Qian Wu

Phenotypic traits, such as plant height, internode length, and node count, are essential indicators of the growth status of tomato plants, carrying significant implications for research on genetic breeding and cultivation management. Deep learning algorithms such as object detection and segmentation have been widely utilized to extract plant phenotypic parameters. However, segmentation-based methods are labor-intensive due to their requirement for extensive annotation during training, while object detection approaches exhibit limitations in capturing intricate structural features. To achieve real-time, efficient, and precise extraction of phenotypic traits of seedling tomatoes, a novel plant phenotyping approach based on 2D pose estimation was proposed. We enhanced a novel heatmap-free method, YOLOv8s-pose, by integrating the Convolutional Block Attention Module (CBAM) and Content-Aware ReAssembly of FEatures (CARAFE), to develop an improved YOLOv8s-pose (IYOLOv8s-pose) model, which efficiently focuses on salient image features with minimal parameter overhead while achieving a superior recognition performance in complex backgrounds. IYOLOv8s-pose manifested a considerable enhancement in detecting bending points and stem nodes. Particularly for internode detection, IYOLOv8s-pose attained a Precision of 99.8%, exhibiting a significant improvement over RTMPose-s, YOLOv5s6-pose, YOLOv7s-pose, and YOLOv8s-pose by 2.9%, 5.4%, 3.5%, and 5.4%, respectively. Regarding plant height estimation, IYOLOv8s-pose achieved an RMSE of 0.48 cm and an rRMSE of 2%, and manifested a 65.1%, 68.1%, 65.6%, and 51.1% reduction in the rRMSE compared to RTMPose-s, YOLOv5s6-pose, YOLOv7s-pose, and YOLOv8s-pose, respectively. When confronted with the more intricate extraction of internode length, IYOLOv8s-pose also exhibited a 15.5%, 23.9%, 27.2%, and 12.5% reduction in the rRMSE compared to RTMPose-s, YOLOv5s6-pose, YOLOv7s-pose, and YOLOv8s-pose. IYOLOv8s-pose achieves high precision while simultaneously enhancing efficiency and convenience, rendering it particularly well suited for extracting phenotypic parameters of tomato plants grown naturally within greenhouse environments. This innovative approach provides a new means for the rapid, intelligent, and real-time acquisition of plant phenotypic parameters in complex backgrounds.

With the advancement of computer vision technology, vision-based target perception has emerged as a predominant approach for harvesting robots to identify and locate fruits. However, little attention has been paid to the fact that fruits may be obscured by stems or other objects. In order to improve the vision detection ability of fruit harvesting robot, a fruit target selection and location approach considering obstacle perception was proposed. To enrich the dataset for tomato harvesting, synthetic data were generated by rendering a 3D simulated model of the tomato greenhouse environment, and automatically producing corresponding pixel-level semantic segmentation labels. An attention-based spatial-relationship feature extraction module (SFM) with lower computational complexity was designed to enhance the ability of semantic segmentation network DeepLab v3+ in accurately segmenting linear-structured obstructions such as stems and wires. An adaptive K-means clustering method was developed to distinguish individual instances of fruits. Furthermore, a barrier-free fruit selection algorithm that integrates information of obstacles and fruit instances was proposed to identify the closest and largest non-occluded fruit as the optimal picking target. The improved semantic segmentation network exhibited enhanced performance, achieving an accuracy of 96.75%. Notably, the Intersection-over-Union (IoU) of wire and stem classes was improved by 5.0% and 2.3%, respectively. Our target selection method demonstrated accurate identification of obstacle types (96.15%) and effectively excluding fruits obstructed by strongly resistant objects (86.67%). Compared to the fruit detection method without visual obstacle avoidance (Yolo v5), our approach exhibited an 18.9% increase in selection precision and a 1.3% reduction in location error. The improved semantic segmentation algorithm significantly increased the segmentation accuracy of linear-structured obstacles, and the obstacle perception algorithm effectively avoided occluded fruits. The proposed method demonstrated an appreciable ability in precisely selecting and locating barrier-free fruits within non-structural environments, especially avoiding fruits obscured by stems or wires. This approach provides a more reliable and practical solution for fruit selection and localization for harvesting robots, while also being applicable to other fruits and vegetables such as sweet peppers and kiwis.

Thidiazuron (TDZ) is widely used as a defoliant to induce leaf abscission in cotton. However, the underlying molecular mechanism is still unclear. In this study, RNA-seq and enzyme-linked immunosorbent assays (ELISA) were performed to reveal the dynamic transcriptome profiling and the change of endogenous phytohormones upon TDZ treatment in leaf, petiole, and abscission zone (AZ). We found that TDZ induced the gene expression of ethylene biosynthesis and signal, and promoted ethylene accumulation earlier in leaf than that in AZ. While TDZ down-regulated indole-3-acetic acid (IAA) biosynthesis genes mainly in leaf and IAA signal and transport genes. Furthermore, the IAA content reduced more sharply in the leaf than that in AZ to change the auxin gradient for abscission. TDZ suppressed CTK biosynthesis genes and induced CTK metabolic genes to reduce the IPA accumulation for the reduction of ethylene sensitivity. Furthermore, TDZ regulated the gene expression of abscisic acid (ABA) biosynthesis and signal and induced ABA accumulation between 12–48 h, which could up-regulate ABA response factor genes and inhibit IAA transporter genes. Our data suggest that TDZ orchestrates metabolism and signal of ethylene, auxin, and cytokinin, and also the transport of auxin in leaf, petiole, and AZ, to control leaf abscission.

Pressurized Mud Cap Drilling (PMCD) technique is typically applied for drilling formations with natural fractures and large vugs that result in severe or total losses. The density of the drilling fluid used in PMCD is slightly below reservoir pore pressure. In the case of very low reservoir pressures below base oil densities (~6.7 ppg), foam can be an option. This paper presents a methodology to develop an oil-based foam system for a PMCD application. The scope includes the descriptions of a foam PMCD application, functional requirements of foam, the development workflow, testing procedures, and modeling that are necessary to qualify a foam for PMCD application. The development methodology first involves identifying the constraints and well conditions of a given PMCD application; these include wellpath, hole and casing sections, reservoir pressure and temperature, surface pumping pressure limits (typically a rotating control device limit), foam stability requirements, etc. The above constraints drive the performance requirements for the foam. Next comes the design of the foam formulation and evaluating its performance against these requirements through various lab tests and modeling efforts, which can include ambient pressure half-life tests for initial screening, static and rotational stability tests at in-situ well conditions, rheology tests, solubility tests, pressure transmission estimates, gas migration estimates, and hydraulics modeling. Example results from the lab tests and modeling are shown to provide more insights into the development process. The proposed methodology may be used as a guide to design a foam drilling fluid for a PMCD application. The iterative nature of the development method is shown and is driven by the functional requirements that are coupled to each other. For example, a more viscous foam may be more stable but it may not be pumpable. Likewise, a less viscous foam may be pumpable but may not be sufficiently stable. Similarly, a highly compressible foam may not be good at pressure transmission to monitor downhole pressure variations as compared to a less compressible foam. In summary, the methodology described in this paper explains the development of an oil-based foam for a PMCD application that satisfies a set of operational constraints and functional requirements while highlighting the major factors that could impact foam performance. The application of foam to PMCD is a new concept and to our knowledge has not been applied in the field; in significantly depleted reservoirs this may become a viable option.

Background: Microglia initially undergoes chronic activation in response to the damages caused by stressful stimuli, such as bacterial infection and hypoxia. Inflammatory responses, as well as oxidative stress, perform crucial roles in the process of neuroinflammation, which results in brain damage. Glabridin is a natural organic compound with extensive beneficial properties such as anti-inflammatory and antioxidant properties. To the best of our knowledge, there are no studies conducted on the anti-neuroinflammatory activity of glabridin. Therefore, in this study, we aimed to investigate the potency of glabridin against the expression of lipopolysaccharide (LPS)-stimulated BV-2 cells. Materials and Methods: BV-2 cells were preincubated with glabridin followed by the LPS challenge. Subsequently, the cellular status of nitric oxide (NO), reactive oxygen species (ROS), prostaglandin E₂(PGE₂), and pro-inflammatory modulators (interleukin [IL]-1β and IL-6) were investigated and related signaling pathways were inspected via blotting assay. Results: Our results indicate that glabridin appreciably alleviated the LPS-induced accretion of inducible-NO synthase (iNOS), PGE₂, IL-1β, and IL-6. Moreover, it noticeably allayed the NO/iNOS, PGE₂/cyclooxygenase-2 protein statuses, and pro-inflammatory cytokine (tumor necrosis factor-α) on LPS-induced microglia. We also found that LPS severely increased the phosphorylation of Inhibitory kappa B kinases (IKKs), IκBα, p65, and nuclear factor (NF)-κB. Although glabridin supplementation suppressed the phosphorylation of the aforementioned molecules, LPS remarkable caused the nuclear interchange of NF-κBp65. Conclusion: Glabridin alleviates LPS-induced neuroinflammation in BV-2 cells by suppressing the accumulation of ROS and cell death and by inhibiting the pro-inflammatory responses via NF-κB-dependent mechanisms. According to our results, glabridin may be beneficial in neuroinflammation-related neurodegenerative disorders.

Background: Mepiquat chloride (MC), a plant growth regulator, enhances root growth by promoting lateral root formation in cotton. However, the underlying molecular mechanisms of this phenomenon is still unknown. Methods: In this study, we used 10 cotton (Gossypium hirsutum Linn.) cultivars to perform a seed treatment with MC to investigate lateral root formation, and selected a MC sensitive cotton cultivar for dynamic monitor of root growth and transcriptome analysis during lateral root development upon MC seed treatment. Results: The results showed that MC treated seeds promotes the lateral root formation in a dosage-depended manner and the effective promotion region is within 5 cm from the base of primary root. MC treated seeds induce endogenous auxin level by altering gene expression of both gibberellin (GA) biosynthesis and signaling and abscisic acid (ABA) signaling. Meanwhile, MC treated seeds differentially express genes involved in indole acetic acid (IAA) synthesis and transport. Furthermore, MC-induced IAA regulates the expression of genes related to cell cycle and division for lateral root development. Conclusions: Our data suggest that MC orchestrates GA and ABA metabolism and signaling, which further regulates auxin biosynthesis, transport, and signaling to promote the cell division responsible for lateral root formation.

In order to efficiently determine genotypic differences in rooting patterns of crops, novel hardware and software are needed simultaneously to characterize dynamics of root development. We describe a prototype robotic monitoring platform—the RhizoChamber-Monitor for analyzing growth patterns of plant roots automatically. The RhizoChamber-Monitor comprises an automatic imaging system for acquiring sequential images of roots which grow on a cloth substrate in custom rhizoboxes, an automatic irrigation system and a flexible shading arrangement. A customized image processing software was developed to analyze the spatio-temporal dynamics of root growth from time-course images of multiple plants. This software can quantify overall growth of roots and extract detailed growth traits (e.g. dynamics of length and diameter) of primary roots and of individual lateral roots automatically. It can also identify local growth traits of lateral roots (pseudo-mean-length and pseudo-maximum-length) semi-automatically. Two cotton genotypes were used to test both the physical platform and the analysis software. The combination of hardware and software is expected to facilitate quantification of root geometry and its spatio-temporal growth patterns, and therefore to provide opportunities for high-throughput root phenotyping in support of crop breeding to optimize root architecture.

Root distribution has a major influence on soil exploration and nutrient and water acquisition by plants. Soil coring is a well-known way to estimate root distribution. However, identifying an optimal core-sampling strategy is important if one is to strike the right balance between the high cost of making field estimates of root length density (RLD) vs. the need for accurate estimates. Virtual assessment of competing soil-coring strategies, based on three-dimensional (3-D) models of root system architecture (RSA), is a highly effective way to find that balance. The trajectories of the axile roots of two maize cultivars having contrasting axile root angles were measured in the field using in situ 3-D digitization. Lateral roots were also measured by recording topological and geometrical parameters. Based on the measurement dataset obtained, contrasting 3-D RSA models of individual maize plants were constructed in which the different lateral rooting angles were represented. Using these RSA models the accuracies of various core-sampling strategies for estimating RLD were assessed in a series of virtual experiments. Substantial biases occur if a one-core sampling strategy is used to estimate RLD. The biases largely remain for two-core sampling, although a weighting method can reduce these. However, given that identification of an optimal weighting method is difficult in practice, a new sampling strategy is proposed based on an area-weighting algorithm. In this way low deviations in RLD estimation can be achieved by sampling between rows and also by using larger-diameter (7.5 or 10 cm) cores. A 3-D root architecture model based on a detailed measurement dataset provides an ideal platform for assessing a range of soil-coring strategies. The improved two-core sampling strategy, based on an area-weighting algorithm, shows considerable promise as a cost-efficient way of obtaining good quality RLD estimates for maize.

Background and Aims Root diameter, especially apical diameter, plays an important role in root development and function. The variation in diameter between roots, and along roots, affects root structure and thus the root system’s overall foraging performance. However, the effect of diameter variation on root elongation, branching and topological connections has not been examined systematically in a population of high-order roots, nor along the roots, especially for mature plants grown in the field. Methods A method combining both excavation and analysis was applied to extract and quantify root architectural traits of adult, field-grown maize plants. The relationships between root diameter and other root architectural characteristics are analysed for two maize cultivars. Key Results The basal diameter of the lateral roots (orders 1–3) was highly variable. Basal diameter was partly determined by the diameter of the bearing segment. Basal diameter defined a potential root length, but the lengths of most roots fell far short of this. This was explained partly by differences in the pattern of diameter change along roots. Diameter tended to decrease along most roots, with the steepness of the gradient of decrease depending on basal diameter. The longest roots were those that maintained (or sometimes increased) their diameters during elongation. The branching density (cm^–1) of laterals was also determined by the diameter of the bearing segment. However, the location of this bearing segment along the mother root was also involved – intermediate positions were associated with higher densities of laterals. Conclusions The method used here allows us to obtain very detailed records of the geometry and topology of a complex root system. Basal diameter and the pattern of diameter change along a root were associated with its final length. These relationships are especially useful in simulations of root elongation and branching in source–sink models.