Search Results (26)

Salinity is a major limiting factor for all food crops, mainly Chinese cabbage. This study aimed to investigate the effects of biochar (BC) on physiological, biochemical, and molecular responses of Chinese cabbage grown under salinity stress in an open field. We supplied three concentrations of BC (5, 10, and 15 t/ha) to the 200 mM NaCl salinity-stress-induced field, which enhanced physical and chemical properties of the soil. Under salinity stress, BC increased photosynthetic pigments and reduced proline and H₂O₂ contents. Notably, 5 t/ha BC boosted plant growth, biomass, and yield by >40% and inhibited ROS accumulation under salinity stress. BC also promoted the concentrations of various key micronutrients, particularly Fe and Zn, in Chinese cabbage under salinity stress, which may contribute to improving the nutrient content. BC under salinity stress significantly induced the expression of NHX family genes (BoNHX1 and BoNHX2). Among these, the BoNHX1 gene was found to be highly expressed in shoot and root tissues of Chinese cabbage grown under salinity stress with BC. Identification of this key candidate gene will lay the groundwork for further functional characterization studies to elucidate its role under salinity stress with BC. This study comprehensively analyzes the physiological, biochemical, and molecular impacts of BC application in Chinese cabbage under salinity stress. This study found that the application of 5 t/ha significantly improved various physiological and biochemical traits of Chinese cabbage under salinity stress compared to the other treatments. The outcome of this study provides novel insights into the bioprotective role of BC, offering a valuable foundation of organic supplements for farmers while also highlighting potential research directions for enhancing crop resilience and productivity in economically important crops. Full article

This study proposes a lightweight adaptive neural network framework based on an improved YOLOv11n model to address the core challenges in identifying cabbage seedlings in visible light images captured by UAVs. These challenges include the loss of small-target features, poor adaptability to complex lighting conditions, and the low deployment efficiency of edge devices. First, the adaptive dual-path downsampling module (ADown) integrates average pooling and maximum pooling into a dual-branch structure to enhance background texture and crop edge features in a synergistic manner. Secondly, the Illumination Robust Contrast Learning Head (IRCLHead) utilizes a temperature-adaptive network to adjust the contrast loss function parameters dynamically. Combined with a dual-output supervision mechanism that integrates growth stage prediction and interference-resistant feature embedding, this module enhances the model’s robustness in complex lighting scenarios. Finally, a lightweight spatial-channel attention convolution module (LAConv) has been developed to optimize the model’s computational load by using multi-scale feature extraction paths and depth decomposition structures. Experiments demonstrate that the proposed architecture achieves an mAP@0.5 of 99.0% in detecting cabbage seedling growth cycles, improving upon the baseline model by 0.71 percentage points. Furthermore, it achieves an mAP@0.5:0.95 of 2.4 percentage points, reduces computational complexity (GFLOPs) by 12.7%, and drastically reduces inference time from 3.7 ms to 1.0 ms. Additionally, the model parameters are simplified by 3%. This model provides an efficient solution for the real-time counting of cabbage seedlings and lightweight operations in drone-based precision agriculture. Full article

The striped flea beetle (SFB), Phyllotreta striolata, is a major pest of Brassicaceae crops, causing substantial yield losses worldwide. Effective biocontrol strategies, particularly the development of mycoinsecticides, require the identification of virulent entomopathogenic fungi (EPF) and the establishment of reliable bioassay systems. However, establishing reliable bioassay systems for SFB has been particularly challenging, especially for larval stages due to their recalcitrant rearing requirements. This study aimed to establish a standardized bioassay protocol to evaluate EPF efficacy against SFB. A specialized larval collection apparatus was developed, and the virulence of three EPF strains (Beauveria bassiana BbPs01, Metarhizium robertii MrCb01, and Cordyceps javanica IjH6102) was assessed against both adult and larval stages using a radish slice-based rearing system. Intriguingly, BbPs01 and MrCb01 exhibited significantly higher LT₅₀ values in larvae than in adults, contrary to the typical pattern of greater larval susceptibility observed in most insect systems. We hypothesized that isothiocyanate—specifically sulforaphane, a compound abundant in radish tissues—exerts fungistatic effects that impair fungal growth and virulence. Follow-up experiments confirmed that radish-derived sulforaphane inhibited fungal activity. Through alternative host plant screening, Chinese flowering cabbage (Brassica campestris L. ssp. chinensis var. utilis) was identified as an optimal larval diet that minimally interferes with EPF bioactivity, enabling reliable virulence assessments. This study presents critical methodological advancements for SFB biocontrol research, providing a robust framework for standardized larval bioassay and novel insights into plant secondary metabolite interactions with entomopathogens. The optimized system supports the development of targeted mycoinsecticides and contributes to a deeper understanding of tri-trophic interactions in crucifer pest management. Full article

Increasing the stomatal aperture is a crucial strategy for enhancing the rate of CO₂ absorption, which ultimately contributes to increased plant yield through improved photosynthetic activity. The successful implementation of this strategy depends on the rapid identification of positive regulatory environmental stimuli that promote stomatal opening. However, current research on stomatal opening regulation has predominantly focused on Arabidopsis and other crops, with comparatively less attention given to leafy vegetables. In this study, Chinese cabbage was selected as the experimental material. A suitable method for isolating stomata from Chinese cabbage was developed by comparing the advantages and disadvantages of several commonly used stomatal isolation techniques. Subsequently, an effective method for observing stomatal aperture was established through an investigation of the time and concentration dependence on potassium-containing solutions. Utilizing this observation method, the stomatal aperture response to twelve environmental stimuli was examined to facilitate the rapid screening of a formula to enhance stomatal opening. The stomatal aperture observation protocol involved incubating the abaxial epidermis, obtained via the epidermal peeling method, in an opening solution containing 0.5% KCl (pH 6.0) under light for 5 h. The results indicated that stomatal opening is concentration dependent on external environmental stimuli. The exogenous application of 100 µM Ca²⁺ (33.5%), 50 µM brassinosteroid (43.5%), and 10 µM cytokinin (43.4%) resulted in an increase in stomatal aperture of over 30%. This research provides a foundation for manipulating the stomatal opening of Chinese cabbage to enhance production. Full article

Flowers are one of the most important organs in plants. Their development serves as a key indicator of the transition from vegetative to reproductive growth and is regulated by various internal signals and environmental factors. NAC (NAM, ATAF, CUC) transcription factors (TFs) play a crucial regulatory role in floral organ development; however, research on the analysis and identification of the NAC TF family in Chinese cabbage (Brassica rapa L.) remains limited. In this study, we performed a comprehensive genome-wide analysis of NACs in B. rapa and identified 279 members of the BrNAC gene family. Their physicochemical properties, domain structure, collinearity relation, and cis-regulatory elements were evaluated. Phylogenetic analysis indicates that NAC proteins from Arabidopsis, B. rapa, B. oleracea, and B. nigra can be classified into seven distinct clades. BrNACs exhibit a tissue-specific expression, and nine BrNACs being specifically expressed in the inflorescence. Furthermore, nine flower-related BrNACs were selected for RT-qPCR analysis to validate their expression profiles. BrNAC2s has been cloned to investigate their subcellular localization, and examine the expression patterns of their promoters in Arabidopsis inflorescences. BrNAC2a and BrNAC2c are highly expressed in stamens while BrNAC2b exhibits elevated expression in pistils and pedicel. Collectively, our findings enhance the understanding of the BrNAC family and provide a foundation for future studies on the molecular mechanisms of BrNACs in floral development. Full article

The challenges posed by climate change have had a crucial impact on global food security, with crop yields negatively affected by abiotic and biotic stresses. Consequently, the identification of abiotic stress-responsive genes (SRGs) in crops is essential for augmenting their resilience. This study presents a computational model utilizing machine learning techniques to predict genes in Chinese cabbage that respond to four abiotic stresses: cold, heat, drought, and salt. To construct this model, data from relevant studies regarding responses to these abiotic stresses were compiled, and the protein sequences encoded by abiotic SRGs were converted into numerical representations for subsequent analysis. For the selected feature set, six distinct machine learning binary classification algorithms were employed. The results demonstrate that the constructed models can effectively predict SRGs associated with the four types of abiotic stresses, with the area under the receiver operating characteristic curve (auROC) for the models being 81.42%, 87.92%, 80.85%, and 88.87%, respectively. For each type of stress, a distinct number of stress-resistant genes was predicted, and the ten genes with the highest scores were selected for further analysis. To facilitate the implementation of the proposed strategy by users, an online prediction server, has been developed. This study provides new insights into computational approaches to the identification of abiotic SRGs in Chinese cabbage as well as in other plants. Full article

Addressing the challenges of complex structure, limited modularization capability, and insufficient responsiveness in traditional hydraulically driven inter-plant mechanical weeding equipment, this study designed and developed an electric swing-type opening and closing intra-row weeding control system. The system integrates deep learning technology for accurate identification and localization of cabbage, enabling precise control and dynamic obstacle avoidance for the weeding knives. The system’s performance was comprehensively evaluated through laboratory and field experiments. Laboratory experiments demonstrated that, under conditions of low speed and large plant spacing, the system achieved a weeding accuracy of 96.67%, with a minimum crop injury rate of 0.83%. However, as the operational speed increased, the weeding accuracy decreased while the crop injury rate increased. Two-way ANOVA results indicated that operational speed significantly affected both weeding accuracy and crop injury rate, whereas plant spacing had a significant effect on weeding accuracy but no significant effect on crop injury rate. Field experiment results further confirmed that the system maintained high weeding accuracy and crop protection under varying speed conditions. At a low speed of 0.1 m/s, the weeding accuracy was 96.00%, with a crop injury rate of 1.57%. However, as the speed increased to 0.5 m/s, the weeding accuracy dropped to 81.79%, while the crop injury rate rose to 5.49%. These experimental results verified the system’s adaptability and reliability in complex field environments, providing technical support for the adoption of intelligent mechanical weeding systems. Future research will focus on optimizing control algorithms and feedback mechanisms to enhance the system’s dynamic response capability and adaptability, thereby advancing the development of sustainable agriculture and precision field management. Full article

In the realm of global food security, plants serve as the primary source of sustenance. However, plant diseases pose a significant threat to this security. The process for diagnosing these diseases forms the bedrock of disease control efforts. The precision and expediency of these diagnoses wield substantial influence over disease management and the consequent reduction of economic losses. This research endeavors to diagnose the prevalent crops in Jordan, as identified by the Jordanian Department of Statistics for the year 2019. These crops encompass four key agricultural varieties: cucumbers, tomatoes, lettuce, and cabbage. To facilitate this, a novel dataset known as “Jordan22” was meticulously curated. Jordan22 was compiled by collecting images of diseased and healthy plants captured on Jordanian farms. These images underwent meticulous classification by a panel of three agricultural specialists well-versed in plant disease identification and prevention. The Jordan22 dataset comprises a substantial size, amounting to 3210 images. The results yielded by the CNN were remarkable, with a test accuracy rate reaching an impressive 0.9712. Optimal performance was observed when images were resized to 256 × 256 dimensions, and max pooling was used instead of average pooling. Furthermore, the initial convolutional layer was set at a size of 32, with subsequent convolutional layers standardized at 128 in size. In conclusion, this research represents a pivotal step towards enhancing plant disease diagnosis and, by extension, global food security. Through the creation of the Jordan22 dataset and the meticulous training of a CNN model, we have achieved substantial accuracy in disease detection, paving the way for more effective disease management strategies in agriculture. Full article

Studies on the phenotypic traits and their associations in Chinese cabbage lack precise and objective digital evaluation metrics. Traditional assessment methods often rely on subjective evaluations and experience, compromising accuracy and reliability. This study develops an innovative, comprehensive trait evaluation method based on 3D point cloud technology, with the aim of enhancing the precision, reliability, and standardization of the comprehensive phenotypic traits of Chinese cabbage. By using multi-view image sequences and structure-from-motion algorithms, 3D point clouds of 50 plants from each of the 17 Chinese cabbage varieties were reconstructed. Color-based region growing and 3D convex hull techniques were employed to measure 30 agronomic traits. Comparisons between 3D point cloud-based measurements of the plant spread, plant height, leaf area, and leaf ball volume and traditional methods yielded R² values greater than 0.97, with root mean square errors of 1.27 cm, 1.16 cm, 839.77 cm³, and 59.15 cm², respectively. Based on the plant spread and plant height, a linear regression prediction of Chinese cabbage weights was conducted, yielding an R² value of 0.76. Integrated optimization algorithms were used to test the parameters, reducing the measurement time from 55 min when using traditional methods to 3.2 min. Furthermore, in-depth analyses including variation, correlation, principal component analysis, and clustering analyses were conducted. Variation analysis revealed significant trait variability, with correlation analysis indicating 21 pairs of traits with highly significant positive correlations and 2 pairs with highly significant negative correlations. The top six principal components accounted for 90% of the total variance. Using the elbow method, k-means clustering determined that the optimal number of clusters was four, thus classifying the 17 cabbage varieties into four distinct groups. This study provides new theoretical and methodological insights for exploring phenotypic trait associations in Chinese cabbage and facilitates the breeding and identification of high-quality varieties. Compared with traditional methods, this system provides significant advantages in terms of accuracy, speed, and comprehensiveness, with its low cost and ease of use making it an ideal replacement for manual methods, being particularly suited for large-scale monitoring and high-throughput phenotyping. Full article

The exploration of the impact of different spatial scales on the low-altitude remote sensing identification of Chinese cabbage (Brassica rapa subsp. Pekinensis) plants offers important theoretical reference value in balancing the accuracy of plant identification with work efficiency. This study focuses on Chinese cabbage plants during the rosette stage; RGB images were obtained by drones at different flight heights (20 m, 30 m, 40 m, 50 m, 60 m, and 70 m). Spectral sampling analysis was conducted on different ground backgrounds to assess their separability. Based on the four commonly used vegetation indices for crop recognition, the Excess Green Index (ExG), Red Green Ratio Index (RGRI), Green Leaf Index (GLI), and Excess Green Minus Excess Red Index (ExG-ExR), the optimal index was selected for extraction. Image processing methods such as frequency domain filtering, threshold segmentation, and morphological filtering were used to reduce the impact of weed and mulch noise on recognition accuracy. The recognition results were vectorized and combined with field data for the statistical verification of accuracy. The research results show that (1) the ExG can effectively distinguish between soil, mulch, and Chinese cabbage plants; (2) images of different spatial resolutions differ in the optimal type of frequency domain filtering and convolution kernel size, and the threshold segmentation effect also varies; (3) as the spatial resolution of the imagery decreases, the optimal window size for morphological filtering also decreases, accordingly; and (4) at a flight height of 30 m to 50 m, the recognition effect is the best, achieving a balance between recognition accuracy and coverage efficiency. The method proposed in this paper is beneficial for agricultural growers and managers in carrying out precision planting management and planting structure optimization analysis and can aid in the timely adjustment of planting density or layout to improve land use efficiency and optimize resource utilization. Full article

Cabbage (Brassica oleracea), a crop of major economic importance worldwide, is affected by numerous diseases, which are caused by a wide range of microorganisms, including fungi, oomycetes, bacteria, and viruses, which lead to important losses in yield and quality. The increasing availability of reference genomes of plant-associated microbes together with recent advances in metagenomic approaches provide new opportunities to identify microbes linked to distinct symptomatology in Brassica leaves. In this study, shotgun metagenomics was used to investigate the microbial community in leaves of B. oleracea plants from agricultural farmlands. Compared with conventional techniques based on culture-based methods, whole-genome shotgun sequencing allows the reliable identification of the microbial population inhabiting a plant tissue at the species level. Asymptomatic and symptomatic leaves showing different disease symptoms were examined. In the asymptomatic leaves, Xanthomonas species were the most abundant taxa. The relative abundance of bacterial and fungal communities varied depending on disease symptoms on the leaf. The microbiome of the leaves showing mild to severe levels of disease was enriched in bacterial populations (Sphingomonas, Methylobacterium, Paracoccus) and to a lesser degree in some fungal taxa, such as Alternaria and Colletotrichum (e.g., in leaves with high levels of necrotic lesions). Sclerotinia species were highly abundant in severely damaged leaves (S. sclerotium, S. trifolium, S. bolearis), followed by Botrytis species. The common and specific bacterial and fungal species associated to disease symptoms were identified. Finally, the analysis of the gene functions in the metagenomic data revealed enrichment in carbohydrate-active enzymes potentially involved in pathogenicity, whose distribution also varied among disease severity groups. Understanding the B. oleracea leaf microbiome in agricultural ecosystems will pave the way for the efficient management of diseases in this crop. Full article

Precise navigation in agricultural applications necessitates accurate guidance from the seedling belt, which the Global Positioning System (GPS) alone cannot provide. The overlapping leaves of Chinese cabbage (Brassica pekinensis Rupr.) present significant challenges for seedling belt fitting due to difficulties in plant identification. This study aims to address these challenges by improving the You Only Look Once (YOLO) v7 model with a novel approach that decouples its network head deriving from the Faster-Regions with Convolutional Neural Network (Faster R-CNN) architecture. Additionally, this study introduced a BiFormer attention mechanism to accurately identify the centers of overlapping Chinese cabbages. Using these identified centers and pixel distance verification, this study achieved precise fitting of the Chinese cabbage seedling belt (CCSB). Our experimental results demonstrated a significant improvement in performance metrics, with our improved model achieving a 2.5% increase in mean average precision compared to the original YOLO v7. Furthermore, our approach attained a 94.2% accuracy in CCSB fitting and a 91.3% Chinese cabbage identification rate. Compared to traditional methods such as the Hough transform and linear regression, our method showed an 18.6% increase in the CCSB identification rate and a 17.6% improvement in angle accuracy. The novelty of this study lies in the innovative combination of the YOLO v7 model with a decoupled head and the BiFormer attention mechanism, which together advance the identification and fitting of overlapping leafy vegetables. This advancement supports intelligent weeding, reduces the reliance on chemical herbicides, and promotes safer, more sustainable agricultural practices. Our research not only improves the accuracy of overlapping vegetable identification, but also provides a robust framework for enhancing precision agriculture. Full article

Field crops are usually planted in rows, and accurate identification and extraction of crop row centerline is the key to realize autonomous navigation and safe operation of agricultural machinery. However, the diversity of crop species and morphology, as well as field noise such as weeds and light, often lead to poor crop detection in complex farming environments. In addition, the curvature of crop rows also poses a challenge to the safety of farm machinery during travel. In this study, a combined multi-crop row centerline extraction algorithm is proposed based on improved YOLOv8 (You Only Look Once-v8) model, threshold DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering, least squares method, and B-spline curves. For the detection of multiple crops, a DCGA-YOLOv8 model is developed by introducing deformable convolution and global attention mechanism (GAM) on the original YOLOv8 model. The introduction of deformable convolution can obtain more fine-grained spatial information and adapt to crops of different sizes and shapes, while the combination of GAM can pay more attention to the important feature areas of crops. The experimental results shown that the F1-score and mAP value of the DCGA-YOLOv8 model for Cabbage, Kohlrabi, and Rice are 96.4%, 97.1%, 95.9% and 98.9%, 99.2%, 99.1%, respectively, which has good generalization and robustness. A threshold-DBSCAN algorithm was proposed to implement clustering for each row of crops. The correct clustering rate for Cabbage, Kohlrabi and Rice reaches 98.9%, 97.9%, and 100%, respectively. And LSM and cubic B-spline curve methods were applied to fit straight and curved crop rows, respectively. In addition, this study constructed a risk optimization function for the wheel model to further improve the safety of agricultural machines operating between crop rows. This indicates that the proposed method can effectively realize the accurate recognition and extraction of navigation lines of different crops in complex farmland environment, and improve the safety and stability of visual navigation and field operation of agricultural machines. Full article

Non-heading Chinese cabbage prefers cool temperatures, and heat stress has become a major factor for reduced yield. The proline transporter protein (ProT) is highly selective for proline transport, contributing to the heat tolerance of non-heading Chinese cabbage. However, there has been no systematic study on the identification and potential functions of the ProT gene family in response to heat stress in non-heading Chinese cabbage. We identified six BchProT genes containing 11–12 transmembrane helices characteristic of membrane proteins through whole-genome sequencing. These genes diverged into three evolutionary branches and exhibited similarity in motifs and intron/exon numbers. Segmental duplication is the primary driving force for the amplification of BchProT. Notably, many stress-related elements have been identified in the promoters of BchProT using cis-acting element analysis. The expression level of BchProT6 was the highest in petioles, and the expression level of BchProT1 was the highest under high-temperature stress. Subcellular localization indicated their function at cell membranes. Heterologous expression of BchProT1 in Arabidopsis plants increased proline transport synthesis under heat-stress conditions. This study provides valuable information for exploring the molecular mechanisms underlying heat tolerance mediated by members of the BchProT family. Full article

The cultivation of kimchi cabbage in South Korea has always faced significant challenges due to the looming presence of Alternaria leaf spot (ALS), which is a fungal disease mainly caused by Alternaria alternata. The emergence of black spots resulting from Alternaria infection lowers the quality of the plant, rendering it inedible and unmarketable. The timely identification of this disease is crucial, as it provides essential data enabling swift intervention, thereby localizing the infection throughout the field. Hyperspectral imaging technologies excel in detecting subtle shifts in reflectance values induced by chemical differences within leaf tissues. However, research on the spectral correlation between Alternaria and kimchi cabbage remains relatively scarce. Therefore, this study aims to identify the spectral signature of Alternaria infection on kimchi cabbage and develop an automatic classifier for detecting Alternaria disease symptoms. Alternaria alternata was inoculated on various sizes of kimchi cabbage leaves and observed daily using a hyperspectral imaging system. Datasets were created based on captured hyperspectral images to train four classifier models, including support vector machine (SVM), random forest (RF), one-dimensional convolutional neural network (1D-CNN), and one-dimensional residual network (1D-ResNet). The results suggest that 1D-ResNet outperforms the other models with an overall accuracy of 0.91, whereas SVM, RF, and 1D-CNN achieved 0.80, 0.88, and 0.86, respectively. This study may lay the foundation for future research on high-throughput disease detection, frequently incorporating drones and aerial imagery. Full article