Maize Tassel Research Articles

Mining Candidate Genes for Maize Tassel Spindle Length Based on a Genome-Wide Association Analysis

Maize tassel spindle length is closely related to the number of pollen grains and the duration of the flowering stage, ultimately affecting maize yield and adaptations to stress conditions. In this study, 182 maize inbred lines were included in an association population. A genome-wide association study was conducted on maize tassel spindle length using the Q + K model. With p ≤ 1.0 × 10−4 applied as the significance threshold, 240 SNPs significantly associated with tassel spindle length were detected, which were associated with 99 quantitative trait loci (QTLs), with 21 QTLs detected in two or more environments. Moreover, 51 candidate genes were detected in 21 co-localized QTLs. A KEGG enrichment analysis and candidate gene expression analysis indicated that Zm00001d042312 affects plant hormone signal transduction and is highly expressed in maize tassels. A haplotype analysis of Zm00001d042312 revealed three main haplotypes, with significant differences between Hap1 and Hap2. In conclusion, we propose that Zm00001d042312 is a gene that regulates maize tassel spindle length. This study has further elucidated the genetic basis of maize tassel spindle length, while also providing excellent genetic targets and germplasm resources for the genetic improvement of maize tassel spindle length and yield.

Impacts of High Temperature and Vapor Pressure Deficit on the Maize Opened Spikelet Ratio and Pollen Viability

High temperatures (HTs) and high vapor pressure deficits (VPDs) have significant impacts on maize yields, partly due to the high sensitivity of maize tassels. However, there are few studies quantifying the impacts of HTs and VPDs on maize tassel performance under changing environments. Therefore, we carried out a two-year field experiment that included 20 inbred lines and four sowing dates. Compared with the first sowing date, the seed set in the second sowing date decreased by ~80% in both years. The opened spikelet ratio (OSR) and pollen viability (PV) were the key determinants of seed set, and their respective correlation coefficients with seed set were 0.58 and 0.90. The OSR and PV decreased by ~20% and ~50%, respectively, under high-temperature stress. When Tmax exceeded 32.5 °C or the VPD exceeded 0.91 KPa, PV began to decline; when Tmax exceeded 33.8 °C or VPD exceeded 1.10 KPa, the OSR began to decline. The OSR was more dependent on genotypic background than PV (28.4% vs. 19.7%). The maize tassel water content was significantly correlated with the OSR and PV. Based on the OSR and PV values, the 20 genotypes were divided into three different groups, namely the high H, middle M, and low L groups. The H group, on average, had the highest kernel number per ear and seed set, followed by the M and L groups. The average seed sets of the H, M, and L genotypes under the second sowing date were 17.4%, 10.9%, and 0%, respectively, in 2019 and 13.8%, 7.9%, and 0.6%, respectively, in 2020. The present results indicate that selecting maize varieties with a high OSR is an effective approach for improving maize yield under heat stress.

Evaluation of maize tassel ash and Xylopia aethiopica powder as protectants against cowpea seed infestation by Callosobruchus maculatus Fab. (Coleoptera: Bruchidae)

Evaluation of maize tassel ash and Xylopia aethiopica powder as protectants against cowpea seed infestation by Callosobruchus maculatus Fab. (Coleoptera: Bruchidae)

Vision foundation model for agricultural applications with efficient layer aggregation network

Agricultural production is transitioning from traditional tools to IoT-connected automation devices. The integration of computer vision and agricultural automation is becoming closer with the rapid development of deep learning technology. Agricultural vision tasks, nevertheless, are relatively simple and less diverse compared to large-scale vision tasks, and there is a scarcity of plant data. This is also the reason why researchers need to make significant adjustments when directly applying advanced algorithms to the agricultural domain. In practice, downstream vision models should be designed to be concise and efficient. In this paper, we meticulously designed a deep convolutional network called TasselELANet for agricultural vision applications. It features a concise and efficient global architecture, including a 16-fold downsampling layer encoder and a decoder utilizing only 2 feature layers, which is greatly different from the general approaches. At its core is the efficient layer aggregation network, which utilizes the cross-stage fusion strategy to effectively optimize the gradient propagation path, thereby enhancing the learning ability and inference speed of the network. We validated the performance of TasselELANet on three challenging publicly available plant detection and counting datasets: maize tassels, wheat ears, and rice panels. The experimental tests achieved an average Accuracy of 0.899 and an average determination coefficient R2 of 0.867, remarkably outperforming other advanced computer vision methods in terms of accuracy and efficiency, and demonstrating sufficient generalizability. We firmly believe that TasselELANet can serve as a powerful and reliable vision tool for agricultural practitioners while also providing a solid foundation for future research. The code, datasets can be accessed at https://github.com/Ye-Sk/TasselELANet.git.

Maize Anthesis-Silking Interval Estimation via Image Detection under Field Rail-Based Phenotyping Platform

The Anthesis-Silking Interval (ASI) is a crucial indicator of the synchrony of reproductive development in maize, reflecting its sensitivity to adverse environmental conditions such as heat stress and drought. This paper presents an automated method for detecting the maize ASI index using a field high-throughput phenotyping platform. Initially, high temporal-resolution visible-light image sequences of maize plants from the tasseling to silking stage are collected using a field rail-based phenotyping platform. Then, the training results of different sizes of YOLOv8 models on this dataset are compared to select the most suitable base model for the task of detecting maize tassels and ear silks. The chosen model is enhanced by incorporating the SENetv2 and the dual-layer routing attention mechanism BiFormer, named SEBi-YOLOv8. The SEBi-YOLOv8 model, with these combined modules, shows improvements of 2.3% and 8.2% in mAP over the original model, reaching 0.989 and 0.886, respectively. Finally, SEBi-YOLOv8 is used for the dynamic detection of maize tassels and ear silks in maize populations. The experimental results demonstrate the method’s high detection accuracy, with a correlation coefficient (R2) of 0.987 and an RMSE of 0.316. Based on these detection results, the ASI indices of different inbred lines are calculated and compared.

Fine-Scale Quantification of the Effect of Maize Tassel on Canopy Reflectance with 3D Radiative Transfer Modeling

Fine-Scale Quantification of the Effect of Maize Tassel on Canopy Reflectance with 3D Radiative Transfer Modeling

YOLOv5-T: A precise real-time detection method for maize tassels based on UAV low altitude remote sensing images

Maize tassels is an important organ of maize plant, and has a very important impact on yield prediction and variety breeding. Therefore, realizing the efficient and accurate detection of maize tassels in the natural environment is the key to obtain maize phenotypes in large quantities and accurately predict maize yield. This study constructed a dataset of tassels from different growth stages, proposed a YOLOv5-Tassel (YOLOv5-T) maize tassels detection model based on UAV remote sensing platform, combined with attention mechanism, YOLOv5_l network, spatial pyramid pooling structure and multi-scale extraction advantages of Atrous convolution. The experimental results showed that the Average Precision (AP) of the model for maize tassels detection could reach 98.70 %, and the detection speed was 42.2f/s. And the method in this paper had strong robustness to changes in light intensity and maize tassels at different growth stages. It was feasible to use YOLOv5-T to detect large areas of maize tassels in real time, which provided a useful reference for the estimation of maize yield and the selection of maize varieties.

Maize tassel number and tasseling stage monitoring based on near-ground and UAV RGB images by improved YoloV8

Maize tassel number and tasseling stage monitoring based on near-ground and UAV RGB images by improved YoloV8

Smart Farming with Sooty Tern Optimization based LS-HGNet Classification Model

Smart farming technologies enable farmers to use resources like water, fertilizer and pesticides as efficiently as possible. This paper discusses how Unmanned Aerial Vehicle (UAV) pictures can be used to automatically detect and count tassels, thereby advancing the advancement of strategic maize planting. The real state of affairs in cornfields is complicated, though, and the current algorithms struggle to provide the speed and accuracy required for real-time detection. This research employed a sizable, excellent dataset of maize tassels to solve this problem. This paper suggests using the bottom-hat-top-hat preprocessing technique to address the lighting irregularities and noise in maize photos taken by drones. The Lightweight weight-stacked hourglass Network (LS-HGNet) model is suggested for classification. The hourglass network structure of LS-HGNet, which is mostly utilised as a backbone network, has allowed significant advancements in the discovery of maize tassels. In light of this, the current work suggests a lighter variant of the hourglass network that also enhances the accuracy of tassel detection in maize plants. The additional skip connections used in the new hourglass network architecture allow minimal changes to the number of network parameters while improving performance. Consequently, the suggested LS-HGNet classifier lowers the computational burden and increases the convolutional receptive field. The hyperparameter tuning process is then carried out using the Sooty Tern Optimisation Algorithm (STOA), which helps increase tassel detection accuracy. Numerous tests were conducted to verify that the suggested approach is more accurate at 98.7% and more efficient than the most advanced techniques currently in use.

Image Filtering to Improve Maize Tassel Detection Accuracy Using Machine Learning Algorithms.

Unmanned aerial vehicle (UAV)-based imagery has become widely used to collect time-series agronomic data, which are then incorporated into plant breeding programs to enhance crop improvements. To make efficient analysis possible, in this study, by leveraging an aerial photography dataset for a field trial of 233 different inbred lines from the maize diversity panel, we developed machine learning methods for obtaining automated tassel counts at the plot level. We employed both an object-based counting-by-detection (CBD) approach and a density-based counting-by-regression (CBR) approach. Using an image segmentation method that removes most of the pixels not associated with the plant tassels, the results showed a dramatic improvement in the accuracy of object-based (CBD) detection, with the cross-validation prediction accuracy (r2) peaking at 0.7033 on a detector trained with images with a filter threshold of 90. The CBR approach showed the greatest accuracy when using unfiltered images, with a mean absolute error (MAE) of 7.99. However, when using bootstrapping, images filtered at a threshold of 90 showed a slightly better MAE (8.65) than the unfiltered images (8.90). These methods will allow for accurate estimates of flowering-related traits and help to make breeding decisions for crop improvement.

Green biosourced composite for efficient reactive dye decontamination: immobilized Gibberella fujikuroi on maize tassel biomatrix

Biosorptive treatment with microbial biomass is regarded as an environmentally friendly and effective way to reduce dye contamination in contaminated aquatic environments. Immobilizing microbial cells for use in this process can significantly improve their effectiveness as biosorbents in the water treatment process. The current investigation searches for a sustainable and environmentally friendly approach to decolorization by employing a green biocomposite material sorbent system (ZM@GFC) created by immobilizing fungal cells (Gibberella fujikuroi) on maize tassel tissues to efficiently remove Reactive Yellow 2 (RY2) from contaminated water sources. Batch and dynamic flow tests were performed to evaluate the biodecolorization properties of the newly created immobilized biomaterial as well as the effects of several essential operating conditions factors on the sorption behavior. Biosorption yields of 95.7% and 90.0% in batch and dynamic modes were achieved for experimental dye decolorization. The biosorption of RY2 by ZM@GFC occurred fast and achieved equilibrium within 60 min. The pseudo-second-order kinetic model elucidated the dye biosorption onto ZM@GFC. The Langmuir model provided a more accurate representation of the results than the Freundlich model. At the same time, Redlich-Peterson isotherm demonstrated the best level of agreement with the experimental data. These findings indicate that the biosorption mechanism predominantly involved the formation of a monolayer covering and that the energy properties of the ZM@GFC surface were uniform. The breakthrough capacity at the exhaustion time was 537.32 mg g−1. The predicted cost of generating ZM@GFC was anticipated to be 61.03 USD/kg. The investigations on safe disposal demonstrated that the biosorption process did not generate any secondary pollution. In conclusion, using maize tassel tissue as an immobilized decolorization agent offers a possible method for removing reactive azo dye pollutants from the aquatic medium that is both economical and environmentally benign.

Physiological Mechanisms Underlying Tassel Symptom Formation in Maize Infected with Sporisorium reilianum.

Head smut is a soil-borne fungal disease caused by Sporisorium reilianum that infects maize tassels and ears. This disease poses a tremendous threat to global maize production. A previous study found markedly different and stably heritable tassel symptoms in some maize inbred lines with Sipingtou blood after infection with S. reilianum. In the present study, 55 maize inbred lines with Sipingtou blood were inoculated with S. reilianum and classified into three tassel symptom types (A, B, and C). Three maize inbred lines representing these classes (Huangzao4, Jing7, and Chang7-2, respectively) were used as test materials to investigate the physiological mechanisms of tassel formation in infected plants. Changes in enzyme activity, hormone content, and protein expression were analyzed in all three lines after infection and in control plants. The activities of peroxidase (POD), superoxide dismutase (SOD), and phenylalanine-ammonia-lyase (PAL) were increased in the three typical inbred lines after inoculation. POD and SOD activities showed similar trends between lines, with the increase percentage peaking at the V12 stage (POD: 57.06%, 63.19%, and 70.28% increases in Huangzao4, Jing7, and Chang7-2, respectively; SOD: 27.01%, 29.62%, and 47.07% in Huangzao4, Jing7, and Chang7-2, respectively. These were all higher than in the disease-resistant inbred line Mo17 at the same growth stage); this stage was found to be key in tassel symptom formation. Levels of gibberellic acid (GA3), indole-3-acetic acid (IAA), and abscisic acid (ABA) were also altered in the three typical maize inbred lines after inoculation, with changes in GA3 and IAA contents tightly correlated with tassel symptoms after S. reilianum infection. The differentially expressed proteins A5H8G4, P09233, and Q8VXG7 were associated with changes in enzyme activity, whereas P49353, P13689, and P10979 were associated with changes in hormone contents. Fungal infection caused reactive oxygen species (ROS) and nitric oxide (NO) bursts in the three typical inbred lines. This ROS accumulation caused biofilm disruption and altered host signaling pathways, whereas NO signaling triggered strong secondary metabolic responses in the host and altered the activities of defense-related enzymes. These factors together resulted in the formation of varying tassel symptoms. Thus, interactions between S. reilianum and susceptible maize materials were influenced by a variety of signals, enzymes, hormones, and metabolic cycles, encompassing a very complex regulatory network. This study preliminarily identified the physiological mechanisms leading to differences in tassel symptoms, deepening our understanding of S. reilianum-maize interactions.

Maize tassel detection with CA-YOLO for UAV images in complex field environments

Maize detasseling is a key step in maize cross-pollination and is a key part of maize seed production, and the accurate identification and detection of maize tassels is a prerequisite for maize detasseling. The traditional manual method has the disadvantages of strong subjectivity, high workload, and poor timeliness. An artificial intelligence algorithm based on deep learning provides a new method for maize tassel detection. In this research, an uncrewed aerial vehicle (UAV) collected data for 10 consecutive days, covering the entire maize tasseling period. Data collected under different weather conditions and UAV flight altitudes were used to construct a diverse dataset for maize tassel detection. A preprocessing strategy suitable for the tassel dataset was developed through a dataset analysis. Then, a coordinate-attention(CA) mechanism was added to the backbone network of You Only Look Once version 5 to enhance the regression and localization capability of the model by embedding location information to extract important features. The improved model is called CA-YOLO. Comparative experiments revealed that CA-YOLO provides an effective method for detecting maize tassels, with an average precision of 96 %, surpassing the YOLO series models, such as YOLOv5m and YOLOv7, and classical detection models, such as the faster region-based convolutional neural network and single-shot detector. Moreover, CA-YOLO is robust and can effectively detect early-stage, leaf-obscured, mutually obscured, and complex-background tassels. This study provides a theoretical basis for accurately monitoring tassels and mechanical detasseling systems.

The Long-Term Application of Controlled-Release Nitrogen Fertilizer Maintains a More Stable Bacterial Community and Nitrogen Cycling Functions Than Common Urea in Fluvo-Aquic Soil

Controlled-release nitrogen fertilizer (CRNF) has been proven to surpass common urea by mitigating nutrient losses, enhancing soil quality, and improving crop productivity. However, the long-term effects of CRNF on soil biological properties are not well understood. Here, a 12-year field experiment was conducted with five treatments: no N fertilizer (PK); the split application of urea at the farmer’s practice rate (FP) and the optimal rate (OPT); the one-time application of CRNF at the same rate as the OPT (CRNF); and a 20% reduced rate of the OPT (0.8CRNF). Soil samples were collected during the maize tasseling and filling stages; high-throughput sequencing and the PICRUSt2 method were employed to determine the bacterial community and its functional potential. The results showed that CRNF significantly increased alkaline hydrolysis N by 14.10% and 9.45% compared to OPT during the tasseling and filling stages, respectively. This increase in soil available N resulted in a significant increase in bacterial diversity of 2.09% and 2.35% compared with the FP and OPT, respectively. The bacterial community in the FP and OPT changed markedly between the tasseling and filling stages, with many bacterial species at the ASV and genus levels showing variations in relative abundance. In contrast, CRNF and 0.8CRNF exhibited stable N-cycling functions, as indicated by the lower variations in nitrate reductase and predicted N-cycling functional genes between the tasseling and filling stages. The obtained results suggest that CRNF application can enhance soil N supply, promote the formation of stable bacterial communities, and maintain stable N-cycling functions.

A review on the current approach for the sustainable production of nanocomposites from agricultural wastes and their application for the adsorption of organophosphate flame retardants

Water pollution caused by emerging organic pollutants such as the halogenated organophosphate flame retardants, has exacerbated the issue of water scarcity. An eco-friendly technology, e.g., the adsorption technique, requires the use of affordable and safe adsorbents. Agricultural waste materials are promising adsorbents for wastewater remediation due to their relative abundance, biodegradability, non-toxic and cost-effective properties. However, most agricultural wastes are not ideal for adsorption in their raw forms and may require physical or chemical pre-treatments/modification, in order to increase the materials' compatibility and natural adsorption properties. Cellulose is an important constituent of plant residues that can be used as a precursor for the production of greener and sustainable industrial nanomaterials. Therefore, in this review, a discourse on the most recent approach/strategies for the modification of a range of agricultural wastes is presented. The result from their applications showed agricultural wastes has great prospects as adsorbents. The challenges encountered in the synthesis of plant-based adsorbents and the development of 3-D structured nanocomposites from cellulose, to circumvent these difficulties is extensively reviewed. Furthermore, the prospects in the application of cellulose beads/ZnO nanocomposites (CB-ZnO) derived from the maize tassel, for the adsorption of the halogenated organophosphate esters from aqueous matrices are presented in this review

MT-Det: A novel fast object detector of maize tassel from high-resolution imagery using single level feature

Accurately and quickly detecting maize tassels is crucial for successful maize breeding and seed production. However, the complex planting environment in the field, including unsynchronized growth stages, cluttered backgrounds, severe occlusions, and varying tassel sizes and shapes, presents a challenge for detection methods. Current methods either use mainstream object detectors off-the-shelf or employ shallow adaptations on the modular level, lacking a systematic design that balances both speed and accuracy. Furthermore, most studies only report evaluation results on sliced image patches instead of original high-resolution images, which can offer limited guidance for large-scale crop management. In this study, we propose a novel one-stage anchor-free maize tassel detector (MT-Det), which is based on a single level feature and designed to be simple yet effective. Extensive comparisons with both one-level counterparts and feature pyramid detectors demonstrate that MT-Det outperforms them in both detection accuracy and inference speed. To address the issue of significant accuracy drop when directly inferring on high-resolution images, we introduce a bundle of slicing-aided hyper inference technologies to our proposed MT-Det, which results in a 13% and 38% improvement of mean average precision (mAP) on proximal and unmanned aerial vehicle (UAV) high-resolution images, respectively. We believe that MT-Det provides a promising high-throughput solution for accurate and efficient detection and counting of maize tassels in real-world field conditions.

WheatLFANet: in-field detection and counting of wheat heads with high-real-time global regression network

BackgroundDetection and counting of wheat heads are of crucial importance in the field of plant science, as they can be used for crop field management, yield prediction, and phenotype analysis. With the widespread application of computer vision technology in plant science, monitoring of automated high-throughput plant phenotyping platforms has become possible. Currently, many innovative methods and new technologies have been proposed that have made significant progress in the accuracy and robustness of wheat head recognition. Nevertheless, these methods are often built on high-performance computing devices and lack practicality. In resource-limited situations, these methods may not be effectively applied and deployed, thereby failing to meet the needs of practical applications.ResultsIn our recent research on maize tassels, we proposed TasselLFANet, the most advanced neural network for detecting and counting maize tassels. Building on this work, we have now developed a high-real-time lightweight neural network called WheatLFANet for wheat head detection. WheatLFANet features a more compact encoder-decoder structure and an effective multi-dimensional information mapping fusion strategy, allowing it to run efficiently on low-end devices while maintaining high accuracy and practicality. According to the evaluation report on the global wheat head detection dataset, WheatLFANet outperforms other state-of-the-art methods with an average precision AP of 0.900 and an R2 value of 0.949 between predicted values and ground truth values. Moreover, it runs significantly faster than all other methods by an order of magnitude (TasselLFANet: FPS: 61).ConclusionsExtensive experiments have shown that WheatLFANet exhibits better generalization ability than other state-of-the-art methods, and achieved a speed increase of an order of magnitude while maintaining accuracy. The success of this study demonstrates the feasibility of achieving real-time, lightweight detection of wheat heads on low-end devices, and also indicates the usefulness of simple yet powerful neural network designs.

The richness of Fusarium species in maize tassels and their relationship with Fusarium stalk rot

The richness of Fusarium species in maize tassels and their relationship with Fusarium stalk rot

TIPS: A three-dimensional phenotypic measurement system for individual maize tassel based on TreeQSM

Maize tassel is a crucial pollen producing organ that plays an important role in maize production. It is challenging to investigate the morphological and structural traits of maize tassels for breeding programs, since traditional manual measurements of organ-scale phenotypic traits are labor-intensive and prone to human errors. It is, therefore, urgent to design and develop new phenotyping systems for maize tassels to improve throughput and measurement accuracy. This study first introduced the TreeQSM in crop phenotyping at organ scale and developed TIPS (TreeQSM based Image Phenotyping System for maize tassels). The system mainly consists of three digital cameras for acquiring multi-view images of individual maize tassel. These cameras were vertically arranged with different angles of view. The acquired images were used to reconstruct the 3D point cloud data of individual maize tassel, which were fed into the TreeQSM to extract four tassel phenotypic parameters including trunk length, branch number, branch length, and branch angle. The performance analyses of the developed system were conducted on 52 tassel samples from 37 maize materials with different canopy geometric structures. The experimental results showed that the TIPS could achieve accurate tassel parameters estimation with the R2 of 0.964, 0.973, 0.935, and 0.857, and the RMSE of 1.72, 1.28, 19.94, and 2.73 for the trunk length, branch number, total branch length, and first node branch angle, respectively. This study also investigated the advantages of the data acquisition with the TIPS and the effects of different shooting angles, number of images, lighting conditions and tassel types on the four tassel parameters estimation accuracy. The comparison results indicated that the four influencing factors reduced the estimation accuracy to varying degree. Compared with the other three parameters, the estimation accuracy of branch angles less than 20 were largely affected. The higher the degree of compactness, the worse the estimation accuracy. Compared with shooting angles, the reduction of the number of images in a certain range had less impact on the quality of 3D point cloud reconstruction. The influence of low light was obviously greater than that of strong light. This study provides a valuable guide to the collection and quantitative analysis of high-throughput phenotype information of maize tassels in breeding program.

Identification of QTLs and their candidate genes for the number of maize tassel branches in F2 from two higher generation sister lines using QTL mapping and RNA-seq analysis.

Tassel branch number is an important agronomic trait that is closely associated with maize kernels and yield. The regulation of genes associated with tassel branch development can provide a theoretical basis for analyzing tassel branch growth and improving maize yield. In this study. we used two high-generation sister maize lines, PCU (unbranched) and PCM (multiple-branched), to construct an F2 population comprising 190 individuals, which were genotyped and mapped using the Maize6H-60K single-nucleotide polymorphism array. Candidate genes associated with tassel development were subsequently identified by analyzing samples collected at three stages of tassel growth via RNA-seq. A total of 13 quantitative trait loci (QTLs) and 22 quantitative trait nucleotides (QTNs) associated with tassel branch number (TBN) were identified, among which, two major QTLs, qTBN6.06-1 and qTBN6.06-2, on chromosome 6 were identified in two progeny populations, accounting for 15.07% to 37.64% of the phenotypic variation. Moreover, we identified 613 genes that were differentially expressed between PCU and PCM, which, according to Kyoto Encyclopedia of Genes and Genomes enrichment analysis, were enriched in amino acid metabolism and plant signal transduction pathways. Additionally, we established that the phytohormone content of Stage I tassels and the levels of indole-3-acetic acid (IAA) and IAA-glucose were higher in PCU than in PCM plants, whereas contrastingly, the levels of 5-deoxymonopolyl alcohol in PCM were higher than those in PCU. On the basis of these findings, we speculate that differences in TBN may be related to hormone content. Collectively, by combining QTL mapping and RNA-seq analysis, we identified five candidate genes associated with TBN. This study provides theoretical insights into the mechanism of tassel branch development in maize.