Integrating spectral and textural information for identifying the tasseling date of summer maize using UAV based RGB images

Guo, Yahui; Fu, Yulan; Chen, Shouzhi; Bryant, Christopher; Li, Xinxi; Senthilnath, J.; Sun, Hongyong; Wang, Shuxin; Wu, Zhaofei; Beurs, Kirsten M. de

doi:10.1016/j.jag.2021.102435

Cited by 38 publications

(41 citation statements)

References 90 publications

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“…The low-altitude remote-sensing capabilities of UAVs provide the potential for identifying crops on cultivated land owing to their low cost, flexibility, high spatial resolution, and independence in regards to climatic conditions (cloud cover, etc.). UAVs have been widely used for crop growth monitoring [32,33], yield estimation [34], and other aspects. However, the use of UAVs to quantify orchards in cultivated land is still in its infancy.…”

Section: Image Acquisition Using Uav Datamentioning

confidence: 99%

Identifying Peach Trees in Cultivated Land Using U-Net Algorithm

Zhang

2022

Land

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Non-grain production has emerged as a potential threat to grain production capacity and security in China. Agricultural products with higher economic returns are beginning to replace traditional grain crops, which have relatively low economic returns on a large scale. In this study, we proposed and verified an identification method utilizing an unmanned aerial vehicle and a U-net algorithm to distinguish peach trees in cultivated land; the overall accuracy for verification and prediction were 0.90 and 0.92, respectively. Additionally, a non-grain production index was developed to assess the degree of non-grain production in target plots. The index was 76.90% and 91.38% in the projected plots, representing a high degree of non-grain production. This combination of an identification method and non-grain production index could provide efficient tools for agricultural management to inspect peach trees in cultivated land, thus replacing field measurements to achieve significant labor savings. Furthermore, this method can provide a reference for creating high-standard farmland, sustainable development of cultivated land, and policymaking.

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Section: Image Acquisition Using Uav Datamentioning

confidence: 99%

Identifying Peach Trees in Cultivated Land Using U-Net Algorithm

Zhang

2022

Land

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“…Therefore, in this study, the texture features of GLCM and LBP were extracted from the multispectral image. As a classic texture feature extraction method, GLCM is widely used in machine vision, and its performance has been recognized by scholars [109][110][111][112][113]. In this study, 16-level grayscale and a step size of 1 were set when extracting GLCM texture features.…”

Section: Vegetation Characteristicsmentioning

confidence: 99%

Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves

Chen

Zhao

et al. 2021

Remote Sensing

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The production of high-quality tea by Camellia sinensis (L.) O. Ktze is the goal pursued by both producers and consumers. Rapid, nondestructive, and low-cost monitoring methods for monitoring tea quality could improve the tea quality and the economic benefits associated with tea. This research explored the possibility of monitoring tea leaf quality from multi-spectral images. Threshold segmentation and manual sampling methods were used to eliminate the image background, after which the spectral features were constructed. Based on this, the texture features of the multi-spectral images of the tea canopy were extracted. Three machine learning methods, partial least squares regression, support vector machine regression, and random forest regression (RFR), were used to construct and train multiple monitoring models. Further, the four key quality parameters of tea polyphenols, total sugars, free amino acids, and caffeine content were estimated using these models. Finally, the effects of automatic and manual image background removal methods, different regression methods, and texture features on the model accuracies were compared. The results showed that the spectral characteristics of the canopy of fresh tea leaves were significantly correlated with the tea quality parameters (r ≥ 0.462). Among the sampling methods, the EXG_Ostu sampling method was best for prediction, whereas, among the models, RFR was the best fitted modeling algorithm for three of four quality parameters. The R2 and root-mean-square error values of the built model were 0.85 and 0.16, respectively. In addition, the texture features extracted from the canopy image improved the prediction accuracy of most models. This research confirms the modeling application of a combination of multi-spectral images and chemometrics, as a low-cost, fast, reliable, and nondestructive quality control method, which can effectively monitor the quality of fresh tea leaves. This provides a scientific reference for the research and development of portable tea quality monitoring equipment that has general applicability in the future.

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“…The use of ML algorithms demonstrates great potential in crop yield prediction [37][38][39][40]. In particular, the use of a VIs' time series helped to derive descriptors of land surface phenology (LSP, i.e., the spatial and temporal development of the vegetated land surface) [41][42][43][44][45] such as the start of season (SOS), the peak of growing season, the stay-green duration (onset of senescence), the end of the season (EOS), and growing season length [46]. Among the available descriptors of LSP, the peak of a VI is one of the most important descriptors for crop yield prediction, such as the peak of NDVI [47] and EVI2 [17] for grain yield and the peak of GNDVI for biomass yields of perennial grass [26].…”

Section: Introductionmentioning

confidence: 99%

UAV Remote Sensing for High-Throughput Phenotyping and for Yield Prediction of Miscanthus by Machine Learning Techniques

et al. 2022

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Miscanthus holds a great potential in the frame of the bioeconomy, and yield prediction can help improve Miscanthus’ logistic supply chain. Breeding programs in several countries are attempting to produce high-yielding Miscanthus hybrids better adapted to different climates and end-uses. Multispectral images acquired from unmanned aerial vehicles (UAVs) in Italy and in the UK in 2021 and 2022 were used to investigate the feasibility of high-throughput phenotyping (HTP) of novel Miscanthus hybrids for yield prediction and crop traits estimation. An intercalibration procedure was performed using simulated data from the PROSAIL model to link vegetation indices (VIs) derived from two different multispectral sensors. The random forest algorithm estimated with good accuracy yield traits (light interception, plant height, green leaf biomass, and standing biomass) using a VIs time series, and predicted yield using a peak descriptor derived from a VIs time series with 2.3 Mg DM ha−1 of the root mean square error (RMSE). The study demonstrates the potential of UAVs’ multispectral images in HTP applications and in yield prediction, providing important information needed to increase sustainable biomass production.

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Integrating spectral and textural information for identifying the tasseling date of summer maize using UAV based RGB images

Cited by 38 publications

References 90 publications

Identifying Peach Trees in Cultivated Land Using U-Net Algorithm

Identifying Peach Trees in Cultivated Land Using U-Net Algorithm

Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves

UAV Remote Sensing for High-Throughput Phenotyping and for Yield Prediction of Miscanthus by Machine Learning Techniques

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