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23 pages, 26465 KiB  
Article
DHS-YOLO: Enhanced Detection of Slender Wheat Seedlings Under Dynamic Illumination Conditions
by Xuhua Dong and Jingbang Pan
Agriculture 2025, 15(5), 510; https://doi.org/10.3390/agriculture15050510 - 26 Feb 2025
Viewed by 816
Abstract
The precise identification of wheat seedlings in unmanned aerial vehicle (UAV) imagery is fundamental for implementing precision agricultural practices such as targeted pesticide application and irrigation management. This detection task presents significant technical challenges due to two inherent complexities: (1) environmental interference from [...] Read more.
The precise identification of wheat seedlings in unmanned aerial vehicle (UAV) imagery is fundamental for implementing precision agricultural practices such as targeted pesticide application and irrigation management. This detection task presents significant technical challenges due to two inherent complexities: (1) environmental interference from variable illumination conditions and (2) morphological characteristics of wheat seedlings characterized by slender leaf structures and flexible posture variations. To address these challenges, we propose DHS-YOLO, a novel deep learning framework optimized for robust wheat seedling detection under diverse illumination intensities. Our methodology builds upon the YOLOv11 architecture with three principal enhancements: First, the Dynamic Slender Convolution (DSC) module employs deformable convolutions to adaptively capture the elongated morphological features of wheat leaves. Second, the Histogram Transformer (HT) module integrates a dynamic-range spatial attention mechanism to mitigate illumination-induced image degradation. Third, we implement the ShapeIoU loss function that prioritizes geometric consistency between predicted and ground truth bounding boxes, particularly optimizing for slender plant structures. The experimental validation was conducted using a custom UAV-captured dataset containing wheat seedling images under varying illumination conditions. Compared to the existing models, the proposed model achieved the best performance with precision, recall, mAP50, and mAP50-95 values of 94.1%, 91.0%, 95.2%, and 81.9%, respectively. These results demonstrate our model’s effectiveness in overcoming illumination variations while maintaining high sensitivity to fine plant structures. This research contributes an optimized computer vision solution for precision agriculture applications, particularly enabling automated field management systems through reliable crop detection in challenging environmental conditions. Full article
(This article belongs to the Special Issue Computational, AI and IT Solutions Helping Agriculture)
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26 pages, 44426 KiB  
Article
Deep Learning-Based Seedling Row Detection and Localization Using High-Resolution UAV Imagery for Rice Transplanter Operation Quality Evaluation
by Yangfan Luo, Jiuxiang Dai, Shenye Shi, Yuanjun Xu, Wenqi Zou, Haojia Zhang, Xiaonan Yang, Zuoxi Zhao and Yuanhong Li
Remote Sens. 2025, 17(4), 607; https://doi.org/10.3390/rs17040607 - 11 Feb 2025
Viewed by 1033
Abstract
Accurately and precisely obtaining field crop information is crucial for evaluating the effectiveness of rice transplanter operations. However, the working environment of rice transplanters in paddy fields is complex, and data obtained solely from GPS devices installed on agricultural machinery cannot directly reflect [...] Read more.
Accurately and precisely obtaining field crop information is crucial for evaluating the effectiveness of rice transplanter operations. However, the working environment of rice transplanters in paddy fields is complex, and data obtained solely from GPS devices installed on agricultural machinery cannot directly reflect the specific information of seedlings, making it difficult to accurately evaluate the quality of rice transplanter operations. This study proposes a CAD-UNet model for detecting rice seedling rows based on low altitude orthorectified remote sensing images, and uses evaluation indicators such as straightness and parallelism of seedling rows to evaluate the operation quality of the rice transplanter. We have introduced convolutional block attention module (CBAM) and attention gate (AG) modules on the basis of the original UNet network, which can merge multiple feature maps or information flows together, helping the model better select key areas or features of seedling rows in the image, thereby improving the understanding of image content and task execution performance. In addition, in response to the characteristics of dense and diverse shapes of seedling rows, this study attempts to integrate deformable convolutional network version 2 (DCNv2) into the UNet network, replacing the original standard square convolution, making the sampling receptive field closer to the shape of the seedling rows and more suitable for capturing various shapes and scales of seedling row features, further improving the performance and generalization ability of the model. Different semantic segmentation models are trained and tested using low altitude high-resolution images of drones, and compared. The experimental results indicate that CAD-UNet provides excellent results, with precision, recall, and F1-score reaching 91.14%, 87.96%, and 89.52%, respectively, all of which are superior to other models. The evaluation results of the rice transplanter’s operation effectiveness show that the minimum and maximum straightnessof each seedling row are 4.62 and 13.66 cm, respectively, and the minimum and maximum parallelismbetween adjacent seedling rows are 5.16 and 23.34 cm, respectively. These indicators directly reflect the distribution of rice seedlings in the field, proving that the proposed method can quantitatively evaluate the field operation quality of the transplanter. The method proposed in this study can be applied to decision-making models for farmland crop management, which can help improve the efficiency and sustainability of agricultural operations. Full article
(This article belongs to the Section AI Remote Sensing)
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21 pages, 5903 KiB  
Article
Composite Materials Based on Spent Coffee Grounds and Paper Pulp
by Victoria Bejenari, Maricel Danu, Alina-Mirela Ipate, Mirela-Fernanda Zaltariov, Daniela Rusu and Gabriela Lisa
J. Compos. Sci. 2024, 8(12), 491; https://doi.org/10.3390/jcs8120491 - 24 Nov 2024
Viewed by 3442
Abstract
The need for biodegradable and environmentally friendly materials is increasing due to resource shortages and rising levels of environmental pollution. Agro-food waste, which includes coffee grounds, is of great interest in the production of composite materials due to its low cost, low density, [...] Read more.
The need for biodegradable and environmentally friendly materials is increasing due to resource shortages and rising levels of environmental pollution. Agro-food waste, which includes coffee grounds, is of great interest in the production of composite materials due to its low cost, low density, easy availability, non-abrasive nature, specific properties such as reduced wear on the machinery used, the absence of residues and toxic products, and biodegradable characteristics. The composite materials developed that include coffee grounds exhibit good characteristics. This field is evolving and requires further improvements, but, at this moment, it can be stated that coffee grounds are not just waste but can be transformed into a highly efficient material applicable in various domains. In this study, composite materials were prepared using paper pulp as a matrix, coffee grounds as a filler material, and water as a binding agent. The obtained composite materials were evaluated through thermal analysis, SEM, EDX, ATR-FTIR, and rheological behavior analysis. The composite materials created from paper pulp and coffee grounds proved to be effective for use in the production of seedling pots. The seedling pots created in this study are produced at a low cost, are environmentally friendly, exhibit thermal stability, have good stability over time, and have good resistance to deformation. Full article
(This article belongs to the Section Composites Applications)
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15 pages, 6071 KiB  
Article
One-Year-Old Precocious Chinese Mitten Crab Identification Algorithm Based on Task Alignment
by Hao Gu, Dongmei Gan, Ming Chen and Guofu Feng
Animals 2024, 14(14), 2128; https://doi.org/10.3390/ani14142128 - 21 Jul 2024
Cited by 1 | Viewed by 1326
Abstract
The cultivation of the Chinese mitten crab (Eriocheir sinensis) is an important component of China’s aquaculture industry and also a field of concern worldwide. It focuses on the selection of high-quality, disease-free juvenile crabs. However, the early maturity rate of more [...] Read more.
The cultivation of the Chinese mitten crab (Eriocheir sinensis) is an important component of China’s aquaculture industry and also a field of concern worldwide. It focuses on the selection of high-quality, disease-free juvenile crabs. However, the early maturity rate of more than 18.2% and the mortality rate of more than 60% make it difficult to select suitable juveniles for adult culture. The juveniles exhibit subtle distinguishing features, and the methods for differentiating between sexes vary significantly; without training from professional breeders, it is challenging for laypersons to identify and select the appropriate juveniles. Therefore, we propose a task-aligned detection algorithm for identifying one-year-old precocious Chinese mitten crabs, named R-TNET. Initially, the required images were obtained by capturing key frames, and then they were annotated and preprocessed by professionals to build a training dataset. Subsequently, the ResNeXt network was selected as the backbone feature extraction network, with Convolutional Block Attention Modules (CBAMs) and a Deformable Convolution Network (DCN) embedded in its residual blocks to enhance its capability to extract complex features. Adaptive spatial feature fusion (ASFF) was then integrated into the feature fusion network to preserve the detailed features of small targets such as one-year-old precocious Chinese mitten crab juveniles. Finally, based on the detection head proposed by task-aligned one-stage object detection, the parameters of its anchor alignment metric were adjusted to detect, locate, and classify the crab juveniles. The experimental results showed that this method achieves a mean average precision (mAP) of 88.78% and an F1-score of 97.89%. This exceeded the best-performing mainstream object detection algorithm, YOLOv7, by 4.17% in mAP and 1.77% in the F1-score. Ultimately, in practical application scenarios, the algorithm effectively identified one-year-old precocious Chinese mitten crabs, providing technical support for the automated selection of high-quality crab juveniles in the cultivation process, thereby promoting the rapid development of aquaculture and agricultural intelligence in China. Full article
(This article belongs to the Section Aquatic Animals)
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21 pages, 6702 KiB  
Article
Design and Experiment of a Soil-Covering and -Pressing Device for Planters
by Qi Lu, Jinhui Zhao, Lijing Liu, Zhongjun Liu and Chunlei Wang
Agriculture 2024, 14(7), 1040; https://doi.org/10.3390/agriculture14071040 - 28 Jun 2024
Cited by 1 | Viewed by 1767
Abstract
In response to the practical production challenges posed by the unreliable operation of the V-shaped squeezing soil-covering and -pressing device (VCP) for planters under clay soil conditions in Northeast China, incomplete seed furrow closure, and severe soil adhesion on pressing wheels, this study [...] Read more.
In response to the practical production challenges posed by the unreliable operation of the V-shaped squeezing soil-covering and -pressing device (VCP) for planters under clay soil conditions in Northeast China, incomplete seed furrow closure, and severe soil adhesion on pressing wheels, this study proposes a device with star-toothed concave discs for soil-covering and -pressing (STCP) with the aim of enhancing the soil-covering quality of planters. The main working principles of STCP were expounded, and its main structural and installation parameters were determined and designed. Based on bionics, with the dung beetle’s protruding head structure as the research object and UHMWPE as the material, an optimized protuberance-type bionic pressing wheel was designed. A Box–Behnken experiment was conducted by taking the width of the compression wheel, the spring deformation, and the installation angle as experimental factors, as well as the weight of the soil adhered to the surface of the pressing wheel (SW) and the soil compactness (SC) as the evaluation indicators. The optimal structural parameters of the pressing device were determined as follows: the width of the pressing wheel was 60.57 mm, the spring deformation was 55.19 mm, and the installation angle was 10.70°. The field comparison tests of soil covering performance showed that the star-tooth concave disc soil-covering device can effectively solve the problem of seed “hanging” and “drying”. The average covered soil weight of the star-tooth concave disc soil-covering device was 241.46 g, and the average covered soil weight of VCP was 223.56 g. Compared with VCP, the average covered soil weight of STCP increased by 8.01%. The variation coefficient of covered soil weight after the operation of the star-tooth concave disc soil-covering device was 3.71%, which was more uniform than VCP. The field comparison tests of soil-covering thickness showed that the uniformity of soil-covering thickness can be significantly improved by adding a star-tooth concave disc soil-covering device to VCP. The comparative tests of soil anti-adhesive showed that the convex hull type pressing wheels optimized by bionics had better soil anti-adhesive performance, and the soil adhesion weight was reduced by 43.68% compared with VCP. The field comparative tests of seedling emergence showed that the seedling emergence rate after STCP operation was 3.9% higher than that of VCP. Full article
(This article belongs to the Section Agricultural Technology)
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16 pages, 2004 KiB  
Article
Root Zone Water Management Effects on Soil Hydrothermal Properties and Sweet Potato Yield
by Shihao Huang, Lei Zhao, Tingge Zhang, Minghui Qin, Tao Yin, Qing Liu and Huan Li
Plants 2024, 13(11), 1561; https://doi.org/10.3390/plants13111561 - 5 Jun 2024
Cited by 1 | Viewed by 1558
Abstract
Sufficient soil moisture is required to ensure the successful transplantation of sweet potato seedlings. Thus, reasonable water management is essential for achieving high quality and yield in sweet potato production. We conducted field experiments in northern China, planted on 18 May and harvested [...] Read more.
Sufficient soil moisture is required to ensure the successful transplantation of sweet potato seedlings. Thus, reasonable water management is essential for achieving high quality and yield in sweet potato production. We conducted field experiments in northern China, planted on 18 May and harvested on 18 October 2021, at the Nancun Experimental Base of Qingdao Agricultural University. Three water management treatments were tested for sweet potato seedlings after transplanting: hole irrigation (W1), optimized drip irrigation (W2), and traditional drip irrigation (W3). The variation characteristics of soil volumetric water content, soil temperature, and soil CO2 concentration in the root zone were monitored in situ for 0–50 days. The agronomy, root morphology, photosynthetic parameters, 13C accumulation, yield, and yield components of sweet potato were determined. The results showed that soil VWC was maintained at 22–25% and 27–32% in the hole irrigation and combined drip irrigation treatments, respectively, from 0 to 30 days after transplanting. However, there was no significant difference between the traditional (W3) and optimized (W2) drip irrigation systems. From 30 to 50 days after transplanting, the VWC decreased significantly in all treatments, with significant differences among all treatments. Soil CO2 concentrations were positively correlated with VWC from 0 to 30 days after transplanting but gradually increased from 30 to 50 days, with significant differences among treatments. Soil temperature varied with fluctuations in air temperature, with no significant differences among treatments. Sweet potato survival rates were significantly lower in the hole irrigation treatments than in the drip irrigation treatments, with no significant difference between W2 and W3. The aboveground biomass, photosynthetic parameters, and leaf area index were significantly higher under drip irrigation than under hole irrigation, and values were higher in W3 than in W2. However, the total root length, root volume, and 13C partitioning rate were higher in W2 than in W3. These findings suggest that excessive drip irrigation can lead to an imbalance in sweet potato reservoir sources. Compared with W1, the W2 and W3 treatments exhibited significant yield increases of 42.98% and 36.49%, respectively. The W2 treatment had the lowest sweet potato deformity rate. Full article
(This article belongs to the Special Issue Strategies to Improve Water-Use Efficiency in Plant Production)
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15 pages, 9376 KiB  
Article
Comparison and Optimal Method of Detecting the Number of Maize Seedlings Based on Deep Learning
by Zhijie Jia, Xinlong Zhang, Hongye Yang, Yuan Lu, Jiale Liu, Xun Yu, Dayun Feng, Kexin Gao, Jianfu Xue, Bo Ming, Chenwei Nie and Shaokun Li
Drones 2024, 8(5), 175; https://doi.org/10.3390/drones8050175 - 28 Apr 2024
Cited by 3 | Viewed by 2047
Abstract
Effective agricultural management in maize production operations starts with the early quantification of seedlings. Accurately determining plant presence allows growers to optimize planting density, allocate resources, and detect potential growth issues early on. This study provides a comprehensive analysis of the performance of [...] Read more.
Effective agricultural management in maize production operations starts with the early quantification of seedlings. Accurately determining plant presence allows growers to optimize planting density, allocate resources, and detect potential growth issues early on. This study provides a comprehensive analysis of the performance of various object detection models in maize production, with a focus on the effects of planting density, growth stages, and flight altitudes. The findings of this study demonstrate that one-stage models, particularly YOLOv8n and YOLOv5n, demonstrated superior performance with AP50 scores of 0.976 and 0.951, respectively, outperforming two-stage models in terms of resource efficiency and seedling quantification accuracy. YOLOv8n, along with Deformable DETR, Faster R-CNN, and YOLOv3-tiny, were identified for further examination based on their performance metrics and architectural features. The study also highlights the significant impact of plant density and growth stage on detection accuracy. Increased planting density and advanced growth stages (particularly V6) were associated with decreased model accuracy due to increased leaf overlap and image complexity. The V2–V3 growth stages were identified as the optimal periods for detection. Additionally, flight altitude negatively affected image resolution and detection accuracy, with higher altitudes leading to poorer performance. In field applications, YOLOv8n proved highly effective, maintaining robust performance across different agricultural settings and consistently achieving rRMSEs below 1.64% in high-yield fields. The model also demonstrated high reliability, with Recall, Precision, and F1 scores exceeding 99.00%, affirming its suitability for practical agricultural use. These findings suggest that UAV-based image collection systems employing models like YOLOv8n can significantly enhance the accuracy and efficiency of seedling detection in maize production. The research elucidates the critical factors that impact the accuracy of deep learning detection models in the context of corn seedling detection and selects a model suited for this specific task in practical agricultural production. These findings offer valuable insights into the application of object detection technology and lay a foundation for the future development of precision agriculture, particularly in optimizing deep learning models for varying environmental conditions that affect corn seedling detection. Full article
(This article belongs to the Special Issue UAS in Smart Agriculture: 2nd Edition)
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13 pages, 5571 KiB  
Article
Cabbage Transplantation State Recognition Model Based on Modified YOLOv5-GFD
by Xiang Sun, Yisheng Miao, Xiaoyan Wu, Yuansheng Wang, Qingxue Li, Huaji Zhu and Huarui Wu
Agronomy 2024, 14(4), 760; https://doi.org/10.3390/agronomy14040760 - 8 Apr 2024
Cited by 5 | Viewed by 1311
Abstract
To enhance the transplantation effectiveness of vegetables and promptly formulate subsequent work strategies, it is imperative to study the recognition approach for transplanted seedlings. In the natural and complex environment, factors like background and sunlight often hinder accurate target recognition. To overcome these [...] Read more.
To enhance the transplantation effectiveness of vegetables and promptly formulate subsequent work strategies, it is imperative to study the recognition approach for transplanted seedlings. In the natural and complex environment, factors like background and sunlight often hinder accurate target recognition. To overcome these challenges, this study explores a lightweight yet efficient algorithm for recognizing cabbage transplantation states in natural settings. Initially, FasterNet is integrated as the backbone network in the YOLOv5s model, aiming to expedite convergence speed and bolster feature extraction capabilities. Secondly, the introduction of the GAM attention mechanism enhances the algorithm’s focus on cabbage seedlings. EIoU loss is incorporated to improve both network convergence speed and localization precision. Lastly, the model incorporates deformable convolution DCNV3, which further optimizes model parameters and attains a superior balance between accuracy and speed. Upon testing the refined YOLOv5s target detection algorithm, improvements were evident. When compared to the original model, the mean average precision (mAP) rose by 3.5 percentage points, recall increased by 1.7 percentage points, and detection speed witnessed an impressive boost of 52 FPS. This enhanced algorithm not only reduces model complexity but also elevates network performance. The method is expected to streamline transplantation quality measurements, minimize time and labor inputs, and elevate field transplantation quality surveys’ automation levels. Full article
(This article belongs to the Special Issue Effects of Integrated Environment Management on Crop Photosynthesis)
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17 pages, 3517 KiB  
Article
Antifungal and Plant-Growth Promotion Effects of Bacillus velezensis When Applied to Coastal to Pine (Pinus thunbergii Parl.) Seedlings
by Ju-Yeol Yun, Hyun-Seop Kim, Jae-Hyun Moon, Sang-Jae Won, Vantha Choub, Su-In Choi, Henry B. Ajuna, Peter Sang-Hoon Lee and Young Sang Ahn
Forests 2024, 15(1), 62; https://doi.org/10.3390/f15010062 - 28 Dec 2023
Cited by 4 | Viewed by 2028
Abstract
Fungal diseases such as root rot and leaf blight cause substantial losses in coastal pine (Pinus thunbergii Parl.) seedling production, which hinders afforestation/forest restoration programs. We isolated and identified Fusarium oxysporum and Alternaria alternata as the causal agents of root rot and [...] Read more.
Fungal diseases such as root rot and leaf blight cause substantial losses in coastal pine (Pinus thunbergii Parl.) seedling production, which hinders afforestation/forest restoration programs. We isolated and identified Fusarium oxysporum and Alternaria alternata as the causal agents of root rot and needle blight diseases and investigated the biocontrol efficacy against the fungal pathogens and growth promotion of coastal pine seedlings using Bacillus velezensis CE 100. The bacterium produced the hydrolytic enzymes chitinase, β-1,3-glucanase, and protease enzymes, and the crude enzyme fraction of the biocontrol strain caused the deformation of the fungal cell wall and antagonized F. oxysporum and A. alternata, causing respective inhibition of spore germination by 91.0% and 85.9% and mycelial growth by 58.3% and 54.3%, at a concentration of 1000 µL/mL. Consequently, the bacterial treatment improved the survival rate of seedlings 1.9 times relative to the control group. The bacterium secreted indole-acetic acid (IAA) phytohormone and enhanced root growth and absorption of nutrients, which notably enhanced the biomass production of coastal pine seedlings. Therefore, these results provide evidence that B. velezensis CE 100 is an effective antifungal and growth-promoting bacterium that can facilitate the production of high-quality coastal pine seedlings for the restoration and establishment of coastal forests. Full article
(This article belongs to the Section Forest Health)
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14 pages, 1575 KiB  
Article
Optimization of In Vitro Embryo Rescue and Development of a Kompetitive Allele-Specific PCR (KASP) Marker Related to Stenospermocarpic Seedlessness in Grape (Vitis vinifera L.)
by Xiaojun Xi, Benjamin Gutierrez, Qian Zha, Xiangjing Yin, Pengpeng Sun and Aili Jiang
Int. J. Mol. Sci. 2023, 24(24), 17350; https://doi.org/10.3390/ijms242417350 - 11 Dec 2023
Cited by 2 | Viewed by 1638
Abstract
Seedlessness is one of the highest valued agronomic traits in grapes. Embryo rescue in combination with marker-assisted selection have been widely applied in seedless grape breeding due to the advantages of increasing the ratio of seedless progenies and shortening the breeding cycle. However, [...] Read more.
Seedlessness is one of the highest valued agronomic traits in grapes. Embryo rescue in combination with marker-assisted selection have been widely applied in seedless grape breeding due to the advantages of increasing the ratio of seedless progenies and shortening the breeding cycle. However, the large number of deformed seedlings produced during embryo rescue and the lack of fast, efficient, and low-cost markers severely inhibit the process of seedless grape breeding. In this study, a total of eighty-three grape cultivars (51 seedless and 32 seeded) with diverse genetic backgrounds and two populations derived from embryo rescue, including 113 F1 hybrid individuals (60 seedless and 53 seeded), were utilized. We screened suitable media for converting malformed seedlings into normal seedlings, analyzed the association between the SNP in VviAGL11 and seeded/seedless phenotype, and developed a KASP marker related to stenospermocarpic seedlessness. Our results indicated that the transformation rate of 37.8% was obtained with MS medium supplemented with 2.0 mg·L−1 of 6-BA and 0.5 mg·L−1 of IBA. The presence of an A nucleotide allele at position chr18:26889437 was further confirmed to be fully associated with the stenospermocarpic seedlessness phenotype. The developed KASP marker, based on the verified SNP locus in VviAGL11, successfully distinguished the seedless and seeded genotypes with high precision and throughput. The results will contribute to enhancing the efficiency of embryo rescue and facilitate parent selection and early selection of seedless offspring with molecular markers, thereby accelerating the breeding process in seedless table grapes. Full article
(This article belongs to the Special Issue Advances in Research for Fruit Crop Breeding and Genetics 2023)
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19 pages, 14107 KiB  
Article
A Biocontrol Strain of Serratia plymuthica MM Promotes Growth and Controls Fusarium Wilt in Watermelon
by Zhaoyu Li, Jinxiu Ma, Jiajia Li, Yinglong Chen, Zhihong Xie, Yongqiang Tian, Xu Su, Tian Tian and Tong Shen
Agronomy 2023, 13(9), 2437; https://doi.org/10.3390/agronomy13092437 - 21 Sep 2023
Cited by 6 | Viewed by 2625
Abstract
Fusarium wilt, caused by Fusarium oxysporum f. sp. niveum (FON), is a predominant and devastating soil-borne disease that results in significant yield losses in watermelon cultivation. In this study, a strain MM isolated from the herbage rhizosphere soil, exhibited an inhibition rate of [...] Read more.
Fusarium wilt, caused by Fusarium oxysporum f. sp. niveum (FON), is a predominant and devastating soil-borne disease that results in significant yield losses in watermelon cultivation. In this study, a strain MM isolated from the herbage rhizosphere soil, exhibited an inhibition rate of 65.46% against FON, leading to mycelial collapse, atrophy, and deformation. In pot experiments, strain MM effectively controlled Fusarium wilt of watermelon, showing a control efficacy of 74.07%. Through morphological observation and 16S rDNA gene sequencing, strain MM was identified as Serratia plymuthica. Additionally, S. plymuthica MM demonstrated antagonistic activity against eight plant pathogens, indicating that MM had broad-spectrum antifungal activity. The strain also exhibited the ability to synthesize siderophores and indole acetic acid (IAA), both of which are growth-promoting compounds. Moreover, strain MM secreted various extracellular enzymes, including protease, chitinase, β-glucanase, and cellulase. This ability allowed S. plymuthica MM to readily colonize watermelon roots and promote seedling growth. Inoculation with S. plymuthica MM increased the activity of PAL, POD, PPO, and CAT enzymes associated with watermelon defense. Furthermore, qRT-PCR analysis revealed up-regulation of LOX, POD, PAL, ClPR3, and C4H genes, which are related to plant disease resistance. The results indicated that S. plymuthica MM enhances watermelon plants’ resistance to FON by activating the JA, SA, and shikimic acid phenylpropanoid–lignin synthesis pathways. Gas chromatography–mass spectrometry (GC-MS) analysis of S. plymuthica MM culture supernatant identified piperazinedione, pyrrolo[1,2-a]pyrazine-1,4-dione, and octadecenamide as the main antimicrobial substances. Overall, S. plymuthica MM shows promise as a biocontrol agent against Fusarium wilt of watermelon, suggesting its potential for the development of a new biocontrol agent. Full article
(This article belongs to the Special Issue Advances in Plant–Fungal Pathogen Interactions)
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23 pages, 9576 KiB  
Article
Effect of Waterlogging Stress on Leaf Anatomical Structure and Ultrastructure of Phoebe sheareri Seedlings
by Fenghou Shi, Zhujing Pan, Pengfei Dai, Yongbao Shen, Yizeng Lu and Biao Han
Forests 2023, 14(7), 1294; https://doi.org/10.3390/f14071294 - 23 Jun 2023
Cited by 9 | Viewed by 3167
Abstract
Phoebe sheareri is an excellent roadside tree with a wide distribution range and high ornamental value. Excessive moisture can affect the external morphology, the microstructure, and the ultrastructure of the leaf. Little is known at present regarding the leaf structure of P. sheareri [...] Read more.
Phoebe sheareri is an excellent roadside tree with a wide distribution range and high ornamental value. Excessive moisture can affect the external morphology, the microstructure, and the ultrastructure of the leaf. Little is known at present regarding the leaf structure of P. sheareri under waterlogging stress. In this paper, the external morphology of leaves, the microstructure of leaf epidermis, and the ultrastructure of mesophyll cells of P. sheareri seedlings under waterlogging stress and drainage were dynamically observed. Waterlogging stress contributed to the yellowing and wilting of P. sheareri seedling leaves, the gradual closing of leaf epidermal stomata, increasing density of leaf stomata, gradual loosening of the arrangement of leaf cell structure, and merging of leaf palisade tissue cells. Waterlogging stress forced the structure of the chloroplast membranes to blur, gradually causing swelling, and deformation, with plasmolysis occurring in severe cases. During waterlogging, the basal lamellae were disorganized, and the mitochondrial membrane structure was damaged. The damaged state of the leaves was not relieved after drainage. Waterlogging stress not only inhibited the growth of leaves, but also accelerated the closure of stomata, disordered the arrangement of palisade tissue and spongy tissue gradually, and damaged the internal organelles of mesophyll cells. Full article
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14 pages, 2335 KiB  
Article
The Potential of Paulownia fortunei L. for the Phytoremediation of Pb
by Lu Du, Hang Yang, Juan Xie, Liangze Han, Zhiyi Liu, Zhiming Liu, Yonghua Chen and Rongkui Su
Forests 2023, 14(6), 1245; https://doi.org/10.3390/f14061245 - 15 Jun 2023
Cited by 7 | Viewed by 1962
Abstract
Pb endangers forest ecological health; phytoremediation is an effective Pb remediation technology. Woody plants with Pb tolerance provided a mechanism for the phytoremediation of Pb. Paulownia fortunei (L.), a fast-growing woody plant, has a good tolerance to Pb. However, its tolerance mechanism is [...] Read more.
Pb endangers forest ecological health; phytoremediation is an effective Pb remediation technology. Woody plants with Pb tolerance provided a mechanism for the phytoremediation of Pb. Paulownia fortunei (L.), a fast-growing woody plant, has a good tolerance to Pb. However, its tolerance mechanism is unclear. The results in this study revealed that P. fortunei seedlings can withstand 400 mg·L−1 Pb stress. The quantification of Pb in different P. fortunei tissues showed an increasing trend of accumulation in root > leaf > stem; the transport coefficient and enrichment coefficient decreased with an increase in Pb concentration. The tolerance of P. fortunei to Pb may be related to cell partition and immobilization by the cell wall. Microstructural analysis performed using scanning electron microscopy showed that the absorbed Pb is mainly distributed in cell wall components, and when the concentration of Pb increases, it can be transferred to soluble parts and organelles. The Fourier transform infrared spectrometry results showed that excess hydroxyl groups occurred under Pb stress in the outer epidermis cell walls of roots and leaves adsorbing heavy metals. When the concentration of Pb was over 400 mg·L−1, the growth of P. fortunei was inhibited, the root cell wall was deformed, the plasmolysis occurred in the cauline cell, and the internal leaf capsule was ruptured. Furthermore, antioxidant enzyme activity was significantly reduced. Therefore, P. fortunei can transfer the underground part of Pb to the aboveground part up to the concentration of 400 mg·L−1. This study provides a theoretical basis and technical reference for fully utilizing woody plant resources to restore the ecological environment of forests. Full article
(This article belongs to the Special Issue Soil Contamination in Forest Ecosystem)
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18 pages, 7465 KiB  
Article
Salt Stress Inhibits Photosynthesis and Destroys Chloroplast Structure by Downregulating Chloroplast Development–Related Genes in Robinia pseudoacacia Seedlings
by Chaoxia Lu, Lingyu Li, Xiuling Liu, Min Chen, Shubo Wan and Guowei Li
Plants 2023, 12(6), 1283; https://doi.org/10.3390/plants12061283 - 11 Mar 2023
Cited by 47 | Viewed by 5571
Abstract
Soil salinization is an important factor limiting food security and ecological stability. As a commonly used greening tree species, Robinia pseudoacacia often suffers from salt stress that can manifest as leaf yellowing, decreased photosynthesis, disintegrated chloroplasts, growth stagnation, and even death. To elucidate [...] Read more.
Soil salinization is an important factor limiting food security and ecological stability. As a commonly used greening tree species, Robinia pseudoacacia often suffers from salt stress that can manifest as leaf yellowing, decreased photosynthesis, disintegrated chloroplasts, growth stagnation, and even death. To elucidate how salt stress decreases photosynthesis and damages photosynthetic structures, we treated R. pseudoacacia seedlings with different concentrations of NaCl (0, 50, 100, 150, and 200 mM) for 2 weeks and then measured their biomass, ion content, organic soluble substance content, reactive oxygen species (ROS) content, antioxidant enzyme activity, photosynthetic parameters, chloroplast ultrastructure, and chloroplast development-related gene expression. NaCl treatment significantly decreased biomass and photosynthetic parameters, but increased ion content, organic soluble substances, and ROS content. High NaCl concentrations (100–200 mM) also led to distorted chloroplasts, scattered and deformed grana lamellae, disintegrated thylakoid structures, irregularly swollen starch granules, and larger, more numerous lipid spheres. Compared to control (0 mM NaCl), the 50 mM NaCl treatment significantly increased antioxidant enzyme activity while upregulating the expression of the ion transport-related genes Na+/H+ exchanger 1(NHX 1) and salt overly sensitive 1 (SOS 1) and the chloroplast development-related genes psaA, psbA, psaB, psbD, psaC, psbC, ndhH, ndhE, rps7, and ropA. Additionally, high concentrations of NaCl (100–200 mM) decreased antioxidant enzyme activity and downregulated the expression of ion transport- and chloroplast development-related genes. These results showed that although R. pseudoacacia can tolerate low concentrations of NaCl, high concentrations (100–200 mM) can damage chloroplast structure and disturb metabolic processes by downregulating gene expression. Full article
(This article belongs to the Special Issue Abiotic Stress Signaling and Responses in Plants)
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12 pages, 983 KiB  
Article
Controlling Pepper Mild Mottle Virus (PMMoV) Infection in Pepper Seedlings by Use of Chemically Synthetic Silver Nanoparticles
by Esam K. F. Elbeshehy, Wael M. Hassan and Areej A. Baeshen
Molecules 2023, 28(1), 139; https://doi.org/10.3390/molecules28010139 - 24 Dec 2022
Cited by 10 | Viewed by 3130
Abstract
We investigated the roles of different concentrations of chemical synthetic spherical silver nanoparticles (AgNPs) in protecting pepper seedlings of the Mecca region, which were naturally and artificially infected by the pepper mild mottle virus (PMMoV). The virus shows many infection symptoms, including pepper [...] Read more.
We investigated the roles of different concentrations of chemical synthetic spherical silver nanoparticles (AgNPs) in protecting pepper seedlings of the Mecca region, which were naturally and artificially infected by the pepper mild mottle virus (PMMoV). The virus shows many infection symptoms, including pepper leaf deformation with filiform leaves and severe mosaic symptoms. Our study focused on the antiviral activity of different concentrations of spherical nanoparticles in controlling PMMoV infecting pepper seedlings. PMMoV identification was confirmed via DAS-ELISA using the following antiserum: PMMoV, cucumber mosaic virus (CMV), tobacco mosaic virus (TMV), tomato mosaic virus (ToMV), potato virus Y (PVY), and tomato spotted wilt virus (TSWV). The presence of PMMoV was confirmed using electron microscopy and reverse transcription polymerase chain reaction (RT-PCR). We evaluated the effects of exogenously applied different concentrations of AgNPs on CMV infection rate, infection severity, virus concentration, and the concentrations of photosynthetic pigments chlorophyll a, chlorophyll b, carotenoid content, phenolic compounds, and protein components in virus-infected plant cells that were treated with three different concentration of nanoparticles (200, 300, and 400 µg/L) compared to the positive and negative control. Full article
(This article belongs to the Special Issue Bioactive Compounds: Design, Synthesis and Biological Evaluation)
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