (UAVs) and imaging technologies has opened new research avenues for precision agriculture, particularly in the context of plant phenotyping where their utilization has been intensive over the last decade. This review focuses on the interplay of machine learning, UAV-based multispectral imagery and plant phenotyping. We systematically reviewed the current literature to catalog and assess the variety of machine learning methodologies applied to multispectral UAV data for the prediction of key phenotypic traits such as biomass, yield and nitrogen. In this study, we conducted a comprehensive meta-analysis to analyze the relationship between the machine learning model performance and variables such crop type, the type of aerial phenotyping platform, the phenological stage, etc A trait-based comparison of the efficiency and popularity of machine learning algorithms was conducted. Our findings showed that the multiple linear regression is the most effective model in predicting biomass while artificial neural networks showed up as the top performing algorithm in determining nitrogen content. Random forest was identified as the most popular algorithm in estimating those key phenotypic traits. The best combinations of UAV and sensors that significantly enhance model performance for predicting critical agronomic traits were thoroughly examined. Results highlighted, for instance, that pairing the DJI 2 UAV with Micasense sensor led to better machine learning performance in predicting biomass while Parrot Sequoia was identified as the most efficient multispectral sensor to phenotype leaf nitrogen content. Ultimately, the challenges and future research prospects of UAV-based predictions related to the phenotype data variability, the choice of UAV platform, the model complexity and interpretability are discussed. Since previous studies described the broad applications of UAVs and sensors in agriculture, this review aimed to provide a targeted, systematic and quantitative analysis of optimal use of machine learning algorithms and UAV-based multispectral imagery for plant phenotyping. |
| format | Article |
| id | doaj-art-fb6ac6fee2d24dd28678f6f2784ed444 |
| institution | DOAJ |
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| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
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| series | Environmental Research Communications |
| spelling | doaj-art-fb6ac6fee2d24dd28678f6f2784ed4442025-08-20T03:15:35ZengIOP PublishingEnvironmental Research Communications2515-76202025-01-017707200210.1088/2515-7620/ade84fOptimal machine learning algorithms and UAV multispectral imagery for crop phenotypic trait estimation: a comprehensive review and meta-analysisAdama Ndour0https://orcid.org/0000-0001-5188-3921Gerald Blasch1https://orcid.org/0000-0002-8265-0052João Valente2https://orcid.org/0000-0002-6241-4124Bisrat Haile Gebrekidan3Tesfaye Shiferaw Sida4International Maize and Wheat Improvement Center (CIMMYT), Addis Ababa, EthiopiaInternational Maize and Wheat Improvement Center (CIMMYT), Addis Ababa, EthiopiaCentre for Automation and Robotics (CAR), Spanish National Research Council (CSIC) , 28006 Madrid, SpainInternational Maize and Wheat Improvement Center (CIMMYT), Addis Ababa, EthiopiaInternational Maize and Wheat Improvement Center (CIMMYT), Addis Ababa, EthiopiaThe rapid development of unmanned aerial vehicles (UAVs) and imaging technologies has opened new research avenues for precision agriculture, particularly in the context of plant phenotyping where their utilization has been intensive over the last decade. This review focuses on the interplay of machine learning, UAV-based multispectral imagery and plant phenotyping. We systematically reviewed the current literature to catalog and assess the variety of machine learning methodologies applied to multispectral UAV data for the prediction of key phenotypic traits such as biomass, yield and nitrogen. In this study, we conducted a comprehensive meta-analysis to analyze the relationship between the machine learning model performance and variables such crop type, the type of aerial phenotyping platform, the phenological stage, etc A trait-based comparison of the efficiency and popularity of machine learning algorithms was conducted. Our findings showed that the multiple linear regression is the most effective model in predicting biomass while artificial neural networks showed up as the top performing algorithm in determining nitrogen content. Random forest was identified as the most popular algorithm in estimating those key phenotypic traits. The best combinations of UAV and sensors that significantly enhance model performance for predicting critical agronomic traits were thoroughly examined. Results highlighted, for instance, that pairing the DJI 2 UAV with Micasense sensor led to better machine learning performance in predicting biomass while Parrot Sequoia was identified as the most efficient multispectral sensor to phenotype leaf nitrogen content. Ultimately, the challenges and future research prospects of UAV-based predictions related to the phenotype data variability, the choice of UAV platform, the model complexity and interpretability are discussed. Since previous studies described the broad applications of UAVs and sensors in agriculture, this review aimed to provide a targeted, systematic and quantitative analysis of optimal use of machine learning algorithms and UAV-based multispectral imagery for plant phenotyping.https://doi.org/10.1088/2515-7620/ade84fUAVcomprehensive reviewfield crop phenotypingprecision agricultureartificial intelligencepredictive modeling |
| spellingShingle | Adama Ndour Gerald Blasch João Valente Bisrat Haile Gebrekidan Tesfaye Shiferaw Sida Optimal machine learning algorithms and UAV multispectral imagery for crop phenotypic trait estimation: a comprehensive review and meta-analysis Environmental Research Communications UAV comprehensive review field crop phenotyping precision agriculture artificial intelligence predictive modeling |
| title | Optimal machine learning algorithms and UAV multispectral imagery for crop phenotypic trait estimation: a comprehensive review and meta-analysis |
| title_full | Optimal machine learning algorithms and UAV multispectral imagery for crop phenotypic trait estimation: a comprehensive review and meta-analysis |
| title_fullStr | Optimal machine learning algorithms and UAV multispectral imagery for crop phenotypic trait estimation: a comprehensive review and meta-analysis |
| title_full_unstemmed | Optimal machine learning algorithms and UAV multispectral imagery for crop phenotypic trait estimation: a comprehensive review and meta-analysis |
| title_short | Optimal machine learning algorithms and UAV multispectral imagery for crop phenotypic trait estimation: a comprehensive review and meta-analysis |
| title_sort | optimal machine learning algorithms and uav multispectral imagery for crop phenotypic trait estimation a comprehensive review and meta analysis |
| topic | UAV comprehensive review field crop phenotyping precision agriculture artificial intelligence predictive modeling |
| url | https://doi.org/10.1088/2515-7620/ade84f |
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