Improving efficiency of ground-truth data collection for UAV-based rice growth estimation models: investigating the effect of sampling size on model accuracy

ABSTRACT

One of the bottlenecks in the development of UAV-based crop growth estimation models has been the need for ground-truth data collection through plant sampling. Thus, we investigated the viability of utilizing datasets derived from reduced sampling size for the development of growth estimation models, with the aim of enhancing the efficiency of ground-truth data collection. Koshihikari, a Japonica rice variety, was grown with various fertilizer conditions and transplanting dates. Once a week from transplanting to the heading date, aerial RGB and multispectral images were collected with a UAV. Subsequently, four adjacent hills from each plot were harvested, and above-ground biomass (AGB) and leaf area index (LAI) measurements were taken for each hill. For each hill, the ground-measured data was linked to the UAV-derived features (plant height, vegetation indices, and texture indices). Three datasets were compiled using the values of single hill, the average values of two adjacent hills, and those of four adjacent hills. Models estimating AGB and LAI from UAV-derived features were developed with each dataset using single regression and machine learning (ML) algorithms, and the prediction accuracy was compared among the three datasets. The prediction accuracy of the single regression models was similar across all datasets. In addition, it was demonstrated that the dataset based on single-harvested hills can contribute to improving the prediction accuracy of the ML models. Our results indicated that the dataset based on single-harvested hills was sufficiently reliable for model development and can be utilized, consequently allowing for more efficient ground-truth data collection.

GRAPHICAL ABSTRACT

KEYWORDS:

• Biomass estimation
• UAV
• machine learning
• rice
• ground-truth data

Acknowledgments

We would like to thank Mr. Koji Matsukawa, a technical staff member of the Field Museum Honmachi of Tokyo University of Agriculture and Technology, for his help with experiments in the paddy field.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

All authors contributed to the study. K.K. supervised the project. T.Y. and K.K. designed the experiments. T.Y., K.S., and K.K. conducted the experiments. K.S. organized the collected data. T.Y. analyzed the data, visualized the results, and wrote the main manuscript. All authors reviewed and approved the manuscript.

Data availability statement

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/1343943X.2023.2299641.

Additional information

Funding

This work was supported by funding from Science and Technology Research Partnership for Sustainable Development (SATREPS), Japan Science and Technology Agency (JST)/Japan International Cooperation Agency (JICA) (Grant No. JP-MJSA1907), Japan Society for the Promotion of Science (JSPS) KAKENHI (Grant No. JP20H02968 and JP21H02314), and JSPS Grant-in-Aid for JSPS Fellows (Grant No. 22J13389).