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Summary Fruits with few or no seeds are favoured by consumers because they provide an improved eating experience alongside other important quality traits such as taste and shelf‐life. Gamma irradiation has been widely used to induce favourable trait changes in plants, including a reduction in seediness. For this reason, it has been extremely important in the development of new commercial citrus cultivars. The variety IrM2 is a mutant derived from the mandarin variety, Murcott, by gamma irradiation. IrM2 has improved consumer and economic appeal due to its earlier fruit maturity time, low number of seeds and improved external skin colour compared with its progenitor. Here, we developed high‐quality, haplotype‐resolved genomes for Murcott and IrM2, using PacBio HiFi and Hi‐C sequencing. The assemblies ranged from 329 to 344 Mb, with N50s of more than 30 Mb, and more than 98% assembly and annotation completeness for the four haplotypes. Duplications, inversions, translocations and INDELs were the predominant types of mutations found in IrM2. Two large heterozygous inversions (3.1 Mb in Chr3 and 8.6 Mb in Chr6) and one large heterozygous, non‐reciprocal translocation (between Chr3 and Chr6) were prominent in IrM2 and may be the causes of the reduced seeds. Variations such as insertions and deletions were also found, resulting in additions and loss of genes in IrM2. The genes lost in IrM2 were associated with many processes, including hormone signalling, flowering, DNA transcription, reproduction, gene expression and transmembrane transport. These high‐quality genomes contribute to a deeper understanding of how irradiation affects plant genomes.

Background Genome structural variations (SVs) have been associated with key traits in a wide range of agronomically important species; however, SV profiles of peach and their functional impacts remain largely unexplored. Results Here, we present an integrated map of 202,273 SVs from 336 peach genomes. A substantial number of SVs have been selected during peach domestication and improvement, which together affect 2268 genes. Genome-wide association studies of 26 agronomic traits using these SVs identify a number of candidate causal variants. A 9-bp insertion in Prupe.4G186800 , which encodes a NAC transcription factor, is shown to be associated with early fruit maturity, and a 487-bp deletion in the promoter of PpMYB10.1 is associated with flesh color around the stone. In addition, a 1.67 Mb inversion is highly associated with fruit shape, and a gene adjacent to the inversion breakpoint, PpOFP1 , regulates flat shape formation. Conclusions The integrated peach SV map and the identified candidate genes and variants represent valuable resources for future genomic research and breeding in peach.

Fruit maturity is a critical factor in the supply chain, consumer preference, and agriculture industry. Most classification methods on fruit maturity identify only two classes: ripe and unripe, but this paper estimates six maturity stages of papaya fruit. Deep learning architectures have gained respect and brought breakthroughs in unimodal processing. This paper suggests a novel non-destructive and multimodal classification using deep convolutional neural networks that estimate fruit maturity by feature concatenation of data acquired from two imaging modes: visible-light and hyperspectral imaging systems. Morphological changes in the sample fruits can be easily measured with RGB images, while spectral signatures that provide high sensitivity and high correlation with the internal properties of fruits can be extracted from hyperspectral images with wavelength range in between 400 nm and 900 nm—factors that must be considered when building a model. This study further modified the architectures: AlexNet, VGG16, VGG19, ResNet50, ResNeXt50, MobileNet, and MobileNetV2 to utilize multimodal data cubes composed of RGB and hyperspectral data for sensitivity analyses. These multimodal variants can achieve up to 0.90 F1 scores and 1.45% top-2 error rate for the classification of six stages. Overall, taking advantage of multimodal input coupled with powerful deep convolutional neural network models can classify fruit maturity even at refined levels of six stages. This indicates that multimodal deep learning architectures and multimodal imaging have great potential for real-time in-field fruit maturity estimation that can help estimate optimal harvest time and other in-field industrial applications.

Pyracantha crenulata is a high value multipurpose shrub of the Himalaya. Fruits of the species possess several therapeutic and nutraceutical properties and are edible. Undertaking phenological studies on high-value wild plant species, particularly the ones that are yet to be domesticated, is particularly important as they provide baseline data on patterns of their vegetative growth, flowering, fruiting, fruit maturity, seed set etc. Considering these facts, the present study was carried out with the aim to identify and document the phenological features of P. crenulata and to study patterns of its phenological events under two habitat conditions. The study was carried out at two closely located sites, different in microsite conditions, at Pithoragarh (Uttarakhand), India, under the Western Himalaya in the Indian Himalayan Region (IHR) for two consecutive years. Five phenological events (emergence of new leaves and twigs; flowering; fruit setting and development; fruit drop and leaf fall) were recorded at fortnightly intervals, i.e., first fortnight and second fortnight in each month on selected individuals of the species, in both the sites. All the phenological phases investigated followed identical or near identical patterns across both sites so that there were no significant differences (P<0.05) between the sites in terms of phenological events and growth parameters recorded. Being an economically and ecologically valuable species, findings on the patterns of flowering and fruiting of P. crenulata are significant not only for management of its natural populations in the region, but will also serve as a baseline data for studies to be carried out for its domestication in future.

Citrus fruits do not ripen at the same time in natural environments and exhibit different maturity stages on trees, hence it is necessary to realize selective harvesting of citrus picking robots. The visual attention mechanism reveals a physiological phenomenon that human eyes usually focus on a region that is salient from its surround. The degree to which a region contrasts with its surround is called visual saliency. This study proposes a novel citrus fruit maturity method combining visual saliency and convolutional neural networks to identify three maturity levels of citrus fruits. The proposed method is divided into two stages: the detection of citrus fruits on trees and the detection of fruit maturity. In stage one, the object detection network YOLOv5 was used to identify the citrus fruits in the image. In stage two, a visual saliency detection algorithm was improved and generated saliency maps of the fruits; The information of RGB images and the saliency maps were combined to determine the fruit maturity class using 4-channel ResNet34 network. The comparison experiments were conducted around the proposed method and the common RGB-based machine learning and deep learning methods. The experimental results show that the proposed method yields an accuracy of 95.07%, which is higher than the best RGB-based CNN model, VGG16, and the best machine learning model, KNN, about 3.14% and 18.24%, respectively. The results prove the validity of the proposed fruit maturity detection method and that this work can provide technical support for intelligent visual detection of selective harvesting robots.

Aquaporins (AQPs) in plants can transport water and small molecules, and they play an important role in plant development and abiotic stress response. However, to date, a comprehensive study on AQP family members is lacking. In this study, 27 AQP genes were identified from the passion fruit genome and classified into four groups (NIP, PIP, TIP, SIP) on the basis of their phylogenetic relationships. The prediction of protein interactions indicated that the AQPs of passion fruit were mainly associated with AQP family members and boron protein family genes. Promoter cis-acting elements showed that most PeAQPs contain light response elements, hormone response elements, and abiotic stress response elements. According to collinear analysis, passion fruit is more closely related to Arabidopsis than rice. Furthermore, three different fruit ripening stages and different tissues were analyzed on the basis of the transcriptome sequencing results for passion fruit AQPs under drought, high-salt, cold and high-temperature stress, and the results were confirmed by qRT-PCR. The results showed that the PeAQPs were able to respond to different abiotic stresses, and some members could be induced by and expressed in response to multiple abiotic stresses at the same time. Among the three different fruit ripening stages, 15 AQPs had the highest expression levels in the first stage. AQPs are expressed in all tissues of the passion fruit. One of the passion fruit aquaporin genes, PeTIP3-2, which was induced by drought stress, was selected and transformed into Arabidopsis. The survival rate of transgenic plants under drought stress treatment is higher than that of wild-type plants. The results indicated that PeTIP3-2 was able to improve the drought resistance of plants. Our discovery lays the foundation for the functional study of AQPs in passion fruit.

Measurement of plant characteristics is still the primary bottleneck in both plant breeding and crop management. Rapid and accurate acquisition of information about large plant populations is critical for monitoring plant health and dissecting the underlying genetic traits. In recent years, high-throughput phenotyping technology has benefitted immensely from both remote sensing and machine learning. Simultaneous use of multiple sensors (e.g., high-resolution RGB, multispectral, hyperspectral, chlorophyll fluorescence, and light detection and ranging (LiDAR)) allows a range of spatial and spectral resolutions depending on the trait in question. Meanwhile, computer vision and machine learning methodology have emerged as powerful tools for extracting useful biological information from image data. Together, these tools allow the evaluation of various morphological, structural, biophysical, and biochemical traits. In this review, we focus on the recent development of phenomics approaches in strawberry farming, particularly those utilizing remote sensing and machine learning, with an eye toward future prospects for strawberries in precision agriculture. The research discussed is broadly categorized according to strawberry traits related to (1) fruit/flower detection, fruit maturity, fruit quality, internal fruit attributes, fruit shape, and yield prediction; (2) leaf and canopy attributes; (3) water stress; and (4) pest and disease detection. Finally, we present a synthesis of the potential research opportunities and directions that could further promote the use of remote sensing and machine learning in strawberry farming.

Anthocyanin accumulation is coordinated in plants by a number of conserved transcription factors. In apple (Malus × domestica), an R2R3 MYB transcription factor has been shown to control fruit flesh and foliage anthocyanin pigmentation (MYB10) and fruit skin color (MYB1). However, the pattern of expression and allelic variation at these loci does not explain all anthocyanin-related apple phenotypes. One such example is an open-pollinated seedling of cv Sangrado that has green foliage and develops red flesh in the fruit cortex late in maturity. We used methods that combine plant breeding, molecular biology, and genomics to identify duplicated MYB transcription factors that could control this phenotype. We then demonstrated that the red-flesh cortex phenotype is associated with enhanced expression of MYBllOa, a paralog of MYB10. Functional characterization of MYB110a showed that it was able to up-regulate anthocyanin biosynthesis in tobacco (Nicotiana tabacum). The chromosomal location of MYB110a is consistent with a whole-genome duplication event that occurred during the evolution of apple within the Maloideae family. Both MYB10 and MYB110a have conserved function in some cultivars, but they differ in their expression pattern and response to fruit maturity.

Automatic harvesting robots are crucial for enhancing agricultural productivity, and precise fruit maturity detection is a fundamental and core technology for efficient and accurate harvesting. Strawberries are distributed irregularly, and their images contain a wealth of characteristic information. This characteristic information includes both simple and intuitive features, as well as deeper abstract meanings. These complex features pose significant challenges to robots in determining fruit ripeness. To increase the precision, accuracy, and efficiency of robotic fruit maturity detection methods, a strawberry maturity detection algorithm based on an improved CES-YOLOv8 network structure from YOLOv8 was developed in this study. Initially, to reflect the characteristics of actual planting environments, the study collected image data under various lighting conditions, degrees of occlusion, and angles during the data collection phase. Subsequently, parts of the C2f module in the YOLOv8 model’s backbone were replaced with the ConvNeXt V2 module to enhance the capture of features in strawberries of varying ripeness, and the ECA attention mechanism was introduced to further improve feature representation capability. Finally, the angle compensation and distance compensation of the SIoU loss function were employed to enhance the IoU, enabling the rapid localization of the model’s prediction boxes. The experimental results show that the improved CES-YOLOv8 model achieves an accuracy, recall rate, mAP50, and F1 score of 88.20%, 89.80%, 92.10%, and 88.99%, respectively, in complex environments, indicating improvements of 4.8%, 2.9%, 2.05%, and 3.88%, respectively, over those of the original YOLOv8 network. This algorithm provides technical support for automated harvesting robots to achieve efficient and precise automated harvesting. Additionally, the algorithm is adaptable and can be extended to other fruit crops.

In order to achieve accurate, fast, and robust recognition of blueberry fruit maturity stages for edge devices such as orchard inspection robots, this research proposes a lightweight detection method based on an improved YOLOv5 algorithm. In the improved YOLOv5 algorithm, the ShuffleNet module is used to achieve lightweight deep-convolutional neural networks. The Convolutional Block Attention Module (CBAM) is also used to enhance the feature fusion capability of lightweight deep-convolutional neural networks. The effectiveness of this method is evaluated using the blueberry fruit dataset. The experimental results demonstrate that this method can effectively detect blueberry fruits and recognize their maturity stages in orchard environments. The average recall (R) of the detection is 92.0%. The mean average precision (mAP) of the detection at a threshold of 0.5 is 91.5%. The average speed of the detection is 67.1 frames per second (fps). Compared to other detection algorithms, such as YOLOv5, SSD, and Faster R-CNN, this method has a smaller model size, smaller network parameters, lower memory usage, lower computation usage, and faster detection speed while maintaining high detection performance. It is more suitable for migration and deployment on edge devices. This research can serve as a reference for the development of fruit detection systems for intelligent orchard devices.