Estimation of winter wheat LAI and SPAD based on fusion of texture features and vegetation indices from UAV image
DOI:
https://doi.org/10.25165/ijabe.v18i4.8969Keywords:
LAI, SPAD, texture features, vegetation indices, stepwise selection, PCAAbstract
Accurate and efficient quantitative estimation of leaf area index (LAI) and soil and plant analyzer development (SPAD) in winter wheat is significant for field management decisions and yield prediction. This study compared the performance of three linear regression techniques: multiple linear regression (MLR), ridge regression (RR), and partial least squares regression (PLSR) and three machine learning algorithms: back-propagation neural networks(BP), random forests (RF) and support vector regression (SVR) with spectral vegetation indices (VIs), texture features (TEs) and their combinations extracted from UAV RGB images. A total of 36 estimation models were constructed, which included 24 models based on single datasets (VIs-based and TEs-based), and 12 models based on data fusion (VIs+TIs-based). The results revealed that combin VIs and TEs improved the accuracy of LAI and SPAD estimation for wheat compared to using VIs or TEs alone. Moreover, different data dimensionality reduction methods include principal component analysis (PCA), and stepwise selection (ST) were used to improve the accuracy of LAI and SPAD estimation.The results showed that ST, PCA and ST_PCA methods have different impacts on the accuracy of estimating crop parameters with the combination of VIS and TEs, where ST_PCA is effective in dealing with high-dimensional data and maintaining the accuracy of the model. The RF model combined with ST_PCA for integrating VIS and TEs achieved the best estimations, with R2 of 0.86 and 0.91, RMSE of 0.26 and 2.01, and MAE of 0.22 and 1.66 for LAI and SPAD, respectively. ST_PCA, combined with machine learning algorithms, holds promising potential for monitoring crop physiological and biochemical parameters.References
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Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
