Application of unmanned aerial systems in precision agriculture and plant breeding



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Unmanned aerial systems (UAS) are a promising remote sensing technology in smart agriculture. The overall objective of this study was to evaluate the effectiveness of UAS technology in the application of precision agriculture and plant breeding. UAS remote sensing was applied in estimating surface soil water content (SWC), correcting cotton yield monitor data, assessing water stress of cotton cultivars, and predicting cotton yield. SWC is a major determinant of crop production, and accurately retrieving SWC plays a crucial role in effective water management. The objective of chapter II was to develop an algorithm to retrieve SWC by integrating soil texture into a vegetation index derived from UAS multispectral and thermal images. The Normalized Difference Vegetation Index (NDVI) and surface temperature (Ts) derived from the UAS multispectral and thermal images were employed to construct the Temperature Vegetation Dryness Index (TVDI) using the trapezoid model. Soil texture was incorporated into the trapezoid model based on the relationship between soil texture and the lower and upper limits of SWC to form the Texture Temperature Vegetation Dryness Index (TTVDI). The results showed that the TTVDI had better performance in estimating SWC compared to the TVDI, with an increase in R2 by 14.5% and 14.9%, and a decrease in RMSE by 46.1% and 10.8% for the 2019 and 2020 samples, respectively. The yield monitor is one of the most adopted precision agriculture technologies. The objective of chapter III was to evaluate the application of UAS images in improving yield monitor data for cotton (Gossypium hirsutum L.) yield estimation. The results showed that the estimated yield using cotton unit coverage (CUC) from UAS images was correlated with the yield monitor data (R2 = 0.57 and 0.75) in two fields. The multiple linear regression (MLR) models had better performance in correcting the yield monitor data compared to the simple linear regression (SLR) model, with an increase in R2 by 10.3% to 14.3%, a decrease in root-mean-square deviation (RMSE) by 9.7%, and 29.4%, and a decrease in relative Absolute Error (RAE) by 13.5% and 24.7% in two fields. After correction, the spatial autocorrelation of cotton yield increased in two fields, as indicated by the increase of Moran`s I value by 4.5% and 19.7% in the two fields, respectively. Efficiently monitoring and quantifying the response of crop cultivars to water stress is critical in developing high-performance genotypes in water-limited environments. The objective of chapter IV was to assess water stress in cotton using UAS images to select water stress-resistant cultivars in plant breeding. Vegetation indices (VIs) and crop water stress index (CWSI) derived from UAS images were applied to assess water stress in eight cotton cultivars. The VIs effectively differentiated cultivars in the middle and late seasons, while CWSI detected cultivar differences in the late growing season. VIs had a strong positive relationship with cotton yield starting from the mid-growing season in two years (R2 ranged from 0.90 to 0.95). The successful classification of cultivars using UAS images provides critical information for selecting drought-resistant cultivars under low-level irrigation rates (e.g., 30% ET), while it is a good indicator for selecting high-yielding cultivars under minimal water stress (e.g., 90% ET) in cotton breeding. Accurate prediction of crop yield is critical to improving the selection of water stress-resistant cultivars. The objective of chapter V was to assess the relationship between cotton lint yield and VIs to determine the optimal period for cotton yield prediction over the growing season. Individual VIs and multi-temporal VIs derived from UAS images were applied to build the relationship with cotton yield. The results showed that multi-temporal VIs has a stronger relationship with cotton yield. The analysis based on multi-temporal VIs showed that EVI performed better in yield prediction than other VIs. The optimal period for cotton yield prediction was in the mid-late stages of cotton growth, as suggested by high R2 (0.92), low RMSE (229.8 kg ha-1), and RAE% (37.4%). This study demonstrated the applicability of UAS images in estimating surface SWC and improving the yield monitor data in precision agriculture. In addition, UAS images have great potential in assessing the performance of cotton cultivars and predicting yield in response to water stress, which helps to improve breeding efficiency in selecting water stress-resistant cotton cultivars.

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Unmanned Aerial Systems, Precision Agriculture, Plant Breeding, Yield Monitor, Cotton, Yield Prediction