Kiwifruit Flowers Research Articles

Deep learning based approach for actinidia flower detection and gender assessment

Pollination is critical for crop development, especially those essential for subsistence. This study addresses the pollination challenges faced by Actinidia, a dioecious plant characterized by female and male flowers on separate plants. Despite the high protein content of pollen, the absence of nectar in kiwifruit flowers poses difficulties in attracting pollinators. Consequently, there is a growing interest in using artificial intelligence and robotic solutions to enable pollination even in unfavourable conditions. These robotic solutions must be able to accurately detect flowers and discern their genders for precise pollination operations. Specifically, upon identifying female Actinidia flowers, the robotic system should approach the stigma to release pollen, while male Actinidia flowers should target the anthers to collect pollen. We identified two primary research gaps: (1) the lack of gender-based flower detection methods and (2) the underutilisation of contemporary deep learning models in this domain. To address these gaps, we evaluated the performance of four pretrained models (YOLOv8, YOLOv5, RT-DETR and DETR) in detecting and determining the gender of Actinidia flowers. We outlined a comprehensive methodology and developed a dataset of manually annotated flowers categorized into two classes based on gender. Our evaluation utilised k-fold cross-validation to rigorously test model performance across diverse subsets of the dataset, addressing the limitations of conventional data splitting methods. DETR provided the most balanced overall performance, achieving precision, recall, F1 score and mAP of 89%, 97%, 93% and 94%, respectively, highlighting its robustness in managing complex detection tasks under varying conditions. These findings underscore the potential of deep learning models for effective gender-specific detection of Actinidia flowers, paving the way for advanced robotic pollination systems.

A Hierarchical Model to Predict Time of Flowering of Kiwifruit Using Weather Data and Budbreak Dynamics.

Accurate prediction of flowering times is essential for efficient orchard management for kiwifruit, facilitating timely pest and disease control and pollination interventions. In this study, we developed a predictive model for flowering time using weather data and observations of budbreak dynamics for the 'Hayward' and 'Zesy002' kiwifruit. We used historic data of untreated plants collected from 32 previous studies conducted between 2007 and 2022 and analyzed budbreak and flowering timing alongside cumulative heat sum (growing degree days, GDDs), chilling unit (CU) accumulation, and other environmental variables using weather data from the weather stations nearest to the study orchards. We trained/parameterized the model with data from 2007 to 2019, and then evaluated the model's efficacy using testing data from 2020 to 2022. Regression models identified a hierarchical structure with the accumulation of GDDs at the start of budbreak, one of the key predictors of flowering time. The findings suggest that integrating climatic data with phenological events such as budbreak can enhance the predictability of flowering in kiwifruit vines, offering a valuable tool for kiwifruit orchard management.

Enhancing kiwifruit flower pollination detection through frequency domain feature fusion: a novel approach to agricultural monitoring.

The pollination process of kiwifruit flowers plays a crucial role in kiwifruit yield. Achieving accurate and rapid identification of the four stages of kiwifruit flowers is essential for enhancing pollination efficiency. In this study, to improve the efficiency of kiwifruit pollination, we propose a novel full-stage kiwifruit flower pollination detection algorithm named KIWI-YOLO, based on the fusion of frequency-domain features. Our algorithm leverages frequency-domain and spatial-domain information to improve recognition of contour-detailed features and integrates decision-making with contextual information. Additionally, we incorporate the Bi-Level Routing Attention (BRA) mechanism with C3 to enhance the algorithm's focus on critical areas, resulting in accurate, lightweight, and fast detection. The algorithm achieves a of 91.6% with only 1.8M parameters, the AP of the Female class and the Male class reaches 95% and 93.5%, which is an improvement of 3.8%, 1.2%, and 6.2% compared with the original algorithm. Furthermore, the Recall and F1-score of the algorithm are enhanced by 5.5% and 3.1%, respectively. Moreover, our model demonstrates significant advantages in detection speed, taking only 0.016s to process an image. The experimental results show that the algorithmic model proposed in this study can better assist the pollination of kiwifruit in the process of precision agriculture production and help the development of the kiwifruit industry.

Development and evaluation of precision liquid pollinator for kiwifruit

Cultivation of kiwifruit heavily relies on assisted pollination, and sufficient pollination can enhance fruit quality. A precision liquid pollinator for kiwifruit robotic pollination was developed as part of the research, and it was optimized with the aim of improving pollination quality while saving pollen usage. The pollinator employs a grating ruler to measure the stroke of a pneumatic hydraulic cylinder to achieve precise control of pollen suspension dosage, and uses an internal mixing air-assisted nozzles to spray pollen suspension for pollination. Through experiments of optimizing pollen suspension volume control, constructing of pollination distance prediction model, and measuring droplet size distribution, a working range of air pressure and liquid pressure was established. Eight sets of air–liquid pressure parameters were selected to evaluate the planar pollen deposition of the pollination device, and the optimal parameters were applied to pollinate kiwifruit flowers. The results indicated that reducing droplet size enhanced the overall pollen deposition rate.. In a windless laboratory environment, the developed pollinator achieved a pollen deposition efficiency of 21.15% under an air pressure of 0.15 MPa and liquid pressure of 0.25 MPa. This precision liquid pollination device is advantageous for precise control under high pressure and low flow conditions, suitable for the end effector of autonomous pollination robots. The optimization and evaluation methods established within this study provide reference for the development of precision pollination devices.

Predicting positions and orientations of individual kiwifruit flowers and clusters in natural environments

The positional and orientational detection of kiwifruit flower clusters in the natural environment is essential for the development of automated mechanical pollination systems. To ensure the timeliness and accuracy of this process, we propose a deep-learning-based method that predicts kiwifruit flower positions and orientations. Based on a single flower, this method effectively predicts the position and orientation of the entire cluster of flowers. First, the SOLOv2 instance segmentation model is used to locate and segment the location of the target kiwifruit flowers. Subsequently, the MobileNetV2 key point detection model is employed to detect the key points of the centre of gravity of the petal contour and the stamen part of the kiwifruit flower, and draw the orientation prediction line of the corresponding clusters. Finally, flower clusters are determined by calculating the Intersection over Union (IoU) of kiwi flowers. By taking the average of the orientation prediction line of each kiwi flower in a cluster as the entire cluster’s orientation, the method effectively generates a prediction of the said orientation. To verify the effectiveness of the proposed method, we compared and analysed the effects of SOLOv2 with the YOLOv5s, YOLOv4, and Faster-RCNN models with respect to the segmentation effect. The comparison results show that the SOLOv2 model exhibits a fast segmentation speed while maintaining a high segmentation accuracy. Verification results indicate that the SOLOv2 model has an accuracy rate of 74.2% for the detection of multi-target flowers of clustered kiwifruit from a wide perspective angle. It is therefore feasible to apply the proposed method to detect the positions and orientations of kiwi flower clusters in real time. This study can provide a technical reference for the development of robotic arms for the purpose of kiwifruit pollination.

Reasearch on Kiwi Fruit Flower Recognition for Efficient Pollination Based on an Improved YOLOv5 Algorithm

A close relationship has been observed between the growth and development of kiwi fruit and the pollination of the kiwi flower. Flower overlap, flower tilt, and other problems will affect this plant’s pollination success rate. A pollination model based on YOLOv5 was developed to improve the pollination of kiwi flowers. The K-means++ clustering method was used to cluster the anchors closer to the target size, which improved the speed of the algorithm. A convolutional block module attention mechanism was incorporated to improve the extraction accuracy with respect to kiwi flower features and effectively reduce the missed detection and error rates. The optimization of the detection function improves the recognition of flower overlap and the accuracy of flower tilt angle calculation and accurately determines flower coordinates, pollination point coordinates, and pollination angles. The experimental results show that the predicted value of the YOLOv5s model is 96.7% and that its recognition accuracy is the highest. Its mean average precision value is up to 89.1%, its F1 score ratio is 90.12%, and its memory requirements are the smallest (only 20 MB). The YOLOv5s model achieved the highest recognition accuracy as determined through a comparison experiment of the four sets of analysed models, thereby demonstrating its ability to facilitate the efficient target pollination of kiwi flowers.

Artificial pollination of kiwifruit (Actinidia chinensis Planch. var. chinensis) (Ericales: Actinidiaceae) results in greater fruit set compared to flowers pollinated by managed bees (Apis mellifera L. (Hymenoptera: Apidae) and Bombus impatiens Cresson (Hymenoptera: Apidae)).

Due to a lack of knowledge on the pollination requirements of kiwifruit cultivars grown within the United States, farmers simultaneously implement multiple pollination methods, like the rental of managed bee species or artificial pollination to achieve high fruit yields. However, implementing multiple pollination methods is costly and possibly an inefficient use of resources. We assessed the contribution of two managed bees (Apis mellifera and Bombus impatiens) to the pollination of kiwifruit by i) determining the relative abundance of kiwifruit pollen collected by foragers of each bee species, and ii) comparing fruit set and fruit quality among insect and artificially pollinated flowers through an insect exclusion experiment. A significant difference was observed between the mean relative abundance of kiwifruit pollen carried in the corbicula of A. mellifera and B. impatiens, with B. impatiens carrying on average 46% more kiwifruit pollen than A. mellifera. Artificially pollinated kiwifruit flowers set significantly greater numbers of fruit per flower at four weeks post-bloom and at harvest compared to insect pollination, wind pollination, and pollen exclusion treatment. Artificial pollination produced fruits of greater weight, size, and seed number compared to insect-pollinated flowers, and few fruits were produced in the pollen exclusion and wind pollination treatments. Kiwifruit producers experiencing similar conditions to ours should focus on artificially pollinating their crops rather than relying on managed or wild insects for kiwifruit pollination. Future research should evaluate other methods of artificial pollination to determine their effectiveness, efficiency, and economics in the pollination of kiwifruit grown within the United States.

A novel pollination robot for kiwifruit flower based on preferential flowers selection and precisely target

Manual pollination of kiwifruit flowers is a labor-intensive work that is highly desired to be replaced by mechanical/robotic operations. In this application note, a pollination robot was developed for achieving precision pollination of kiwifruit flowers in orchard. The pollination robot consists of five systems include vision system, air–liquid spray system, mechanical arm, crawler-type chassis, and control system, which could select suitable flowers and then target to their pistil for precision pollination. Field experiments were conducted in a commercial kiwifruit orchard to evaluate its feasibility and performance, which achieved an average pollination success rate of 99.3% with an average fruit set rate of 88.5%. Furthermore, compared to artificial assisted pollination methods, it could improve utilization rate of kiwifruit pollen with an average consumption of 0.15 g in 60 flowers. The validations demonstrated that this pollination robot could achieve efficient pollination for kiwifruit flowers and save pollen.

Genome-wide identification and characterization of AP2/ERF gene superfamily during flower development in Actinidia eriantha

BackgroundAs one of the largest transcription factor families in plants, AP2/ERF gene superfamily plays important roles in plant growth, development, fruit ripening and biotic and abiotic stress responses. Despite the great progress has been made in kiwifruit genomic studies, little research has been conducted on the AP2/ERF genes of kiwifruit. The increasing kiwifruit genome resources allowed us to reveal the tissue expression profiles of AP2/ERF genes in kiwifruit on a genome-wide basis.ResultsIn present study, a total of 158 AP2/ERF genes in A. eriantha were identified. All genes can be mapped on the 29 chromosomes. Phylogenetic analysis divided them into four main subfamilies based on the complete protein sequences. Additionally, our results revealed that the same subfamilies contained similar gene structures and conserved motifs. Ka/Ks calculation indicated that AP2/ERF gene family was undergoing a strong purifying selection and the evolutionary rates were slow. RNA-seq showed that the AP2/ERF genes were expressed differently in different flower development stages and 56 genes were considered as DEGs among three contrasts. Moreover, qRT-PCR suggested partial genes showed significant expressions as well, suggesting they could be key regulators in flower development in A. eriantha. In addition, two genes (AeAP2/ERF061, AeAP2/ERF067) had abundant transcription level based on transcriptomes, implying that they may play a crucial role in plant flower development regulation and flower tissue forming.ConclusionsWe identified AP2/ERF genes and demonstrated their gene structures, conserved motifs, and phylogeny relationships of AP2/ERF genes in two related species of kiwifruit, A. eriantha and A. chinensis, and their potential roles in flower development in A. eriantha. Such information would lay the foundation for further functional identification of AP2/ERF genes involved in kiwifruit flower development.

Multi-class detection of kiwifruit flower and its distribution identification in orchard based on YOLOv5l and Euclidean distance

Asynchrony of kiwifruit flowering time results in different flower phenological stages in canopy at the same time. Pollination quality of flowers is influenced by their phenological stages, while their distributions determine fruit distributions and influence kiwifruit quality and yield. Thus, it’s necessary to find suitable flowers to be pollinated based on flower phenology and its distribution. However, influences of flower phenology and flower distribution were not considered in most previous studies about robotic pollination of kiwifruit, where pollination of all open flowers was indiscriminate. Therefore, a method was proposed for multi-class detection of kiwifruit flower and its distribution identification in orchard, which was based on You Only Look Once version 5 large (YOLOv5l) and Euclidean distance. According to kiwifruit flower phenology, kiwifruit flowers were classified into 10 classes to find suitable flowers for pollination, while flower cluster and branch junction were divided into 4 classes for obtaining flower distributions. All classes were manually labeled by rectangular bounding boxes for training and testing. Considering high detection accuracy requirements with small model size, YOLOv5l was applied to do transfer learning for multi-class detection of kiwifruit flower. Then, pixels coordinate of multi-class objects and their corresponding Euclidean distances could be gained. Finally, flower distributions in canopy were obtained by matching method. Total mean Average Precision (mAP) was 91.60 % in YOLOv5l, while the mAP of multi-class flower (10 classes) was 93.23 %, which was 5.70 % higher than that of the other 4 classes. Matching accuracy (MA) of flowers matching flower clusters was up to 97.60 %. Moreover, MA of flower cluster matching branch junction (96.20 %) and total MA (93.30 %) increased by 1.20 % and 1.00 % based on improved matching method, respectively. Total processing speed of multi-class flower detection and its distribution identification was 112.46 ms per image including 15.50 ms for image detection by YOLOv5l. Results showed that multi-class kiwifruit flowers and relative flower distributions could be fast and accurately obtained for further selecting suitable flowers for robotic pollination.

Identification of the operating position and orientation of a robotic kiwifruit pollinator

Accurate identification of flower position and orientation is an essential prerequisite for robotic pollinators to accomplish precise targeted pollination and improve the pollen utilisation rate. To accurately identify the pollination position and orientation of kiwifruit flowers, this study presents a vision-based information perception approach for automatically detecting flowers and accurately identifying their positions and orientations. After using YOLO v4 to identify the kiwifruit flower, the contour detection algorithm was used to detect the contour boundary of the flower pistil area and the contour boundary of the minimum circumscribed circle of the petals, and fit the centres of gravity of the pistil contour and the petal contour according to the two contours respectively. The line connecting the two centre points was used as the central axis representing the growth direction of the flowers, achieving precise identification of the flower position and orientation. After building a robotic pollinator, an experiment was conducted to identify kiwifruit flower targets and a performance test experiment was performed to target pollinated kiwifruit flowers in a kiwifruit orchard with a mechanical arm based on flower position and orientation. The experimental results showed that the precision of the identification of kiwifruit flowers was 95.27%, the success rate of the mechanical arm for target pollination based on flower position and orientation identification was 89.59%, the average pollination time was 6 s⋅per flower, and the pollination efficiency was 20 h⋅ha-1. The identification algorithm of flower position and orientation proposed in this study can improve the precision and success rate of robotic pollination.

Influence of Bee Scent and other indigenous bee attractants on bee activity and fruiting behaviour of kiwifruit (Actinidia deliciosa A. Chev.)

Kiwifruit is a dioecious plant requiring cross pollination. The nectar in kiwifruit flowers is negligible which makes it less attractive to honey bees. Hand pollination is a common practice for effective pollination but this practice is inevitable, laborious and time consuming. Spraying of bee attractants during the periods of anthesis can be helpful in increasing the bee visitation to the flowers and improve yield as well as quality of kiwifruits. Keeping in view the possible beneficial effects of bee attractants, an experiment was carried out during 2018–19 to compare the practice of hand pollination with the application of commercial bee attractant ‘Bee Scent’ (1.25%) and indigenous bee attractants like jaggery solution (10%), sugar solution (10%), honey solution (10%) and sugarcane juice (10%) for their effects on bee activity, fruit set and fruit quality of kiwifruit. The sprays of Bee Scent to the flowers resulted in significant improvement in bee visitation to the flowers, fruit set, yield, proportion of ‘A’ and ‘B’ grade fruits, fruit size & weight, number of seeds, TSS, sugars and ascorbic acid contents. Sugarcane juice was also found equally effective in improving bee activity, fruit set and fruit quality, thus shown the potential to be used as a bee attractant in commercial kiwifruit production system.

Transcriptomic Analysis of Sex-Associated DEGs in Female and Male Flowers of Kiwifruit (Actinidia deliciosa [A. Chev] C. F. Liang & A. R. Ferguson)

Kiwifruit (Actinidia deliciosa [A. Chev.], C.V. Liang & A. R. Ferguson, 1984) is a perennial plant, with morphologically hermaphroditic and functionally dioecious flowers. Fruits of this species are berries of great commercial and nutritional importance. Nevertheless, few studies have analyzed the molecular mechanisms involved in sexual differentiation in this species. To determine these mechanisms, we performed RNA-seq in floral tissue at stage 60 on the BBCH scale in cultivar ‘Hayward’ (H, female) and a seedling from ‘Green Light’ × ‘Tomuri’ (G × T, male). From these analyses, we obtained expression profiles of 24,888 (H) and 27,027 (G × T) genes, of which 6413 showed differential transcript abundance. Genetic ontology (GO) and KEGG analysis revealed activation of pathways associated with the translation of hormonal signals, plant-pathogen interaction, metabolism of hormones, sugars, and nucleotides. The analysis of the protein-protein interaction network showed that the genes ERL1, AG, AGL8, LFY, WUS, AP2, WRKY, and CO, are crucial elements in the regulation of the hormonal response for the formation and development of anatomical reproductive structures and gametophytes. On the other hand, genes encoding four Putative S-adenosyl-L-methionine-dependent methyltransferases (Achn201401, Achn281971, Achn047771 and Achn231981) were identified, which were up-regulated mainly in the male flowers. Moreover, the expression profiles of 15 selected genes through RT-qPCR were consistent with the results of RNA-seq. Finally, this work provides gene expression-based interactions between transcription factors and effector genes from hormonal signaling pathways, development of floral organs, biological and metabolic processes or even epigenetic mechanisms which could be involved in the kiwi sex-determination. Thus, in order to decode the nature of these interactions, it could be helpful to propose new models of flower development and sex determination in the Actinidia genus.

Real-time detection of kiwifruit flower and bud simultaneously in orchard using YOLOv4 for robotic pollination

Robotic pollination may help with reducing high labor requirements and saving expensive pollen on artificial pollination of kiwifruit. Fast and accurate detection of kiwifruit flower and bud simultaneously in an orchard is essential for robotic pollination. It not only makes precision pollination possible but also can predict blooming peak to estimate the optimal pollination timing. However, only kiwifruit flower has been labeled and detected in recent studies. Therefore, kiwifruit flower and bud were labeled, trained, and detected simultaneously for robotic pollination. Well-known YOLOv3 and recently released YOLOv4 were applied to do transfer learning for kiwifruit flower and bud detection. Both were trained in the same image dataset and compared by Average Precision (AP) and processing speed, which were aimed to find a better model. Results showed that mean AP (mAP) of YOLOv4 (97.61%) was higher than YOLOv3 (95.24%) on kiwifruit flower and bud detection. The AP of flower and bud detection achieved by YOLOv4 were 96.66% and 98.57%, respectively, which were 0.17% and 4.58% higher than that of YOLOv3. The detection speed of YOLOv4 was 38.64 ms per image with 4608 × 3456 pixels, which was resized to 608 × 608 pixels in detection. In addition, another image dataset was collected from different years and locations to demonstrate the generalizability of YOLOv3 and YOLOv4, which reported mAPs of 80.98% and 91.49% on them, respectively. It can be concluded that YOLOv4 is promising to achieve real-time detection of kiwifruit flower and bud simultaneously for further flower blooming peak estimation and robotic pollination.

Sex change in kiwifruit (Actinidia chinensis Planch.): a developmental framework for the bisexual to unisexual floral transition.

The developmental morphology of male and female kiwifruit flowers is tracked to delimit a framework of events to aid the study of divergence in floral gene expression. The transition from hermaphrodite to unisexual development of kiwifruit (Actinidia chinensis Planch) flowers has been reported previously, but differences in gene expression controlling sexual development for this species have not been associated with the major developmental changes occurring within pistils. We investigated the key stages in male and female flower development to define the point at which meristematic activities diverge in the two sexes. A combination of scanning electron microscopy and light microscopy was used to investigate pistil development from the earliest stages. We identified seven distinct stages characterized by differences in ovary size and shape, macrosporogenesis, ovule primordium development, anther locule lengthening, microspore wall thickening, and pollen degeneration. Sex differences were evident from the initial stage of development, with a laterally compacted gynoecium in male flowers. However, the key developmental stage, at which tissue differentiation clearly deviated between the two sexes, was stage 3, when flowers were 3.5 to 4.5mm in length at approximately 10 d from initiation of stamen development. At this stage, male flowers lacked evident carpel meristem development as denoted by a lack of ovule primordium formation. Pollen degeneration in female flowers, probably driven by programmed cell death, occurred at the late stage 6, while the final stage 7 was represented by pollen release. As the seven developmental stages are associated with specific morphological differences, including flower size, the scheme suggested here can provide the required framework for the future study of gene expression during the regulation of flower development in this crop species.

Autonomous pollination of individual kiwifruit flowers: Toward a robotic kiwifruit pollinator

AbstractThere is an increasing concern that the traditional approach of natural kiwifruit pollination by bees may not be sustainable. The alternatives are currently too costly for most growers due to high labor requirements or inefficient usage of expensive pollen. This paper presents a performance evaluation of a novel kiwifruit pollinating robot designed to provide a more efficient, reliable, and cost‐effective means of producing kiwifruit. The robot comprises a novel air‐assisted sprayer, a machine vision system employing convolution neural networks, and a flower targeting system for efficient and effective application of pollen to individual flowers. We show that this pollination system is capable of individually targeting and pollinating 79.5% of flowers at 3.5 km/hr while using comparable amounts of pollen to commercial Cambrian operators. Furthermore, flowers that were successfully pollinated at 1 km/hr grew into the first robotically pollinated kiwifruit which were comparable in quality to commercially grown kiwifruit. However, the overall fruit set was found to be well below commercial requirements and further work on increasing the overall yield is required.

Plant Microbiome and Its Link to Plant Health: Host Species, Organs and Pseudomonas syringae pv. actinidiae Infection Shaping Bacterial Phyllosphere Communities of Kiwifruit Plants.

Pseudomonas syringae pv. actinidiae (Psa) is the causal agent of the bacterial canker, the most devastating disease of kiwifruit vines. Before entering the host tissues, this pathogen has an epiphytic growth phase on kiwifruit flowers and leaves, thus the ecological interactions within epiphytic bacterial community may greatly influence the onset of the infection process. The bacterial community associated to the two most important cultivated kiwifruit species, Actinidia chinensis and Actinidia deliciosa, was described both on flowers and leaves using Illumina massive parallel sequencing of the V3 and V4 variable regions of the 16S rRNA gene. In addition, the effect of plant infection by Psa on the epiphytic bacterial community structure and biodiversity was investigated. Psa infection affected the phyllosphere microbiome structures in both species, however, its impact was more pronounced on A. deliciosa leaves, where a drastic drop in microbial biodiversity was observed. Furthermore, we also showed that Psa was always present in syndemic association with Pseudomonas syringae pv. syringae and Pseudomonas viridiflava, two other kiwifruit pathogens, suggesting the establishment of a pathogenic consortium leading to a higher pathogenesis capacity. Finally, the analyses of the dynamics of bacterial populations provided useful information for the screening and selection of potential biocontrol agents against Psa.

Regulation of flowering in kiwifruit

Regulation of flowering in kiwifruit

Evaluating the length of time that kiwifruit flowers can be successfully pollinated

The purpose of this research was to investigate the length of time that flowers of Actinidia chinensis ‘AU Golden Sunshine’ and Actinidia deliciosa ‘AU Fitzgerald’ can be successfully pollinated. This study consisted of isolating flowers 1 d prior to anthesis and hand-pollinating approximately 30 different flowers each day for 5-6 d following anthesis. Fruit set of ‘AU Golden Sunshine’ was similar for flowers pollinated up to 5 d following anthesis. Fruit set of ‘AU Fitzgerald’ was similar for flowers pollinated up to 5 d following anthesis, with reduced fruit set realized for flowers pollinated on day 6. Fruit weight and seed number of harvested fruit tended to decrease as flowers were pollinated later with respect to anthesis.

Overexpression of both AcSVP1 and AcSVP4 delays budbreak in kiwifruit A. chinensis var. deliciosa, but only AcSVP1 delays flowering in model plants

Kiwifruit SHORT VEGETATIVE PHASE (AcSVP) genes have been implicated in regulation of dormancy and flowering. Previously, we have shown that AcSVP2 and AcSVP3 have different effects on dormancy and flowering in transgenic kiwifruit and model plants. The role of two homologous genes, AcSVP1 and AcSVP4, remained unclear. Here, AcSVP1 and AcSVP4 were functionally characterized by ectopic expression in Actinidia species which show different chilling requirements for budbreak, as well as the model plant tobacco. Overexpression of AcSVP1 and AcSVP4 delayed spring budbreak in a high-chill A. chinensis var. deliciosa, but did not affect budbreak and flowering in a low-chill A. eriantha. Overexpression of AcSVP1 delayed flowering and seed germination in tobacco, while overexpression of AcSVP4 had only minor effects. Analysis of cis-regulatory motifs in the promoter regions of AcSVP1 and AcSVP4 and promoter transactivation assays revealed that AcSVP1 and AcSVP4 may be controlled by similar transcription factors, suggesting a conserved regulatory mechanism and a synergistic role for AcSVP1 and AcSVP4 in preventing premature kiwifruit budbreak during winter.