Background Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. Results The models with a single color feature from RGB images predicted chlorophyll content with R2 ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R2 ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R2 of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R2 of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy. Conclusion All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum.
We experimentally studied sequential {10 2} twinning in rolled pure titanium at room temperature and identified a new sequential twinning mechanism, i.e., {10 2} () extension twin stimulated by the twin-twin junction of two primary {11 } extension twins and , which holds a certain relation . Schmid factor analysis, as the local stress is taken into account, is able to determine the position of sequential twin variant, but cannot determine the twin variant. Displacement gradient accommodation is used to determine the twin variant. The well-known secondary {10 2} () extension twins in primary {11 2} () contraction twins (referred to as double twinning) are also analyzed according to a generalized Schmid factor analysis. Displacement gradient accommodation and twin nucleation based on dislocations dissociation only work well for the most active twin variants, but cannot address other phenomena associated with double twinning. For rarely activated twin variants, displacement gradient accommodation was not satisfied.
{112 ̅ 2} contraction twins that are commonly activated in α-titanium interact to each other and form three types of twin-twin junctions (𝐶 𝑖,𝑗+1 𝐼 , 𝐶 𝑖,𝑗+2 𝐼 , 𝐶 𝑖,𝑗+3 𝐼 TTJs) corresponding to the crystallography of six twin variants 𝐶 𝑖 𝐼 (i = 1,2, … , 6). We detected 243 {112 ̅ 2} TTJs in rolled pure α-titanium sheets. Electron backscatter diffraction analysis reveals that 𝐶 𝑖,𝑗+1 𝐼 TTJs are profuse, 79.8% among three types while 𝐶 𝑖,𝑗+2 𝐼 , and 𝐶 𝑖,𝑗+3 𝐼TTJs take up 17.7 and 2.5%. Twin transmission does not occur. Consequently, boundaries associated with twin-twin interactions block twin propagation and influence twin growth. We explain structural features of TTJs according to the Schmid factor analysis and the reaction mechanism of twinning dislocations. The knowledge regarding TTJs provides insight for improving the predictive capability of meso/macro-scale crystal plasticity models for hexagonal metals.
Leaf numbers are vital in estimating the yield of crops. Traditional manual leaf-counting is tedious, costly, and an enormous job. Recent convolutional neural network-based approaches achieve promising results for rosette plants. However, there is a lack of effective solutions to tackle leaf counting for monocot plants, such as sorghum and maize. The existing approaches often require substantial training datasets and annotations, thus incurring significant overheads for labeling. Moreover, these approaches can easily fail when leaf structures are occluded in images. To address these issues, we present a new deep neural network-based method that does not require any effort to label leaf structures explicitly and achieves superior performance even with severe leaf occlusions in images. Our method extracts leaf skeletons to gain more topological information and applies augmentation to enhance structural variety in the original images. Then, we feed the combination of original images, derived skeletons, and augmentations into a regression model, transferred from Inception-Resnet-V2, for leaf-counting. We find that leaf tips are important in our regression model through an input modification method and a Grad-CAM method. The superiority of the proposed method is validated via comparison with the existing approaches conducted on a similar dataset. The results show that our method does not only improve the accuracy of leaf-counting, with overlaps and occlusions, but also lower the training cost, with fewer annotations compared to the previous state-of-the-art approaches.The robustness of the proposed method against the noise effect is also verified by removing the environmental noises during the image preprocessing and reducing the effect of the noises introduced by skeletonization, with satisfactory outcomes.
BackgroundLeaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput phenotyping. High throughput imaging techniques are now widely used for nondestructive analysis of plant phenotypic traits. In this study three imaging modules, namely, RGB, hyperspectral, and fluorescence imaging, were used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and regression models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. ResultsModels that included two additional variables, DAS (day after sowing) and SLW (specific leaf weight), were also investigated to improve the prediction of chlorophyll. R2 for chlorophyll concentration for multiple linear models at various color components were 0.77 for R, 0.79 for G, 0.70 for B. To obtain additional spectral information, color component H, S, and I were calculated after color spaces being transformed. The result of HSI space showed that R2 for chlorophyll concentration for multiple linear models were 0.67 for H, 0.88 for S, 0.77 for I. The R2 values for different hyperspectral index like the ratio vegetation index (RVI), the normalized difference vegetation index (NDVI), modified chlorophyll absorption ratio index (MCARI) between 0.77 and 0.78. R2=0.79 was obtained with fluorescence image. Partial least squares regression (PLSR) was employed to using the selected vegetation indices computed from different imaging data to estimate the chlorophyll concentration for sorghum plants. Among all the imaging data, chlorophyll content was predicted with high accuracy (R2 from 0.84 to 2.92, RPD from 2.49 to 3.58). ConclusionAccording to the Akaike's Information Criterion (AIC) error function, the model was better fitted based on images, DAS and SLW than that based on images and DAS. This study indicated that the accuracy for chlorophyll estimation was increased by the image traits combined with DAS and SLW. High throughput imaging provides a simple, rapid, and nondestructive method to estimate the leaf chlorophyll concentration.
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