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The precise segmentation of Zanthoxylum bungeanum clusters is crucial for developing picking robots. An improved Mask R-CNN model was proposed in this study for the segmentation of Zanthoxylum bungeanum clusters in natural environments. Firstly, the Swin-Transformer network was introduced into the model's backbone as the feature extraction network to enhance the model's feature extraction capabilities. Then, the SK attention mechanism was utilized to fuse the detailed information into the mask branch from the low-level feature map of the feature pyramid network (FPN), aiming to supplement the image detail features. Finally, the distance intersection over union (DIOU) loss function was adopted to replace the original bounding box loss function of Mask R-CNN. The model was trained and tested based on a self-constructed Zanthoxylum bungeanum cluster dataset. Experiments showed that the improved Mask R-CNN model achieved 84.0% and 77.2% in detection mAP 50 box and segmentation mAP 50 mask , respectively, representing a 5.8% and 4.6% improvement over the baseline Mask R-CNN model. In comparison to conventional instance segmentation models, such as YOLACT, Mask Scoring R-CNN, and SOLOv2, the improved Mask R-CNN model also exhibited higher segmentation precision. This study can provide valuable technology support for the development of Zanthoxylum bungeanum picking robots. [ABSTRACT FROM AUTHOR]

Loquat leaves exhibiting obvious yellowing, blistering, mosaic, leaf upward cupping, crinkle, and leaf narrowing were identified in Panzhihua City, Sichuan Province, China. High-throughput sequencing (HTS) with the ribo-depleted cDNA library was employed to identify the virome in the loquat samples; only tomato mosaic virus (ToMV) and citrus exocortis viroid (CEVd) were identified in the transcriptome data. The complete genome sequence of ToMV and CEVd were obtained from the loquat leaves. The full-length genome of the ToMV-loquat is 6376 nt and comprises four open reading frames (ORFs) encoding 183 kDa protein, RNA-dependent RNA polymerase (RdRp), movement protein (MP), and coat protein (CP), respectively. A pairwise identity analysis showed that the complete sequence of the ToMV-loquat had a nucleotide identity between 98.5 and 99.3% with other ToMV isolates. A phylogenetic analysis indicated that ToMV-loquat was more closely related to ToMV-IFA9 (GenBank No. ON156781). A CEVd sequence with 361 nt in length was amplified based on the HTS contigs, sequence alignment indicated CEVd-loquat had the highest identity with the strain of CEVd-Balad (GenBank No. PP869624), phylogenetic analysis showed that CEVd-loquat was more closely related to CEVd-lettuce (GenBank No. ON993891). This significant discovery marks the first documentation and characterization of ToMV and CEVd infecting loquat plants, shedding light on potential threats to loquat cultivation and providing insights for disease management strategies. [ABSTRACT FROM AUTHOR]

Mechanical weeding is an important technical means for organic and regenerative agricultural systems. Current weed control equipment has a variety of problems, such as difficulty adapting to high-stalk crops and poor operational quality. A high-clearance mid-tillage weeder (HMTW) has been developed to meet the mechanical weed control needs of high-stalk crops. The weeder mainly comprises a suspension device, a frame, parallel four-rod profiling mechanisms, weeding operation components, and depth-limiting soil-cutting devices. Based on the agronomic requirements of dryland flat planting, the overall structure of the HMTW was determined, and the weeding unit and flat shovel hoe were designed. Theoretical analysis was conducted on the depth stability of the HMTW, and an optimization mathematical model of the HMTW was established to further improve its tillage depth stability for agronomic requirements. The optimization objective was to minimize the deflection angle (∆β) of the profiling rod on a vertical plane, and the parameters of the parallel four-rod profiling mechanism were optimized. Based on the optimized structural parameters, a prototype of the HMTW was developed and evaluated. The test results show that the optimized HMTW exhibited a good weeding effect, and the tillage depth stability was within the design operating range. When the driving speed was 1.0 m/s and the tillage depth was 8 cm, the weed removal rate, seedling injury rate, seedling burial rate, and qualified rate of tillage depth were 90.8%, 3.2%, 4.1%, and 94%, respectively. The proposed HMTW successfully meets the weeding agronomic requirements of high-stalk crops for dryland farming, and the performance analysis and optimization models provide technical references for the design and development of such structures. [ABSTRACT FROM AUTHOR]

The loquat (Eriobotrya japonica L.) is a special evergreen tree, and its fruit is of high medical and health value as well as having stable market demand around the world. In recent years, research on the accumulation of nutrients in loquat fruit, such as carotenoids, flavonoids, and terpenoids, has become a hotspot. The SBP-box gene family encodes transcription factors involved in plant growth and development. However, there has been no report on the SBP-box gene family in the loquat genome and their functions in carotenoid biosynthesis and fruit ripening. In this study, we identified 28 EjSBP genes in the loquat genome, which were unevenly distributed on 12 chromosomes. We also systematically investigated the phylogenetic relationship, collinearity, gene structure, conserved motifs, and cis-elements of EjSBP proteins. Most EjSBP genes showed high expression in the root, stem, leaf, and inflorescence, while only five EjSBP genes were highly expressed in the fruit. Gene expression analysis revealed eight differentially expressed EjSBP genes between yellow- and white-fleshed fruits, suggesting that the EjSBP genes play important roles in loquat fruit development at the breaker stage. Notably, EjSBP01 and EjSBP19 exhibited completely opposite expression patterns between white- and yellow-fleshed fruits during fruit development, and showed a close relationship with SlCnr involved in carotenoid biosynthesis and fruit ripening, indicating that these two genes may participate in the synthesis and accumulation of carotenoids in loquat fruit. In summary, this study provides comprehensive information about the SBP-box gene family in the loquat, and identified two EjSBP genes as candidates involved in carotenoid synthesis and accumulation during loquat fruit development. [ABSTRACT FROM AUTHOR]

Breeding technology is one of the necessary means for agricultural development, and the automatic identification of poor seeds has become a trend in modern breeding. China is one of the main producers of buckwheat, and the cultivation of Hongshan buckwheat plays an important role in agricultural production. The quality of seeds affects the final yield, and improving buckwheat breeding technology is particularly important. In order to quickly and accurately identify broken Hongshan buckwheat seeds, an identification algorithm based on an improved YOLOv5s model is proposed. Firstly, this study added the Ghost module to the YOLOv5s model, which improved the model's inference speed. Secondly, we introduced the bidirectional feature pyramid network (BiFPN) to the neck of the YOLOv5s model, which facilitates multi-scale fusion of Hongshan buckwheat seeds. Finally, we fused the Ghost module and BiFPN to form the YOLOV5s+Ghost+BiFPN model for identifying broken Hongshan buckwheat seeds. The results show that the precision of the YOLOV5s+Ghost+BiFPN model is 99.7%, which is 11.7% higher than the YOLOv5s model, 1.3% higher than the YOLOv5+Ghost model, and 0.7% higher than the YOLOv5+BiFPN model. Then, we compared the FLOPs value, model size, and confidence. Compared to the YOLOv5s model, the FLOPs value decreased by 6.8 G, and the model size decreased by 5.2 MB. Compared to the YOLOv5+BiFPN model, the FLOPs value decreased by 8.1 G, and the model size decreased by 7.3MB. Compared to the YOLOv5+Ghost model, the FLOPs value increased by only 0.9 G, and the model size increased by 1.4 MB, with minimal numerical fluctuations. The YOLOv5s+Ghost+BiFPN model has more concentrated confidence. The YOLOv5s+Ghost+BiFPN model is capable of fast and accurate recognition of broken Hongshan buckwheat seeds, meeting the requirements of lightweight applications. Finally, based on the improved YOLOv5s model, a system for recognizing broken Hongshan buckwheat seeds was designed. The results demonstrate that the system can effectively recognize seed features and provide technical support for the intelligent selection of Hongshan buckwheat seeds. [ABSTRACT FROM AUTHOR]

In the production process of sweet cherry, there are unreasonable planting densities and tree shape selections. With increasing tree age, the crown of the tree continues to expand and the tree body is prone to canopy closure, which leads to the inability to efficiently use space and light energy. Low-light has become a major limiting factor in the quality of sweet cherry. Therefore, we analyzed the changes of various physiological indicators and the transcriptome of 'Hongdeng' sweet cherry under shading treatment in this study to investigate the effects of low-light stress on the photosynthetic characteristics of sweet cherry leaves and fruit physiology and biochemistry. The results showed that shading significantly reduces the light capture capacity of leaves, damages the photosystem, reduces carbon assimilation capacity, and consumes the majority of the captured light energy as photochemical energy, thereby restricting the growth and development of leaves and reducing the accumulation of nutrients in fruits. Shading significantly reduced fruit weight, sugar content, and vitamin C content at maturity and significantly increased acid content. Transcriptomic data demonstrated that low-light stress produces a large number of differential genes related to carbon metabolism, organic acid metabolism, and stress resistance, thereby suggesting that low-light stress may affect the expression of these related genes and inclusions in the fruit. The results of this study will provide theoretical and technical support for the physiological response mechanism of low-light tolerance in sweet cherry, the selection and breeding of low-light tolerant sweet cherry varieties, and the cultivation of sweet cherry in facilities. [ABSTRACT FROM AUTHOR]

Buckwheat is an important minor grain crop with medicinal and edible functions. The accurate judgment of buckwheat maturity is beneficial to reduce harvest losses and improve yield. With the rapid development of unmanned aerial vehicle (UAV) technology, it has been widely used to predict the maturity of agricultural products. This paper proposed a method using recursive feature elimination cross-validation (RFECV) combined with multiple regression models to predict the maturity of buckwheat in UAV-RGB images. The images were captured in the buckwheat experimental field of Shanxi Agricultural University in Jinzhong, Northern China, from September to October in 2021. The variety was sweet buckwheat of "Jinqiao No. 1". In order to deeply mine the feature vectors that highly correlated with the prediction of buckwheat maturity, 22 dimensional features with 5 vegetation indexes, 9 color features, and 8 texture features of buckwheat were selected initially. The RFECV method was adopted to obtain the optimal feature vector dimensions and combinations with six regression models of decision tree regression, linear regression, random forest regression, AdaBoost regression, gradient lifting regression, and extreme random tree regression. The coefficient of determination ( R 2 ) and root mean square error (RMSE) were used to analyze the different combinations of the six regression models with different feature spaces. The experimental results show that the single vegetation index performed poorly in the prediction of buckwheat maturity; the prediction result of feature space "5" combined with the gradient lifting regression model performed the best; and the R 2 and RMSE were 0.981 and 1.70 respectively. The research results can provide an important theoretical basis for the prediction of the regional maturity of crops. [ABSTRACT FROM AUTHOR]

Previous studies indicated that extensive genetic variations could be generated due to polyploidy, which is considered to be closely associated with the manifestation of polyploid heterosis. Our previous studies confirmed that triploid loquats demonstrated significant heterosis, other than the ploidy effect, but the underlying mechanisms are largely unknown. This study aimed to overcome the narrow genetic distance of loquats, increase the genetic variation level of triploid loquats, and systematically illuminate the heterosis mechanisms of triploid loquats derived from two cross combinations. Here, inter-simple sequence repeats (ISSRs) and simple sequence repeats (SSRs) were adopted for evaluating the genetic diversity, and transcriptome sequencing (RNA-Seq) was performed to investigate gene expression as well as pathway changes in the triploids. We found that extensive genetic variations were produced during the formation of triploid loquats. The polymorphism ratios of ISSRs and SSRs were 43.75% and 19.32%, respectively, and almost all their markers had a PIC value higher than 0.5, suggesting that both ISSRs and SSRs could work well in loquat assisted breeding. Furthermore, our results revealed that by broadening the genetic distance between the parents, genetic variations in triploids could be promoted. Additionally, RNA-Seq results suggested that numerous genes differentially expressed between the triploids and parents were screened out. Moreover, KEGG analyses revealed that "photosynthetic efficiency" and "glyco-metabolism" were significantly changed in triploid loquats compared with the parents, which was consistent with the results of physiological indicator analyses, leaf micro-structure observations, and qRT-PCR validation. Collectively, our results suggested that extensive genetic variations occurred in the triploids and that the changes in the "photosynthetic efficiency" as well as "glyco-metabolism" of triploids might have further resulted in heterosis manifestation in the triploid loquats. [ABSTRACT FROM AUTHOR]

Peanut growth in Shandong Province, a major peanut-producing area in China, is greatly affected by drought. The present study uses hierarchical analysis, weighted comprehensive evaluation, and ArcGIS spatial analysis to conduct spatial zoning of peanut drought risk in Shandong Province based on daily precipitation data for the province acquired from 1991 to 2020, the per capita GDP, and the peanut planting area of Shandong Province, so as to quantify the disaster risk of peanut drought and formulate disaster prevention and resilience planning accordingly. The results show the high-drought-risk zone was mainly distributed in the northwestern part of Shandong Province and on the Jiaodong Peninsula, covering 32.4% of the province. Drought risk was concentrated on the Jiaodong Peninsula, covering 20.7% of the province. The high-vulnerability zone was mainly distributed in the cities of Yantai, Weihai, Linyi, and Rizhao, accounting for 26.8% of the total area. The low-disaster-prevention and low-mitigation-capacity zone was mainly distributed in the western part of Shandong Province, covering 38.7% of the province. Medium- and high-risk areas for drought affecting peanuts were widely distributed, while the overall comprehensive risk index was high, covering 76.2% of the province. Spatial analysis to conduct risk zoning and assessment of peanut drought in Shandong Province, so as to provide a basis for peanut drought disaster prevention and safe peanut production in Shandong Province. [ABSTRACT FROM AUTHOR]

D-2-hydroxyglutaric acid (D2HG) is overproduced as a result of the D-2-hydroxyglutaric aciduria and relevant cancers, caused by gene mutation. Accurate analysis of D2HG could help rapid diagnosis of these diseases and allow for timely treatment. In this work, a D-2-hydroxyglutarate dehydrogenase from Ralstonia solanacearum (RsD2HGDH) is cloned and recombinantly expressed. This enzyme features the direct electron transfer to chemical electron mediators (such as methylene blue (MB)) in the absence of additional coenzymes. Therefore, NAD+, a natural electron acceptor for the commercial D2HGDH and usually known for being unstable and difficult for immobilization can be avoided in the preparation of biosensors. The RsD2HGDH and MB are co-immobilized on a two-dimensional material, Ti3C2 MXene, followed by drop-coating on the gold screen-printed electrode (AuSPE) to construct a compact and portable biosensor. The D2HG in samples can be catalyzed by RsD2HGDH, where the current change is measured by chronoamperometry at −0.23 V. The biosensor shows a D2HG detection range of 0.5 to 120 µM (R2 = 0.9974) with a sensitivity of 22.26 μA mM−1 cm−2 and a detection limit of 0.1 µM (S/N = 3). The biosensor retains 72.52% performance of its incipient state after 30 days of storage. The samples of D2HG-containing fetal bovine serum and artificial urine were analyzed with the recovery of 99.56% to 106.83% and 97.30% to 102.47% further indicating the great application potential of our portable D2HG biosensor. [ABSTRACT FROM AUTHOR]

This paper illustrates a simple yet effective spectroscopic technique for the prediction of soil organic matter (SOM) from moist soil through the synchronous 2D correlation spectroscopy (2D-COS) analysis. In the moist soil system, the strong overlap between the water absorption peaks and the SOM characteristic features in the visible-near infrared (Vis-NIR) spectral region have long been recognised as one of the main factors that causes significant errors in the prediction of the SOM content. The aim of the paper is to illustrate how the tangling effects due to the moisture and the SOM can be unveiled under 2D-COS through a sequential correlogram analysis of the two perturbation variables (i.e., the moisture and the SOM) independently. The main outcome from the 2D-COS analysis is the discovery of SOM-related bands at the 597 nm, 1646 nm and 2138 nm, together with the predominant water absorbance feature at the 1934 nm and the relatively less important ones at 1447 nm and 2210 nm. This information is then utilised to build partial least square regression (PLSR) models for the prediction of the SOM content. The experiment has shown that by discarding noisy bands adjacent to the SOM features, and the removal of the water absorption bands, the determination coefficient of prediction (Rp2) and the ratio of prediction to deviation (RPD) for the prediction of SOM from moist soil have achieved Rp2 = 0.92 and the RPD = 3.19, both of which are about 5% better than that of using all bands for building the PLSR model. The very high RPD (=3.19) obtained in this study may suggest that the 2D-COS technique is effective for the analysis of complex system like the prediction of SOM from moist soil. [ABSTRACT FROM AUTHOR]

The aim of this paper is to construct two new classes of multivariate risk statistics, and to study their properties. We, first, introduce the multivariate shortfall risk statistics and multivariate divergence risk statistics. Then, their basic properties are studied, and their representation results are provided. Furthermore, their coherency is also characterized by means of the corresponding loss function. Finally, entropic risk statistics are given to illustrate the proposed new classes of multivariate risk statistics. The relationship between multivariate shortfall and divergence risk statistics is also discussed. [ABSTRACT FROM AUTHOR]