Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A digital single-lens reflex camera was installed on an UAV platform to capture ultrahigh resolution RGB images at two growth stages when most rapeseed plants had at least two leaves. Rapeseed plant objects were segmented from images of vegetation indices using typical Otsu thresholding method. After segmentation, shape features such as area, length-width ratio and elliptic fit were extracted from the segmented rapeseed plant objects to establish regression models of seedling stand count. Three row characteristics (the coefficient of variation of row spacing uniformity, the error rate of the row spacing and the coefficient of variation of seedling uniformity) were further calculated for seeding performance evaluation after crop row detection. Results demonstrated that shape features had strong correlations with ground-measured seedling stand count. The regression models achieved R-squared values of 0.845 and 0.867, respectively, for the two growth stages. The mean absolute errors of total stand count were 9.79 and 5.11% for the two respective stages. A single model over these two stages had an R-squared value of 0.846, and the total number of rapeseed plants was also accurately estimated with an average relative error of 6.83%.Moreover, the calculated row characteristics were demonstrated to be useful in recognizing areas of failed germination possibly resulted from skipped or ineffective planting. In summary, this study developed practical UAV-based remote sensing methods and demonstrated the feasibility of using the methods for rapeseed seedling stand counting and mechanical seeding performance evaluation at early growth stages

Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A digital single-lens reflex camera was installed on an UAV platform to capture ultrahigh resolution RGB images at two growth stages when most rapeseed plants had at least two leaves. Rapeseed plant objects were segmented from images of vegetation indices using typical Otsu thresholding method. After segmentation, shape features such as area, length-width ratio and elliptic fit were extracted from the segmented rapeseed plant objects to establish regression models of seedling stand count. Three row characteristics (the coefficient of variation of row spacing uniformity, the error rate of the row spacing and the coefficient of variation of seedling uniformity) were further calculated for seeding performance evaluation after crop row detection. Results demonstrated that shape features had strong correlations with ground-measured seedling stand count. The regression models achieved R-squared values of 0.845 and 0.867, respectively, for the two growth stages. The mean absolute errors of total stand count were 9.79 and 5.11% for the two respective stages. A single model over these two stages had an R-squared value of 0.846, and the total number of rapeseed plants was also accurately estimated with an average relative error of 6.83%. Moreover, the calculated row characteristics were demonstrated to be useful in recognizing areas of failed germination possibly resulted from skipped or ineffective planting. In summary, this study developed practical UAV-based remote sensing methods and demonstrated the feasibility of using the methods for rapeseed seedling stand counting and mechanical seeding performance evaluation at early growth stages

Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A digital single-lens reflex camera was installed on an UAV platform to capture ultrahigh resolution RGB images at two growth stages when most rapeseed plants had at least two leaves. Rapeseed plant objects were segmented from images of vegetation indices using typical Otsu thresholding method. After segmentation, shape features such as area, length-width ratio and elliptic fit were extracted from the segmented rapeseed plant objects to establish regression models of seedling stand count. Three row characteristics (the coefficient of variation of row spacing uniformity, the error rate of the row spacing and the coefficient of variation of seedling uniformity) were further calculated for seeding performance evaluation after crop row detection. Results demonstrated that shape features had strong correlations with ground-measured seedling stand count. The regression models achieved R-squared values of 0.845 and 0.867, respectively, for the two growth stages. The mean absolute errors of total stand count were 9.79 and 5.11% for the two respective stages. A single model over these two stages had an R-squared value of 0.846, and the total number of rapeseed plants was also accurately estimated with an average relative error of 6.83%.Moreover, the calculated row characteristics were demonstrated to be useful in recognizing areas of failed germination possibly resulted from skipped or ineffective planting. In summary, this study developed practical UAV-based remote sensing methods and demonstrated the feasibility of using the methods for rapeseed seedling stand counting and mechanical seeding performance evaluation at early growth stages

Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A digital single-lens reflex camera was installed on an UAV platform to capture ultrahigh resolution RGB images at two growth stages when most rapeseed plants had at least two leaves. Rapeseed plant objects were segmented from images of vegetation indices using typical Otsu thresholding method. After segmentation, shape features such as area, length-width ratio and elliptic fit were extracted from the segmented rapeseed plant objects to establish regression models of seedling stand count. Three row characteristics (the coefficient of variation of row spacing uniformity, the error rate of the row spacing and the coefficient of variation of seedling uniformity) were further calculated for seeding performance evaluation after crop row detection. Results demonstrated that shape features had strong correlations with ground-measured seedling stand count. The regression models achieved R-squared values of 0.845 and 0.867, respectively, for the two growth stages. The mean absolute errors of total stand count were 9.79 and 5.11% for the two respective stages. A single model over these two stages had an R-squared value of 0.846, and the total number of rapeseed plants was also accurately estimated with an average relative error of 6.83%.Moreover, the calculated row characteristics were demonstrated to be useful in recognizing areas of failed germination possibly resulted from skipped or ineffective planting. In summary, this study developed practical UAV-based remote sensing methods and demonstrated the feasibility of using the methods for rapeseed seedling stand counting and mechanical seeding performance evaluation at early growth stages

Rapeseed Stand Count Estimation at Leaf Development Stages With UAV Imagery and Convolutional Neural Networks

Rapeseed is an important oil crop in China. Timely estimation of rapeseed stand count at early growth stages provides useful information for precision fertilization, irrigation, and yield prediction. Based on the nature of rapeseed, the number of tillering leaves is strongly related to its growth stages. However, no field study has been reported on estimating rapeseed stand count by the number of leaves recognized with convolutional neural networks (CNNs) in unmanned aerial vehicle (UAV) imagery. The objectives of this study were to provide a case for rapeseed stand counting with reference to the existing knowledge of the number of leaves per plant and to determine the optimal timing for counting after rapeseed emergence at leaf development stages with one to seven leaves. A CNN model was developed to recognize leaves in UAV-based imagery, and rapeseed stand count was estimated with the number of recognized leaves. The performance of leaf detection was compared using sample sizes of 16, 24, 32, 40, and 48 pixels. Leaf overcounting occurred when a leaf was much bigger than others as this bigger leaf was recognized as several smaller leaves. Results showed CNN-based leaf count achieved the best performance at the four- to six-leaf stage with F-scores greater than 90% after calibration with overcounting rate. On average, 806 out of 812 plants were correctly estimated on 53 days after planting (DAP) at the four- to sixleaf stage, which was considered as the optimal observation timing. For the 32-pixel patch size, root mean square error (RMSE) was 9 plants with relative RMSE (rRMSE) of 2.22% on 53 DAP, while the mean RMSE was 12 with mean rRMSE of 2.89% for all patch sizes. A sample size of 32 pixels was suggested to be optimal accounting for balancing performance and efficiency. The results of this study confirmed that it was feasible to estimate rapeseed stand count in field automatically, rapidly, and accurately. This study provided a special perspective in phenotyping and cultivation management for estimating seedling count for crops that have recognizable leaves at their early growth stage, such as soybean and potato

Overexpression of an EaZIP gene devoid of transit peptide sequence induced leaf variegation in tobacco.

Leaf variegation is an ornamental trait that is not only biologically but also economically important. In our previous study, a Mg-protoporphyrin IX monomethyl ester cyclase homologue, EaZIP (Epipremnum aureum leucine zipper) was found to be associated with leaf variegation in Epipremnum aureum (Linden & Andre) G.S. Bunting. The protein product of this nuclear-encoded gene is targeted back to chloroplast involving in chlorophyll biosynthesis. Based on a web-based homology analysis, the EaZIP was found to lack a chloroplast transit peptide (cTP) sequence. In the present study, we tested if overexpression of the EaZIP cDNA with or without the cTP sequence could affect leaf variegation. Transgenic tobacco plants overexpressing EaZIP genes with (EaZIPwcTP) and without (EaZIPwocTP) cTP sequence were generated. Many plant lines harboring EaZIPwocTP showed variegated leaves, while none of the plant lines with EaZIPwcTP produced such a phenotype. Molecular analysis of T0 plants and selfed T1 progeny, as well as observations of tagged marker GFP (green fluorescent protein) did not show any other difference in patterns of gene integrity and expression. Results from this study indicate that transgenic approach for expressing EaZIPwocTP could be a novel method of generating variegated plants even through the underlying mechanisms remain to be elucidated

A Rice WRKY Gene Encodes a Transcriptional Repressor of the Gibberellin Signaling Pathway in Aleurone Cells

The molecular mechanism by which GA regulates plant growth and development has been a subject of active research. Analyses of the rice (Oryza sativa) genomic sequences identified 77 WRKY genes, among which OsWRKY71 is highly expressed in aleurone cells. Transient expression of OsWRKY71 by particle bombardment specifically represses GA-induced Amy32b α-amylase promoter but not abscisic acid-induced HVA22 or HVA1 promoter activity in aleurone cells. Moreover, OsWRKY71 blocks the activation of the Amy32b promoter by the GA-inducible transcriptional activator OsGAMYB. Consistent with its role as a transcriptional repressor, OsWRKY71 is localized to nuclei of aleurone cells and binds specifically to functionally defined TGAC-containing W boxes of the Amy32b promoter in vitro. Mutation of the two W boxes prevents the binding of OsWRKY71 to the mutated promoter, and releases the suppression of the OsGAMYB-activated Amy32b expression by OsWRKY71, suggesting that OsWRKY71 blocks GA signaling by functionally interfering with OsGAMYB. Exogenous GA treatment decreases the steady-state mRNA level of OsWRKY71 and destabilizes the GFP:OsWRKY71 fusion protein. These findings suggest that OsWRKY71 encodes a transcriptional repressor of GA signaling in aleurone cells

Annotations and Functional Analyses of the Rice WRKY Gene Superfamily Reveal Positive and Negative Regulators of Abscisic Acid Signaling in Aleurone Cells

The WRKY proteins are a superfamily of regulators that control diverse developmental and physiological processes. This family was believed to be plant specific until the recent identification of WRKY genes in nonphotosynthetic eukaryotes. We have undertaken a comprehensive computational analysis of the rice (Oryza sativa) genomic sequences and predicted the structures of 81 OsWRKY genes, 48 of which are supported by full-length cDNA sequences. Eleven OsWRKY proteins contain two conserved WRKY domains, while the rest have only one. Phylogenetic analyses of the WRKY domain sequences provide support for the hypothesis that gene duplication of single- and two-domain WRKY genes, and loss of the WRKY domain, occurred in the evolutionary history of this gene family in rice. The phylogeny deduced from the WRKY domain peptide sequences is further supported by the position and phase of the intron in the regions encoding the WRKY domains. Analyses for chromosomal distributions reveal that 26% of the predicted OsWRKY genes are located on chromosome 1. Among the dozen genes tested, OsWRKY24, -51, -71, and -72 are induced by abscisic acid (ABA) in aleurone cells. Using a transient expression system, we have demonstrated that OsWRKY24 and -45 repress ABA induction of the HVA22 promoter-β-glucuronidase construct, while OsWRKY72 and -77 synergistically interact with ABA to activate this reporter construct. This study provides a solid base for functional genomics studies of this important superfamily of regulatory genes in monocotyledonous plants and reveals a novel function for WRKY genes, i.e. mediating plant responses to ABA

Rapeseed Stand Count Estimation at Leaf Development Stages With UAV Imagery and Convolutional Neural Networks

Rapeseed is an important oil crop in China. Timely estimation of rapeseed stand count at early growth stages provides useful information for precision fertilization, irrigation, and yield prediction. Based on the nature of rapeseed, the number of tillering leaves is strongly related to its growth stages. However, no field study has been reported on estimating rapeseed stand count by the number of leaves recognized with convolutional neural networks (CNNs) in unmanned aerial vehicle (UAV) imagery. The objectives of this study were to provide a case for rapeseed stand counting with reference to the existing knowledge of the number of leaves per plant and to determine the optimal timing for counting after rapeseed emergence at leaf development stages with one to seven leaves. A CNN model was developed to recognize leaves in UAV-based imagery, and rapeseed stand count was estimated with the number of recognized leaves. The performance of leaf detection was compared using sample sizes of 16, 24, 32, 40, and 48 pixels. Leaf overcounting occurred when a leaf was much bigger than others as this bigger leaf was recognized as several smaller leaves. Results showed CNN-based leaf count achieved the best performance at the four- to six-leaf stage with F-scores greater than 90% after calibration with overcounting rate. On average, 806 out of 812 plants were correctly estimated on 53 days after planting (DAP) at the four- to sixleaf stage, which was considered as the optimal observation timing. For the 32-pixel patch size, root mean square error (RMSE) was 9 plants with relative RMSE (rRMSE) of 2.22% on 53 DAP, while the mean RMSE was 12 with mean rRMSE of 2.89% for all patch sizes. A sample size of 32 pixels was suggested to be optimal accounting for balancing performance and efficiency. The results of this study confirmed that it was feasible to estimate rapeseed stand count in field automatically, rapidly, and accurately. This study provided a special perspective in phenotyping and cultivation management for estimating seedling count for crops that have recognizable leaves at their early growth stage, such as soybean and potato

SMRT and Illumina RNA-Seq Identifies Potential Candidate Genes Related to the Double Flower Phenotype and Unveils SsAP2 as a Key Regulator of the Double-Flower Trait in <i>Sagittaria sagittifolia</i>

Double flowers are one of the important objectives of ornamental plant breeding. Sagittaria sagittifolia is an aquatic herb in the Alismataceae family that is widely used as an ornamental plant in gardens. However, the reference genome has not been published, and the molecular regulatory mechanism of flower formation remains unclear. In this study, single molecule real-time (SMRT) sequencing technology combined with Illumina RNA-Seq was used to perform a more comprehensive analysis of S. sagittifolia for the first time. We obtained high-quality full-length transcripts, including 53,422 complete open reading frames, and identified 5980 transcription factors that belonged to 67 families, with many MADS-box genes involved in flower formation being obtained. The transcription factors regulated by plant hormone signals played an important role in the development of double flowers. We also identified an AP2 orthologous gene, SsAP2, with a deletion of the binding site for miR172, that overexpressed SsAP2 in S. sagittifolia and exhibited a delayed flowering time and an increased number of petals. This study is the first report of a full-length transcriptome of S. sagittifolia. These reference transcripts will be valuable resources for the analysis of gene structures and sequences, which provide a theoretical basis for the molecular regulatory mechanism governing the formation of double flowers