Improving estimation of LAI dynamic by fusion of morphological and vegetation indices based on UAV imagery

Qiao, Liyan; Gao, Dehua; Zhao, Ruomei; Tang, Weijie; An, Lulu; Li, Minzan; Sun, Hong

doi:10.1016/j.compag.2021.106603

Cited by 71 publications

(36 citation statements)

References 24 publications

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“…The reason for this is that the potato growth was most closely related to the nutritional status during the S1, and the accuracy of extraction of each MP was higher, which more effectively reflected the changing status of PNC. During the S2, H and CC tended to saturate, and no longer changed significantly ( Wan et al., 2020 ; Qiao et al., 2022 ), and the link between MPs and PNC weakened. The growth of potato was primarily reproductive during the S3, and the N in plant continued to transfer to the tuber.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, the product of extracted H and CC was defined as the canopy volume ( Qiao et al., 2022 ) to explore the association of multiple MPs with the potato PNC, and the CV was calculated as shown in Equation 5.…”

Section: Experiments and Methodsmentioning

confidence: 99%

“…Finally, the ratio of number of pixels of vegetation to the total number of pixels in each plot was calculated, which was the CC value of that plot based on UAV extraction. In this study, the product of extracted H and CC was defined as the canopy volume (Qiao et al, 2022) to explore the association of multiple MPs with the potato PNC, and the CV was calculated as shown in Equation 5.…”

Section: Extraction Of Morphological Parametersmentioning

confidence: 99%

“…Similar to the red-edge and near-infrared bands, MPs can provide structural information closely related to crop growth. The combination of H and CC extracted from UAV digital images has been shown to significantly improve the accuracy of VIs in estimating crop growth parameters, such as yield, biomass, and the leaf area index ( Lu et al., 2019 ; Wan et al., 2020 ; Qiao et al., 2022 ; Shu et al., 2022 ). Furthermore, most of the growth parameters described above are closely related to the N nutrient status of crops.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of the nitrogen content of potato plants based on morphological parameters and visible light vegetation indices

Fan

Feng²,

Jin³

et al. 2022

Front. Plant Sci.

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Plant nitrogen content (PNC) is an important indicator to characterize the nitrogen nutrition status of crops, and quickly and efficiently obtaining the PNC information aids in fertilization management and decision-making in modern precision agriculture. This study aimed to explore the potential to improve the accuracy of estimating PNC during critical growth periods of potato by combining the visible light vegetation indices (VIs) and morphological parameters (MPs) obtained from an inexpensive UAV digital camera. First, the visible light VIs and three types of MPs, including the plant height (H), canopy coverage (CC) and canopy volume (CV), were extracted from digital images of the potato tuber formation stage (S1), tuber growth stage (S2), and starch accumulation stage (S3). Then, the correlations of VIs and MPs with the PNC were analyzed for each growth stage, and the performance of VIs and MPs in estimating PNC was explored. Finally, three methods, multiple linear regression (MLR), k-nearest neighbors, and random forest, were used to explore the effect of MPs on the estimation of potato PNC using VIs. The results showed that (i) the values of potato H and CC extracted based on UAV digital images were accurate, and the accuracy of the pre-growth stages was higher than that of the late growth stage. (ii) The estimation of potato PNC by visible light VIs was feasible, but the accuracy required further improvement. (iii) As the growing season progressed, the correlation between MPs and PNC gradually decreased, and it became more difficult to estimate the PNC. (iv) Compared with individual MP, multi-MPs can more accurately reflect the morphological structure of the crop and can further improve the accuracy of estimating PNC. (v) Visible light VIs combined with MPs improved the accuracy of estimating PNC, with the highest accuracy of the models constructed using the MLR method (S1: R2 = 0.79, RMSE=0.27, NRMSE=8.19%; S2:R2 = 0.80, RMSE=0.27, NRMSE=8.11%; S3: R2 = 0.76, RMSE=0.26, NRMSE=8.63%). The results showed that the combination of visible light VIs and morphological information obtained by a UAV digital camera could provide a feasible method for monitoring crop growth and plant nitrogen status.

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Section: Discussionmentioning

confidence: 99%

Section: Experiments and Methodsmentioning

confidence: 99%

Section: Extraction Of Morphological Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimation of the nitrogen content of potato plants based on morphological parameters and visible light vegetation indices

Fan

Feng²,

Jin³

et al. 2022

Front. Plant Sci.

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“…In order to realize the segmentation of the buckwheat canopy, the original RGB image was converted into an ExG gray-scale image, and then the K-means algorithm was used to cluster the image pixels into buckwheat canopy and background (“0” represents the background, “1” represents the canopy). Then, morphological open and close operations [ 18 ] were applied for noise removal and holes filling, and the final binary image was the segmentation result of buckwheat canopy.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Buckwheat Maturity in UAV-RGB Images Based on Recursive Feature Elimination Cross-Validation: A Case Study in Jinzhong, Northern China

Zheng

et al. 2022

Plants

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Buckwheat is an important minor grain crop with medicinal and edible functions. The accurate judgment of buckwheat maturity is beneficial to reduce harvest losses and improve yield. With the rapid development of unmanned aerial vehicle (UAV) technology, it has been widely used to predict the maturity of agricultural products. This paper proposed a method using recursive feature elimination cross-validation (RFECV) combined with multiple regression models to predict the maturity of buckwheat in UAV-RGB images. The images were captured in the buckwheat experimental field of Shanxi Agricultural University in Jinzhong, Northern China, from September to October in 2021. The variety was sweet buckwheat of “Jinqiao No. 1”. In order to deeply mine the feature vectors that highly correlated with the prediction of buckwheat maturity, 22 dimensional features with 5 vegetation indexes, 9 color features, and 8 texture features of buckwheat were selected initially. The RFECV method was adopted to obtain the optimal feature vector dimensions and combinations with six regression models of decision tree regression, linear regression, random forest regression, AdaBoost regression, gradient lifting regression, and extreme random tree regression. The coefficient of determination (R2) and root mean square error (RMSE) were used to analyze the different combinations of the six regression models with different feature spaces. The experimental results show that the single vegetation index performed poorly in the prediction of buckwheat maturity; the prediction result of feature space “5” combined with the gradient lifting regression model performed the best; and the R2 and RMSE were 0.981 and 1.70 respectively. The research results can provide an important theoretical basis for the prediction of the regional maturity of crops.

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Rape seedling density estimation in‐field conditions based on improved multi‐column convolutional neural network

et al. 2023

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Early‐stage rape seedling density is closely related to yield estimation, growth diagnosis, cultivated area statistics, and field management. Currently, manual sampling and counting, which are inefficient and inaccurate, are heavily relied upon to estimate rape seedling density. Computer vision techniques have emerged as a promising solution to the automation of this task, as digital images have become more commonplace. Farmland field environments, however, face many challenges, including scale variation, denseness, and background occlusion. An improved multi‐column convolutional neural network, called seedling rape density prediction network (SRDPNet), has been proposed in this study to resolve the issues related to accurate density estimation and counting of rape seedlings in complex farmland scenarios. Based on the multi‐column convolutional attention encoder, filters of different sizes are used to capture the basic feature of rape seedlings at various scales. The channel attention and position attention modules are introduced into branches to alleviate the impact of low counting accuracy caused by background error and growth state differences. The SRDPNet was validated using the seedling rapeseed plant counting (SRPC) dataset created in this study. The experimental results showed that the SRDPNet demonstrated high accurate counting performance for the SRPC dataset with a high coefficient of determination (R2 = 0.97396) and mean absolute error (MAE = 3.26, mean square error = 4.56), which are superior to that of the comparison method. SRDPNet can effectively solve the visual challenges of rape seedlings in complex farmland scenes and improve the robustness for complex visual variations.

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Improving estimation of LAI dynamic by fusion of morphological and vegetation indices based on UAV imagery

Cited by 71 publications

References 24 publications

Estimation of the nitrogen content of potato plants based on morphological parameters and visible light vegetation indices

Estimation of the nitrogen content of potato plants based on morphological parameters and visible light vegetation indices

Prediction of Buckwheat Maturity in UAV-RGB Images Based on Recursive Feature Elimination Cross-Validation: A Case Study in Jinzhong, Northern China

Rape seedling density estimation in‐field conditions based on improved multi‐column convolutional neural network

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