Search Results (133)

As a globally significant economic crop, pepper (Capsicum annuum L.) plants display excessive plant height (etiolation) in greenhouse production under an undesirable environment, leading to lodging-prone plants with reduced stress resistance. In the present study, we provided supplementary ultraviolet-B (UV-B, 280–315 nm) light to pepper plants grown in a greenhouse to assess the influences of UV-B on pepper growth, with an emphasis on the molecular mechanisms mediated through the gibberellin (GA) signaling pathway. The results indicated that UV-B significantly decreased the plant height and the fresh weight of pepper plants. However, no significant differences were observed in the chlorophyll content of pepper plants grown under natural light and supplementary UV-B radiation. The results of the transcriptomic and metabolomic analyses indicated that differentially expressed genes (DEGs) were significantly enriched in plant hormone signal transduction and that UV radiation altered the gibberellin synthesis pathway of pepper plants. Specifically, the GA3 content of the pepper plants grown with UV-B radiation decreased by 39.1% compared with those grown without supplementary UV-B radiation; however, the opposite trend was observed in GA34, GA7, and GA51 contents. In conclusion, UV-B exposure significantly reduced plant height, a phenotypic response mechanistically linked to an alteration in GA homeostasis, which may be caused by a decrease in GA3 content. Our study elucidated the interplay between UV-B and gibberellin biosynthesis in pepper morphogenesis, offering a theoretical rationale for developing UV-B photoregulation technologies as alternatives to chemical growth inhibitors. Full article

Accurate estimation of the leaf area index (LAI), a key indicator of canopy development and light interception, is essential for improving productivity in greenhouse tomato cultivation. This study presents a non-destructive LAI estimation method using side-view images captured by a vertical scanning system. The system recorded the full vertical profile of tomato plants grown under two deleafing strategies: modifying leaf height (LH) and altering leaf density (LD). Vegetative and leaf areas were extracted using color-based masking and semantic segmentation with the Segment Anything Model (SAM), a general-purpose deep learning tool. Regression models based on leaf or all vegetative pixel counts showed strong correlations with destructively measured LAI, particularly under LH conditions (R² > 0.85; mean absolute percentage error ≈ 16%). Under LD conditions, accuracy was slightly lower due to occlusion and leaf orientation. Compared with prior 3D-based methods, the proposed 2D approach achieved comparable accuracy while maintaining low cost and a labor-efficient design. However, the system has not been tested in real production, and its generalizability across cultivars, environments, and growth stages remains unverified. This proof-of-concept study highlights the potential of side-view imaging for LAI monitoring and calls for further validation and integration of leaf count estimation. Full article

The increasing concentration of greenhouse gases is amplifying the global risk of drought on crop productivity. This study sought to investigate the effects of drought on the growth of maize (Zea mays L.) seedlings. A total of 78 maize hybrids were employed in this study to replicate drought conditions through the potting method. The maize seedlings were subjected to a 10-day period of water breakage following a standard watering cycle until they reached the third leaf collar (V3) stage. Parameters including plant height, stem diameter, chlorophyll content, and root number were assessed. The eight phenotypic traits include the fresh and dry weights of both the aboveground and underground parts. Three machine learning methods—random forest (RF), K-nearest neighbor (KNN), and extreme gradient boosting (XGBoost)—were employed to systematically analyze the relevant traits of maize seedlings’ drought tolerance and to assess their predictive performance in this regard. The findings indicated that plant height, aboveground weight, and chlorophyll content constituted the primary indices for phenotyping maize seedlings under drought conditions. The XGBoost model demonstrated optimal performance in the classification (AUC = 0.993) and regression (R² = 0.863) tasks, establishing itself as the most effective prediction model. This study provides a foundation for the feasibility and reliability of screening drought-tolerant maize varieties and refining precision breeding strategies. Full article

Dense planting and intercropping are the main ways to improve soybean production. However, both confront inter- and intra-crop shading stress. This leads to stem elongation, resulting in lodging and yield losses. Most previous studies have focused on the later growth stages for shade tolerance. However, it has been found that the seedling stage is crucial, and understanding the genetic basis of shade tolerance at this stage is pivotal for yield improvement. In this study, 310 soybean accessions were used to evaluate shade tolerance under greenhouse conditions. Plant height (PH), main stem length (MSL), and hypocotyl length (HL) were examined at seedling stage, and their treatment/control ratios (PH_r, MSL_r, HL_r) were used for genetic dissection of shade tolerance. Their overall phenotypic variation and heritability (H²) ranged 22.97–36.85% and 31.66–83.81%, respectively. RTM-GWAS identified 12, 10, and 6 QTLs associated with PH_r, MSL_r, and HL_r, respectively. Among these, Block_17_11907536_11926235 was associated with both PH_r and MSL_r, and Block_1_55630414_55715065 associated with the HL_r trait showed the highest contribution (R² = 10.38%). Additionally, seven promising candidate genes, primarily linked to ethylene-responsive transcription factors, were proposed, supported by their established roles in plant development and stress responses, as evidenced in prior studies. The germplasm, QTLs, and candidate genes identified in this study improve our understanding of shade tolerance and have the potential to accelerate the breeding of shade-resilient soybeans. Full article

High-throughput image analysis is a key tool for the efficient assessment of quantitative plant phenotypes. A typical approach to the computation of quantitative plant traits from image data consists of two major steps including (i) image segmentation followed by (ii) calculation of quantitative traits of segmented plant structures. Despite substantial advancements in deep learning-based segmentation techniques, minor artifacts of image segmentation cannot be completely avoided. For several commonly used traits including plant width, height, convex hull, etc., even small inaccuracies in image segmentation can lead to large errors. Ad hoc approaches to cleaning ’small noisy structures’ are, in general, data-dependent and may lead to substantial loss of relevant small plant structures and, consequently, falsified phenotypic traits. Here, we present a straightforward end-to-end approach to direct computation of phenotypic traits from image data using a deep learning regression model. Our experimental results show that image-to-trait regression models outperform a conventional segmentation-based approach for a number of commonly sought plant traits of plant morphology and health including shoot area, linear dimensions and color fingerprints. Since segmentation is missing in predictions of regression models, visualization of activation layer maps can still be used as a blueprint to model explainability. Although end-to-end models have a number of limitations compared to more complex network architectures, they can still be of interest for multiple phenotyping scenarios with fixed optical setups (such as high-throughput greenhouse screenings), where the accuracy of routine trait predictions and not necessarily the generalizability is the primary goal. Full article

Bacillus thuringiensis (Bt) is widely recognized as the most important microbial pesticide controlling various insect pests and diseases due to its insecticidal crystal proteins (ICPs) and antimicrobial metabolites. The current study investigates the biocontrol potential of B. thuringiensis GBAC46 against the fungal pathogen Fusarium verticillioides and the insect pest Spodoptera frugiperda through multiple mechanisms. Phenotypic experiments revealed that GBAC46 effectively inhibited F. verticillioides growth by inducing reactive oxygen species (ROS) accumulation and showed enhanced larvicidal activity against second instar S. frugiperda larvae. Pot experiments showed that feeding by S. frugiperda enhanced F. verticillioides infection in maize. The Bt strain GBAC46 effectively controlled both pests and diseases in greenhouse maize seedlings. Applying the Bt strain GBAC46 reduced feeding damage from S. frugiperda, decreased leaf yellowing and wilting caused by F. verticillioides, and improved growth indicators such as plant height, fresh weight, and dry weight. RT-qPCR results revealed that the Bt strain GBAC46 induced key defense genes in maize involved in activating salicylic acid, jasmonic acid, and ethylene pathways. The overall study demonstrated and confirmed the GBAC46 strain as a promising microbial agent for disease and pest management. Full article

Plant phenotyping is crucial for advancing precision agriculture and modern breeding, with 3D point cloud segmentation of plant organs being essential for phenotypic parameter extraction. Nevertheless, although existing approaches maintain segmentation precision, they struggle to efficiently process complex geometric configurations and large-scale point cloud datasets, significantly increasing computational costs. Furthermore, their heavy reliance on high-quality annotated data restricts their use in high-throughput settings. To address these limitations, we propose a novel multi-stage region-growing algorithm based on an octree structure for efficient stem-leaf segmentation in maize point cloud data. The method first extracts key geometric features through octree voxelization, significantly improving segmentation efficiency. In the region-growing phase, a preliminary structural segmentation strategy using fitted cylinder parameters is applied. A refinement strategy is then applied to improve segmentation accuracy in complex regions. Finally, stem segmentation consistency is enhanced through central axis fitting and distance-based filtering. In this study, we utilize the Pheno4D dataset, which comprises three-dimensional point cloud data of maize plants at different growth stages, collected from greenhouse environments. Experimental results show that the proposed algorithm achieves an average precision of 98.15% and an IoU of 84.81% on the Pheno4D dataset, demonstrating strong robustness across various growth stages. Segmentation time per instance is reduced to 4.8 s, offering over a fourfold improvement compared to PointNet while maintaining high accuracy and efficiency. Additionally, validation experiments on tomato point cloud data confirm the proposed method’s strong generalization capability. In this paper, we present an algorithm that addresses the shortcomings of traditional methods in complex agricultural environments. Specifically, our approach improves efficiency and accuracy while reducing dependency on high-quality annotated data. This solution not only delivers high precision and faster computational performance but also lays a strong technical foundation for high-throughput crop management and precision breeding. Full article

Wheat plants encounter both biotic and abiotic pressure in their surroundings. Among the biotic stress factors, the Russian wheat aphid (RWA: Diuraphis noxia Kurdjumov) decreases grain yield and quality. The current RWA control strategies, including resistance breeding and the application of aphicides, are outpaced and potentially environmentally harmful. Alternatively, priming can stimulate defence responses to RWA infestation. This study investigated the priming potential of two priming agents, avirulent Puccinia triticina (Pt) isolates and salicylic acid (SA), against RWA infestation. The priming effect of Pt isolates and SA in reducing RWA-induced leaf damage and increased antioxidant activities is an indication of defence responses. Selected South African wheat cultivars and Lesotho landraces, grown under greenhouse conditions, were inoculated with Pt isolates (UVPt13: avirulent, UVPt26: virulent) and treated with SA at the seedling or booting stages. The leaf damage rating score was used for phenotyping. The antioxidant-mediated defence responses were evaluated in three selected cultivars for further priming investigation. Our results revealed that the priming agents significantly reduced the leaf damage in most cultivars at both growth stages, and UVPt13 and SA priming significantly (p ≤ 0.05) increased superoxide dismutase, peroxidase, and ascorbate peroxidase activities. However, catalase activity exhibited a more pronounced decline in plants treated with the UVPt13 isolate. The Pt isolate priming was more efficient than the SA application. However, it is crucial to investigate the potential of effectors from the avirulent Pt isolate to prime wheat plants for resistance against RWA infestation. This could contribute to developing strategies to enhance crop protection and relieve pest pressure in wheat production. Full article

Crop phenotype detection is a precise way to understand and predict the growth of horticultural seedlings in the smart agriculture era to increase the cost-effectiveness and energy efficiency of agricultural production. Crop phenotype detection requires the consideration of plant stature and agricultural devices, like robots and autonomous vehicles, in smart greenhouse ecosystems. However, collecting the imaging dataset is a challenge facing the deep learning detection of plant phenotype given the dynamic changes among leaves and the temporospatial limits of camara sampling. To address this issue, digital cousin is an improvement on digital twins that can be used to create virtual entities of plants through the creation of dynamic 3D structures and plant attributes using RGB image datasets in a simulation environment, using the principles of the variations and interactions of plants in the physical world. Thus, this work presents a two-phase method to obtain the phenotype of horticultural seedling growth. In the first phase, 3D Gaussian splatting is selected to reconstruct and store the 3D model of the plant with 7000 and 30,000 training rounds, enabling the capture of RGB images and the detection of the phenotypes of the seedlings, overcoming temporal and spatial limitations. In the second phase, an improved YOLOv8 model is created to segment and measure the seedlings, and it is modified by adding the LADH, SPPELAN, and Focaler-ECIoU modules. Compared with the original YOLOv8, the precision of our model is 91%, and the loss metric is lower by approximately 0.24. Moreover, a case study of watermelon seedings is examined, and the results of the 3D reconstruction of the seedlings show that our model outperforms classical segmentation algorithms on the main metrics, achieving a 91.0% mAP50 (B) and a 91.3% mAP50 (M). Full article

Accurate collection of plant phenotyping is critical to optimising sustainable farming practices in precision agriculture. Traditional phenotyping in controlled laboratory environments, while valuable, falls short in understanding plant growth under real-world conditions. Emerging sensor and digital technologies offer a promising approach for direct phenotyping of plants in farm environments. This study investigates a learning-based phenotyping method using neural implicit surfaces reconstruction to achieve accurate in situ phenotyping of pepper plants in greenhouse environments. To quantitatively evaluate the performance of this method, traditional point cloud registration on 3D scanning data is implemented for comparison. Experimental result shows that NIR (neural implicit surfaces reconstruction) achieves competitive accuracy compared to the 3D scanning method. The mean distance error between the scanner-based method and the NeRF (neural radiance fields)-based method is 0.811 mm. This study shows that the learning-based NeRF method has similar accuracy to the 3D scanning-based method but with greater scalability and faster deployment capabilities. Full article

This work aimed to evaluate the relative gene expression of the candidate genes psI, psII, isr, utp, and prk involved in the defense response to Black Sigatoka in banana cultivars Calcutta-4, Krasan Saichon, Grand Nain, and Akondro Mainty, by a quantitative real-time PCR. Biotic stress was imposed on 6-month-old plants during five sampling intervals under greenhouse conditions. The psII and isr genes were upregulated for the Calcutta-4- and Krasan Saichon-resistant cultivars, and were validated in this study. For Grande Naine, a susceptible cultivar, there was an early downregulation of the psI, psII, and isr genes and a late upregulation of the psII gene. There was no significant expression of any of the genes for the susceptible cultivar Akondro Mainty. Computational biology tools such as ORFFinder and PlantCARE revealed that the utp gene has more introns and exons and that, in general, cis-elements involved in the response to biotic stress, such as as-1, w-box, and STRE, were detected in the promoter region of the genes studied. Data from this work also support the phenotyping studies of banana cultivars affected by Black Sigatoka in the field. Once validated in promising new hybrids, these genes may be used in marker-assisted selection (MAS) and/or gene-editing techniques. Full article

Automatic acquisition of phenotypic traits in tomato plants is important for tomato variety selection and scientific cultivation. Because of time-consuming and labor-intensive traditional manual measurements, the lack of complete structural information in two-dimensional (2D) images, and the complex structure of the plants, it is difficult to automatically obtain the phenotypic traits of the tomato canopy. Thus, a method for calculating the phenotypic traits of tomato canopy in greenhouse was proposed based on the extraction of the branch skeleton. First, a top-view-based acquisition platform was built to obtain the point cloud data of the tomato canopy, and the improved K-means algorithm was used to segment the three-dimensional (3D) point cloud of branches. Second, the Laplace algorithm was used to extract the canopy branch skeleton structure. Branch and leaf point cloud separation was performed using branch local skeleton vectors and internal features. In addition, the DBSCAN clustering algorithm was applied to recognize individual leaf organs. Finally, phenotypic traits including mean leaf inclination, digital biomass, and light penetration depth of tomato canopies were calculated separately based on the morphological structure of the 3D point cloud. The experimental results show that the detection accuracies of branches and leaves were above 88% and 93%, respectively, and the coefficients of determination between the calculated and measured values of mean leaf inclination, digital biomass, and light penetration depth were 0.9419, 0.9612, and 0.9093, respectively. The research results can provide an effective quantitative basis and technical support for variety selection and scientific cultivation of the tomato plant. Full article

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. Full article

Climate change has intensified droughts, severely impacting crops like oats and highlighting the need for effective adaptation strategies. In this context, the implementation of IoT-based climate control systems in greenhouses emerges as a promising solution for optimizing microclimates. These systems allow for the precise monitoring and adjustment of critical variables such as temperature, humidity, vapor pressure deficit (VPD), and photosynthetically active radiation (PAR), ensuring optimal conditions for crop growth. During the experiment, the average daytime temperature was 22.6 °C and the nighttime temperature was 15.7 °C. The average relative humidity was 60%, with a VPD of 0.46 kPa during the day and 1.26 kPa at night, while the PAR reached an average of 267 μmol m⁻² s⁻¹. Additionally, the use of high-throughput gravimetric phenotyping platforms enabled precise data collection on the plant–soil–atmosphere relationship, providing exhaustive control over water balance and irrigation. This facilitated the evaluation of the physiological response of plants to abiotic stress. Inoculation with microbial consortia (PGPB) was used as a tool to mitigate water stress. In this 69-day study, irrigation was suspended in specific treatments to simulate drought, and it was observed that inoculated plants maintained chlorophyll b and carotenoid levels akin to those of irrigated plants, indicating greater tolerance to water deficit. These plants also exhibited greater efficiency in dissipating light energy and rapid recovery after rehydration. The results underscore the potential of combining IoT monitoring technologies, advanced phenotyping platforms, and microbial consortia to enhance crop resilience to climate change. Full article

To develop aryloxyphenoxypropionate herbicides with novel structure and improved activity, a total of twenty-eight novel quinazolinone–phenoxypropionate derivatives containing a diester moiety were designed and synthesized. The herbicidal bioassay results in the greenhouse showed that QPEP-I-4 exhibited excellent herbicidal activity against E. crusgalli, D. sanguinalis, S. alterniflora, E. indica, and P. alopecuroides with inhibition rates >80% at a dosage of 150 g ha⁻¹ and displayed higher crop safety to G. hirsutum, G. max, and A. hypogaea than the commercial herbicide quizalofop-p-ethyl. Studying the herbicidal mechanism by phenotypic observation, membrane permeability evaluation, and transcriptomic analysis revealed that a growth inhibition of plants by QPPE-I-4 was the result from damage of the plants’ biomembrane. The evaluation of ACCase activity in vivo indicated that QPPE-I-4 could inhibit ACCase and may be a new type of ACCase inhibitor. The present work indicated that QPPE-I-4 could represent a lead compound for further developing novel AOPP herbicides. Full article