New UAV-Based Method Enhances Wheat Uniformity Monitoring and Yield Prediction

A research team has developed an innovative method to quantify wheat uniformity using unmanned aerial vehicle (UAV) imaging technology. This method estimates leaf area index (LAI), SPAD, fractional vegetation cover, and plant height, calculating 20 uniformity indices throughout the growing season. Pielou’s index of LAI showed the strongest correlation with yield and biomass. This approach enables effective monitoring of wheat uniformity, offering new insights for yield and biomass prediction, and has potential applications in crop management and future wheat breeding programs.

Wheat is a crucial global crop, but current population growth, extreme weather, and climate change have increased demands on wheat production. Uniform population structure is key for high yields, but uneven field conditions lead to competition among plants, preventing uniformity. Traditional methods for measuring uniformity are labor-intensive and inefficient. Current research focuses on spatial uniformity of individual plants and lacks multi-trait assessments across growth stages.

A study (DOI: 10.34133/plantphenomics.0191) published in Plant Phenomics on 18 Jun 2024, aims to develop a comprehensive method for assessing wheat uniformity throughout its growth stages, using UAV-based phenotyping to evaluate its impact on yield and biomass.

This research utilized UAV-based imaging technology to estimate wheat agronomic parameters: SPAD, LAI, and plant height (PH). The BPNN model demonstrated high accuracy for LAI (R²=0.889) and SPAD (R²=0.804), and the PH estimation from 3D point clouds also showed strong accuracy (R²=0.812). These accurate estimations provided a foundation for calculating uniformity indices. The study revealed that uniformity indices for LAI, SPAD, FVC, and PH varied dynamically across growth stages, with indices generally stabilizing after heading. Furthermore, correlation analyses uncovered strong correlations between specific indices, such as LJ for LAI, and yield (r=-0.760) and biomass (r=-0.801). Multiple linear regression models that incorporated these uniformity indices outperformed models based on mean values, resulting in improved accuracy for yield (R²=0.616) and biomass (R²=0.798) predictions. This method effectively monitors wheat uniformity and provides insights for enhancing crop yield and biomass estimation.

According to the study’s lead researcher, Dong Jiang, “The proposed uniformity monitoring method can be used to effectively evaluate the temporal and spatial variations in wheat uniformity and can provide new insights into the prediction of yield and biomass.”

In summary, this study developed a UAV-based method to monitor wheat uniformity. Models using uniformity indices demonstrated higher accuracy than those using mean values, offering valuable insights for yield and biomass prediction. Looking ahead, different uniformity indices can improve crop management and breeding. Future research should explore the relationship between uniformity and productivity across growth stages and validate this method for other crops to enhance agricultural practices.

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References

DOI

10.34133/plantphenomics.0191

Original Source URL

https://doi.org/10.34133/plantphenomics.0191

Authors

Yandong Yang ^1†, Qing Li^2†, Yue Mu ^1*, Haitao Li¹, Hengtong Wang ²,Seishi Ninomiya^1,3*, and Dong Jiang^2*

Affications

1 Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Center for Modern CropProduction co-sponsored by Province and Ministry, State Key Laboratory of Crop Genetics and GermplasmEnhancement and Utilization, Nanjing 210095, China.

2 College of Agriculture, National TechniqueInnovation Center for Regional Wheat Production, Key Laboratory of Crop Ecophysiology, Ministry ofAgriculture, Nanjing Agricultural University, Nanjing 210095, China.

3 Graduate School of Agriculturaland Life Sciences, The University of Tokyo, Nishi-Tokyo, Tokyo 188-0002, Japan.

Funding information

This research was supported by the National Key R&D Program of China (no. 2022YFE0116200), the “JBGS” Project of Seed Industry Revitalization in Jiangsu Province (JBGS [2021] 007), the National Natural Science Foundation of China (32272213, 32030076, U1803235, and 32021004), the Fundamental Research Funds for the Central Universities (XUEKEN2023013), and the National Key Research and Development Program of China (2020YFE0202900).

About Plant Phenomics

Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal’s Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.