The Study of Estimating Peanut Yield Based on Drone Multispectral Remote Sensing
Journal: Modern Economics & Management Forum DOI: 10.32629/memf.v6i2.3806
Abstract
This study utilized drone aerial operations to capture multispectral remote sensing images of peanut fields during three flights on July 10, August 22, and September 20, 2024. Band calculations were performed using vegetation index formulas to derive vegetation index data. In the study area, yield data was collected based on sampling regions. Using vegetation index data as the independent variable and actual yield as the dependent variable, linear regression, curve statistics, multiple linear regression, and machine learning methods were employed to construct and validate yield estimation models. The performance of each model on the validation set was compared, leading to the identification of the optimal yield estimation model. This research provides a relatively precise approach for estimating peanut yield based on drone multispectral remote sensing, achieving real-time and rapid monitoring of peanut yield in the study area. It lays a solid foundation for establishing an economical, applicable, and efficient peanut yield estimation system and offers a reference for decision-making in precision agriculture.
Keywords
drone remote sensing; multispectral; vegetation index; peanuts; yield estimation; machine learning.
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[3] Yu N, Li L J, Schmitza N, et al. Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform[J]. Remote Sensing of Environment, 2016, 187(15): 91-101.
[4] Berni J.,Zarco-tejada P.J.,Suarez L,et al.Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle[J].IEEE Transactions on Geoscience&Remote Sensing,2009,47(3):722-738.
[5] Zhang Chunhua,Kovacs John-M.The application of small unmanned aerial systems for precision agriculture:a review[J].Precision Agriculture2012,13(6):693-712.
[6] Everaerts Jurgen.Remote Sensing and Spatial Information Sciences[J]:CRC Press,2008:117-124.
[7] Li B, Liu R Y, Liu S H, et al. Monitoring vegetation coverage variation of winter wheat by low-altitude UAV remote sensing system[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(13): 6.
[8] Zhang Z J, Li A N, Bian J H, et al. Estimating aboveground biomass of grassland in Zoige by visible vegetation index derived from unmanned aerial vehicle image[J]. Remote Sensing Technology and Application, 2016, 31(1): 51-62.
[9] Tian Z K, Fu Y Y, Liu S H, et al. Rapid crops classification based on UAV low-altitude remote sensing[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013(7): 109-116.
[10] Zhou M W, Liu Q H, Liu Q, et al. A method for classification by fusing full-waveform airborne laser scanning data and aerial images[J]. Remote Sensing Technology and Application, 2010, 25(6): 821-827.
[11] Wang S, Guo Z X, Liang X Y, et al. Study on yield estimation model of tobacco vegetation index based on UAV multi-spectral remote sensing data[J]. Shanxi Agricultural Sciences, 2021, 49(2): 195-203
[12] Luo M S, Jing Y S, Xiong S W. A prediction model of rice meteorological yield based on neural networks optimized by genetic algorithm[J]. Journal of Meteorological Science, 2012, 32(6): 665-670.
[13] Bolton Douglas-K.,Friedl Mark-A.Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics[J].Agricultural&Forest Meteorology2013,173:74-84.
[14] Son Nguyen-Thanh,Chen Chi-Farn,Chen Cheng-Ru,et al.A Phenology-Based Classification of Time-Series MODIS Data for Rice Crop Monitoring in Mekong Delta,Vietnam[J].Remote Sensing,2013,6(1):135-156.
[15] Han W T, Peng X S, Zhang L Y, et al. Summer maize yield estimation based on vegetation index derived from multi-temporal UAV remote sensing[J]. Transactions of the Chinese Society of Agricultural Machinery, 2020, 51(1): 148-155.
[16] Guan Y Y, Wei Z Y, Wang Y F, et al. Effects of humic acid on maize yield, nitrogen use efficiency and soil properties[J]. Journal of Henan Institute of Science and Technology (Natural Science Edition), 2022, 50(3): 7-15.
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