Estimation of the quantity of peanut seedling plant based on UAV hyperspectral imagery
[Purpose]Peanut is one of the important oil crops in china.The ability to accurately identify individual peanut plants within a field and obtain precise plant counts is essential for optimizing field management practices and ultimately enhancing yield.[Method]This research was conducted in a peanut planting base located in the Dengta Basin of Heyuan City,Guangdong Province,China.Hyperspectral images(HSI)covering the peanut fields were acquired using a UAV platform.Recognizing the challenges of directly identifying individual plants within dense canopies,we developed a novel approach to estimate plant counts by predicting the average peanut coverage within the canopy.To achieve this,we first conducted a thorough analysis the spectral characteristics of peanut plants across the 390-1000 nm spectral range.This analysis aimed to pinpoint specific spectral bands that effectively differentiated peanut plants from the surrounding background,aiding in the segmentation of peanut plants from the imagery.A comprehensive set of 20 features was then meticulously extracted from the hyperspectral data.These features encompassed both vegetation indices(VIs),which capture unique spectral reflectance patterns of vegetation,and morphological parameters(MPs),which quantify the shape and structural characteristics of plant canopies.To streamline model development and focus on the most informative features,Pearson correlation analysis was employed to select the 12 most relevant features from the initial set of 20.With the selected features in hand,three powerful machine learning algorithms,namely partial least square regression(PLSR),random forest regression(RFR),and support vector regression(SVR),were trained and rigorously evaluated to predict the average peanut coverage,which could then be translated into plant count estimates.Each algorithm was trained and evaluated using three distinct feature combinations:spectral indices alone,morphological parameters alone,and a combined set of both spectral and morphological features.This comprehensive approach allowed us to assess the individual contributions of spectral and morphological features,as well as their synergistic potential in predicting peanut coverage.[Result](1)In the visible light region,green plants and background areas showed significant differences with no overlapping regions,and the spectral reflectance at 671 nm effectively distinguished peanut plants from the background.(2)Among the three algorithms used for model training(PLSR,RFR,and SVR),the SVR model exhibited the highest estimation accuracy.(3)In single-feature models,the model based on population morphological feature indices demonstrated higher precision compared to models constructed using optimal spectral indices.In multi-feature models,models combining spectral features and population morphological features showed improved accuracy and stability,with R2 values of 0.56,0.58,and 0.75 for the PLSR,RFR,and SVR models,respectively.The SVR model achieved the lowest RMSE of 0.29 plants.[Conclusion]The model constructed using the SVR algorithm,spectral features,and population morphological features effectively monitors peanut coverage levels under field conditions,providing a quick and efficient technical approach for real-time detection of peanut plant numbers using unmanned aerial vehicles,and offers a basis for precise peanut production management.
UAVpeanuthyperspectral imaginggroup characteristicsnumber of plantscoverage
NETL
NSTL
万方数据
