Waterlogging in the early stage of cotton will reduce the number of bolls and do harm to yield. Early detection of waterlogging will help farmers to adjust cotton management and save the loss. To evaluate the application of deep learning for the detection of early waterlogging, this study applied a convolutional neural network (CNN) to classify different durations of waterlogging stress (0, 2, 4, 6, 8, 10 d) based on hyperspectral images (HSIs) of cotton leaves. An experiment was designed to simulate the situation of cotton under waterlogging stress and collect HSIs of visible and near-infrared (VNIR 450-950 nm) spectra with 126 bands 66 d after cotton sowing (66 DAS). It was found the spectral curve reflectance of waterlogging cotton was higher than that of non-waterlogging cotton. Especially near 550 nm and 750 nm, and the spectral curve increased with durations of waterlogging stress and there were 'blue shift' phenomena for the position of the red edge of the spectra. The first principal components of HSIs after band randomly discarding and principal component analysis (PCA) were used to build a dataset. GoogLeNet Inception-v3 (GLNI-v3) and VGG-16 models were selected to detect cotton waterlogging stress with the dataset. The results showed that the average time for a round training for GLNI-v3 was 13.337 s, with a classification accuracy of 96.95% and a loss value of 0.09431. The average time for a round training for VGG-16 was 21.470 s, with a classification accuracy of 97.00% and a loss value of 0.17912. Though these two models had similar classification accuracy and loss value, GLNI-v3 achieved a high accuracy with fewer training iterations. The durations of waterlogging stress of cotton in a short-term can be detected by HSIs of cotton leaves and CNN models are suitable for the classification of HSIs, and this method can provide support for cotton yield estimation and loss assessment after waterlogging.
Rapid and accurate crop chlorophyll content estimation and the leaf area index (LAI) are both crucial for guiding field management and improving crop yields. This paper proposes an accurate monitoring method for LAI and soil plant analytical development (SPAD) values (which are closely related to leaf chlorophyll content; we use the SPAD instead of chlorophyll relative content) based on the fusion of ground–air multi-source data. Firstly, in 2020 and 2021, we collected unmanned aerial vehicle (UAV) multispectral data, ground hyperspectral data, UAV visible-light data, and environmental cumulative temperature data for multiple growth stages of summer maize, respectively. Secondly, the effective plant height (canopy height model (CHM)), effective accumulation temperature (growing degree days (GDD)), canopy vegetation index (mainly spectral vegetation index) and canopy hyperspectral features of maize were extracted, and sensitive features were screened by correlation analysis. Then, based on single-source and multi-source data, multiple linear regression (MLR), partial least-squares regression (PLSR) and random forest (RF) regression were used to construct LAI and SPAD inversion models. Finally, the distribution of LAI and SPAD prescription plots was generated and the trend for the two was analyzed. The results were as follows: (1) The correlations between the position of the hyperspectral red edge and the first-order differential value in the red edge with LAI and SPAD were all greater than 0.5. The correlation between the vegetation index, including a red and near-infrared band, with LAI and SPAD was above 0.75. The correlation between crop height and effective accumulated temperature with LAI and SPAD was above 0.7. (2) The inversion models based on multi-source data were more effective than the models made with single-source data. The RF model with multi-source data fusion achieved the highest accuracy of all models. In the testing set, the LAI and SPAD models’ R2 was 0.9315 and 0.7767; the RMSE was 0.4895 and 2.8387. (3) The absolute error between the extraction result of each model prescription map and the measured value was small. The error between the predicted value and the measured value of the LAI prescription map generated by the RF model was less than 0.4895. The difference between the predicted value and the measured value of the SPAD prescription map was less than 2.8387. The LAI and SPAD of summer maize first increased and then decreased with the advancement of the growth period, which was in line with the actual growth conditions. The research results indicate that the proposed method could effectively monitor maize growth parameters and provide a scientific basis for summer maize field management.
The chlorophyll content is an important indicator of corn growth and yield. In order to improve the prediction accuracy of chlorophyll content, this study combines ground hyperspectral characteristic parameters (original spectral characteristics, first-order differential, characteristic spectral position), vegetation index calculated by multispectral, and effective plant height (Canopy Height Model, CHM) of crops, etc. Through correlation analysis of sensitive characteristics of chlorophyll content, the study uses multiple linear regression (MLR), partial least squares regression (PLSR), classification and regression tree regression (CART), and random forest (RF) to construct a summer maize SPAD inversion model. Then, the accuracy of the model was evaluated through the root mean square error (RMSE) and coefficient of determination (R 2 ). The results show that the position of the red edge and the first-order differential values within the red edge Dr, CHM, SAVI, NDVI, RDVI, GNDVI, RVI, and DVI are significantly correlated with SPAD; the MLR model under a single data source is the best, the model's R 2 is 0.8281, RMSE is 2.136; the RF model under multi-source data is the best. The model's R 2 and RMSE are 0.9114 and 2.3955 respectively. The accuracy of the SPAD inversion model constructed based on multi-source data is better than that of a single data source. This study shows that the random forest model based on multi-source data can invert the SPAD of summer maize better. This method can provide theoretical support for summer maize growth monitoring and fine fertilization management.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.