Practical Applications In Agriculture Research Articles

Evaluation of Data Sufficiency for Interannual Knowledge Transfer of Crop Type Classification Models

We present a study on the effectiveness of using varying data sizes to transfer crop type classification models from one year to the next, emphasizing the balance between data sufficiency and model accuracy. The significance of crop detection through satellite imaging lies in its potential to enhance agricultural productivity and resource management. Machine learning, particularly techniques like long short-term memory (LSTM) models, has become instrumental in interpreting these satellite data due to its predictive accuracy and adaptability. However, the direct application of models trained in one year to subsequent years poses challenges due to variations in environmental conditions and agricultural practices. Fine-tuning pre-existing models is a prevalent strategy to overcome these temporal discrepancies, though it necessitates a careful evaluation of the quantity and relevance of new data. This study explores the cost–benefit of fine-tuning existing models versus developing new ones based on the quantity of new data, utilizing LSTM models for their transferability and practicality in agricultural applications. Experiments conducted using satellite data from farms in southern Alberta reveal that smaller datasets, with fewer than 25 fields per class, can effectively fine-tune models for accurate interannual classification, while larger datasets are more conducive to training new models. This poses a key challenge in optimizing data usage for crop classification, straddling the line between data sufficiency and computational efficiency. The findings offer valuable insights for optimizing data use in crop classification, benefiting both academic research and practical agricultural applications.

A DNA demethylase reduces seed size by decreasing the DNA methylation of AT-rich transposable elements in soybean

Understanding how to increase soybean yield is crucial for global food security. The genetic and epigenetic factors influencing seed size, a major crop yield determinant, are not fully understood. We explore the role of DNA demethylase GmDMEa in soybean seed size. Our research indicates that GmDMEa negatively correlates with soybean seed size. Using CRISPR-Cas9, we edited GmDMEa in the Dongnong soybean cultivar, known for small seeds. Modified plants had larger seeds and greater yields without altering plant architecture or seed nutrition. GmDMEa preferentially demethylates AT-rich transposable elements, thus activating genes and transcription factors associated with the abscisic acid pathway, which typically decreases seed size. Chromosomal substitution lines confirm that these modifications are inheritable, suggesting a stable epigenetic method to boost seed size in future breeding. Our findings provide insights into epigenetic seed size control and suggest a strategy for improving crop yields through the epigenetic regulation of crucial genes. This work implies that targeted epigenetic modification has practical agricultural applications, potentially enhancing food production without compromising crop quality.

Detection Model of Tea Disease Severity under Low Light Intensity Based on YOLOv8 and EnlightenGAN

In response to the challenge of low recognition rates for similar phenotypic symptoms of tea diseases in low-light environments and the difficulty in detecting small lesions, a novel adaptive method for tea disease severity detection is proposed. This method integrates an image enhancement algorithm based on an improved EnlightenGAN network and an enhanced version of YOLO v8. The approach involves first enhancing the EnlightenGAN network through non-paired training on low-light-intensity images of various tea diseases, guiding the generation of high-quality disease images. This step aims to expand the dataset and improve lesion characteristics and texture details in low-light conditions. Subsequently, the YOLO v8 network incorporates ResNet50 as its backbone, integrating channel and spatial attention modules to extract key features from disease feature maps effectively. The introduction of adaptive spatial feature fusion in the Neck part of the YOLOv8 module further enhances detection accuracy, particularly for small disease targets in complex backgrounds. Additionally, the model architecture is optimized by replacing traditional Conv blocks with ODConv blocks and introducing a new ODC2f block to reduce parameters, improve performance, and switch the loss function from CIOU to EIOU for a faster and more accurate recognition of small targets. Experimental results demonstrate that YOLOv8-ASFF achieves a tea disease detection accuracy of 87.47% and a mean average precision (mAP) of 95.26%. These results show a 2.47 percentage point improvement over YOLOv8, and a significant lead of 9.11, 9.55, and 7.08 percentage points over CornerNet, SSD, YOLOv5, and other models, respectively. The ability to swiftly and accurately detect tea diseases can offer robust theoretical support for assessing tea disease severity and managing tea growth. Moreover, its compatibility with edge computing devices and practical application in agriculture further enhance its value.

MLP-based multimodal tomato detection in complex scenarios: Insights from task-specific analysis of feature fusion architectures

Accurate and efficient tomato detection is essential for the practical deployment of robotic picking in practical agricultural applications, but it still remains significantly challenging to detect tomatoes in complex scenarios with fluctuating light, overlapping fruits, and occlusion from branches and leaves when solely using RGB images. The recent development of RGB-D sensors has brought one promising opportunity to adopt multimodal fusion for implementing high-quality fruit detection. However, the feasibility of the existing multimodal fusion and feature extraction architectures for lightweight tomato detection tasks, especially in complex agricultural scenarios, raises questions that need to be explored. As a remedy, we proposed a multimodal fusion encoder that leveraged depth and near-infrared modalities to assist RGB images in making full use of multimodal data. Moreover, the encoder contained a plug-and-play structure capable of being implemented as MLP-based (Multi-Layer Perceptron), ViT-based (Vision Transformer), or CNN-based (Convolutional Neural Networks) architectures. Furthermore, we developed a lightweight experimental detection framework based on YOLOv7-tiny by means of integrating the multimodal fusion encoder, and YOLO-DNA (Depth and Near-infrared Assisted) was put forward based on the MLP-based architecture after conducting comprehensive analysis of the aforementioned three architectures. In addition, a tomato multimodal dataset containing visible, depth, and near-infrared images was established. Experimental results demonstrated that YOLO-DNA achieved mAP0.5 of 98.13% and mAP0.5:0.95 of 74.0%, an average increase of 5.01% in mAP0.5 and 14.55% in mAP0.5:0.95 over mainstream lightweight detection models, with a detection speed of 37.12 FPS, meeting the demand of real-time tomato detection. This finding has the potential to advance research on fruit detection in the field of intelligent agricultural harvesting.

Microbial inoculation alters rhizoplane bacterial community and correlates with increased rice yield

Microbial inoculants are recognized as environmentally friendly methods to promote plant growth and improve soil properties. However, the effects of inoculation on the rhizosphere and rhizoplane community structure of plants remain poorly documented and need further investigation. Rhodopseudomonas palustris (R. palustris) strain has nitrogen fixing ability and Bacillus subtilis (B. subtilis) strain is a plant growth-promoting rhizobacterium (PGPR). In this study, we investigated the effects of single and co-inoculation with R. palustris and B. subtilis on the increase of rice yield as well as on the microbial communities in the rhizosphere and rhizoplane of rice through potting experiments, respectively. The results showed that inoculation significantly increased rice yield and seed setting rate, with co-inoculation raising the yield by up to 13.7%. Inoculation influenced both rhizosphere and rhizoplane community structures and functions, amplifying the differences between them. The most significant changes were brought about by the combined inoculation treatment. Co-inoculation with R. palustris and B. subtilis had a synergistic effect. The profound alterations of rhizoplane bacterial community structures and functions were proved to be positively correlated with rice yield and seed setting rate (r = 0.59–0.76, p < 0.05). These results provide new ideas for the investigation of the potential microbiological mechanisms of microbial co-inoculation in practical agricultural applications.

Synthesis, characterization, and swelling properties of a novel tapioca-g-Poly(Acrylic acid−2−acrylamido−2−methylpropane sulfonic acid)/ammonium polyphosphate superabsorbent polymer

In this study, a novel superabsorbent polymer tapioca starch-g-poly(acrylic acid-2-acrylamido-2-methylpropane sulfonic acid)/ammonium polyphosphate (TS-g-AA-AMPS/APP) was synthesized based on the graft copolymerization of tapioca starch (TS) with acrylic acid (AA) and 2-acrylamido-2-methyl-1-propanesulfonic acid (AMPS) via free radical polymerization in aqueous solution with ammonium polyphosphate (APP) additive added for study. The synthesized superabsorbent polymer material was characterized by Fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscopy (FE-SEM), energy-dispersive x-ray spectroscopy (EDS), and thermogravimetric analysis (TGA). Research investigating the material synthesis conditions to absorbance in distilled water and 0.9 wt% NaCl solution has also been studied. Under optimal synthesis conditions, the absorbance in distilled water and 0.9 wt% NaCl solution were 416 g g−1 and 61 g g−1, respectively, for the sample with 3 wt% APP content. The introduction of APP units has improved the absorption properties of the material such as the water retention capacity reaching 55.25% in 10 h at 60 °C. TS-AA-AMPS/APP exhibits reversible swelling ability, with the swelling level not being reduced compared to the initial after 5 swelling-drying cycles at 60 °C, and water absorption reaches swelling equilibrium after about 270 min. Additionally, TGA thermogravimetric analysis results showed an improvement in the thermal stability of TS-AA-AMPS/APP compared to the pure polymer. These results show that the TS-AA-AMPS/APP polymer with excellent swelling properties, low production cost, and environmentally friendly has the potential for practical applications in agriculture, gardening, and water retention materials.

EfficientNet architecture and attention mechanism-based wheat disease identification model

The image-based automatic plant disease identification has emerged as a prominent research area, driven by recent advancements in machine learning algorithms. Conventional disease detection methods rely on manual feature extraction from acquired images, involving extensive expertise from experts and pathologists. This labor-intensive practice is often time-consuming and impractical. With the recent advancements in digital technologies and computer vision, Deep neural networks address these challenges by learning the features automatically. This paper focuses on the EfficientNet B0 architecture-based deep neural network model integrated with a convolutional block attention mechanism module for identifying major wheat crop diseases. The attention module in the EfficientNet B0 model improves its performance by emphasizing the disease-infected regions in leaf images. To evaluate the proposed methodology, experiments were conducted on the WheatRust21 image dataset, comprising 6556 images of three major fungal diseases, namely, stripe rust, leaf rust, and stem rust, collected in natural field conditions from 2019 to 2021. Our developed EfficientNet B0-CBAM model performed well with a testing accuracy of 98.70% and an impressive F1 score of 98% on the dataset. The Grad-CAM visualization demonstrates that the attention module focuses more on the infected region and plays a crucial role in wheat disease identification. The results are quantitatively substantial, with the model achieving high accuracy and other evaluation metrics scores with an average of 98.5 %. These findings hold promise for practical applications in agriculture and sustainable crop management.

Nicotiana noctiflora Hook. Genome Contains Two Cellular T-DNAs with Functional Genes.

Agrobacterium (Rhizobium)-mediated transformation leads to the formation of crown galls or hairy roots on infected plants. These effects develop due to the activity of T-DNA genes, gathered on a big plasmid, acquired from agrobacteria during horizontal gene transfer. However, a lot of plant species are known to contain such sequences, called cellular T-DNAs (cT-DNAs), and maintain normal phenotypes. Some of the genes remain intact, which leads to the conclusion of their functional role in plants. In this study, we present a comprehensive analysis of the cT-DNAs in the Nicotiana noctiflora Hook. genome, including gene expression and opine identification. Deep sequencing of the Nicotiana noctiflora genome revealed the presence of two different cT-DNAs, NnT-DNA1 and NnT-DNA2, which contain the intact genes iaaM, iaaH, acs, orf13, orf13a, and orf14. According to the expression analysis results, all these genes are most active in roots in comparison with other organs, which is consistent with data on cT-DNA gene expression in other plant species. We also used genetic engineering approaches and HPTLC and HPLC-MS methods to investigate the product of the acs gene (agrocinopine synthase), which turned out to be similar to agrocinopine A. Overall, this study expands our knowledge of cT-DNAs in plants and brings us closer to understanding their possible functions. Further research of cT-DNAs in different species and their functional implications could contribute to advancements in plant genetics and potentially unveil novel traits with practical applications in agriculture and other fields.

Bacillus velezensis BV01 Has Broad-Spectrum Biocontrol Potential and the Ability to Promote Plant Growth.

To evaluate the potential of a bacterial strain as a fungal disease control agent and plant growth promoter, its inhibitory effects on phytopathogens such as Bipolaris sorokiniana, Botrytis cinerea, Colletotrichum capsici, Fusarium graminearum, F. oxysporum, Neocosmospora rubicola, Rhizoctonia solani, and Verticillium dahliae were investigated. The results showed that the inhibitory rates in dual-culture and sterile filtrate assays against these eight phytopathogens ranged from 57% to 83% and from 36% to 92%. The strain was identified as Bacillus velezensis based on morphological and physiological characterization as well as phylogenetic analyses of 16S rRNA and the gyrase subunit A protein (gyrA) regions. The results demonstrated that B. velezensis was able to produce fungal cell-wall-degrading enzymes, namely, protease, cellulase, and β-1,3-glucanase, and the growth-promotion substances indole-3-acetic acid (IAA) and siderophore. Furthermore, B. velezensis BV01 had significant control effects on wheat root rot and pepper Fusarium wilt in a greenhouse. Potted growth-promotion experiments displayed that BV01 significantly increased the height, stem diameter, and aboveground fresh and dry weights of wheat and pepper. The results imply that B. velezensis BV01, a broad-spectrum biocontrol bacterium, is worth further investigation regarding its practical applications in agriculture.

Enhancing crop productivity and sustainability through disease identification in maize leaves: Exploiting a large dataset with an advanced vision transformer model

The timely identification of diseases in maize leaf offers several benefits such as increased crop productivity, reduced reliance on harmful chemicals, and improved production of healthy crops, resulting in enhanced economic returns. Computer-aided systems (CAD) play a crucial role in agriculture by enabling timely and efficient disease identification in plant leaves. Deep learning-based CAD systems facilitate accurate and rapid diagnosis of maize leaf diseases. In this research, we introduce an advanced vision transformer model that achieves exceptional accuracy and inference speed in detecting diseases in maize leaves. To begin with, we adapt the Multi-axis vision transformer (MaxViT) model to a 4-class maize dataset, creating a lightweight structure that offers improved accuracy and inference speed. Furthermore, we enhance the accuracy by replacing the conventional convolutional structure in the MaxViT architecture's Stem with a Squeeze-and-Excitation (SE) block. In addition, to boost accuracy further, we employ the Global Response Normalization (GRN)-based MLP from the ConvNexTv2 architecture instead of the MLP in the MaxViT architecture. Notably, we combine the PlantVillage, PlantDoc, and CD&S datasets from the literature, resulting in the creation of the most extensive dataset available. This dataset is then divided into three sets: training, validation, and testing, enabling the evaluation of the generalization abilities of the deep learning models. Our study goes beyond previous research by offering a comprehensive comparison of the performance of over 28 CNN models and more than 36 vision transformer models on the newly created dataset. By achieving a remarkable accuracy rate of 99.24% and a high inference speed, the proposed method outperforms all existing deep learning models in the literature. Therefore, it has been demonstrated that this advanced vision transformer model, based on MaxViT, is exceedingly effective for practical applications in agriculture.

Uracil hydrazones: design, synthesis, antimicrobial activities, and putative mode of action.

Crop diseases caused by plant pathogenic fungi and bacteria have led to substantial losses in global food production. Chemical pesticides have been widely used as a primary means to mitigate these issues. Nevertheless, the persistent and excessive use of pesticides has resulted in the emergence of microbial resistance. Moreover, the improper application and excessive utilization of pesticides can contribute to environmental pollution and the persistence of pesticide residues. Consequently, the development of novel and highly effective bactericides and fungicides to combat plant pathogens holds immense practical importance. A series of uracil hydrazones IV-B was deliberately designed and evaluated for their antimicrobial efficacy. The results of bioassays indicated that most IV-B exhibited >80% inhibition against the fungal species Monilia fructigena and Sclerotium rolfsii, as well as the bacterial species Clavibacter michiganensis subsp. michiganensis, Xanthomonas oryzae pv. oryzae, and Ralstonia solanacearum, at 50 μg/mL in vitro. In vivo, IV-B20 showed 89.9% of curative and 71.8% of protective activities against C. michiganensis subsp. michiganensis at 100 μg/mL superior to thiodiazole copper and copper hydroxide. IV-B20 also showed excellent protective activity against M. fructigena (96.3% at 200 μg/mL) and S. rolfsii (80.4% at 1000 μg/mL), which were greater than chlorothalonil and equivalent to thifluzamide. Mechanistic studies revealed that IV-B20 induced oxidative damage in pathogenic bacteria and promoted the leakage of cellular contents. This study suggests that IV-B20 with uracil hydrazone skeleton has great potential as an antimicrobial candidate. These findings lay a foundation for practical application in agriculture. © 2023 Society of Chemical Industry.

Recycling of spent Oyster mushroom (Pleurotus ostreatus) sawdust waste for the production of Straw mushroom (Volvariella volvacea)

The cultivation of straw mushroom Volvariella volvacea has a long history and has undergone continuous development. Nevertheless, ongoing research aims to utilize various agricultural wastes to improve biological efficiency. In this study, we investigate a sustainable strategy that promotes the conversion to a circular economy by recycling spent mushroom substrate in new mushroom crops. Our research provides insight into reducing spent mushroom sawdust waste (SMSW) of Pleurotus ostreatus for application in Volvariella volvacea cultivation. We found that the optimal conditions for cultivating V. volvacea were at a temperature of 35.0℃ a humidity of 80-85%, resulting in an average growth rate of 10.60 cm and a total weight of fruit bodies of 0.77 kg, with a biological efficiency of approximately 15.47% within 15 days of cultivation. We discovered that the SMSW could be an alternative substrate for V. volvacea cultivation. Our findings can be applied to improve V. volvacea cultivation and practical applications in agriculture. By involving innovative waste management practices, this article can foster economic growth while preserving the environment for future generations. Therefore, this study contributes to significant socio-economic development and supports green economic policies by discussing the improvements from using SMSW.

Prospects of Computer-Aided Molecular Design of Coumarins as Ecotoxicologically Safe Plant Protection Agents

Coumarins are secondary plant metabolites widely distributed in higher plants, bacteria, fungi, and sponges. This great structural diversity of these natural compounds and their synthesized derivatives enables their wide range of pharmacological activities, such as antioxidant; antibacterial; antifungal; anti-human immunodeficiency infection; anti-tubercular; and anti-cancer activities, which were very well reviewed previously. There are also many reports about their effectiveness against plant pathogenic pests (phytopathogenic fungi, bacteria, nematodes, and insects). These secondary metabolites protect environmental enemies and competing plants. However, there is still limited literature on coumarins’ practical applications in agriculture, as well as their effects on beneficial populations of soil organisms. This review summarizes recent knowledge about the effects of natural and synthesized coumarins on phytopathogens and beneficial populations of soil organisms. A separate section is dedicated to poorly reviewed computer-aided molecular design (CAMD) methods of agrochemicals. It also reviewed CAMD techniques to develop low-toxicity and environmentally safe pesticides. Despite the many positive effects of coumarins related to plant protection, they do possess properties harmful to the environment and health. These properties are described in the last section. Prediction of coumarin hazardous properties using a quantitative structure-activity relationship (QSAR) approach was also reviewed.

Specialized Metabolites Produced by Phytotopatogen Fungi to Control Weeds and Parasite Plants.

Weeds such as parasite plants are one of the most serious pests that farmers are forced to combat since the development of agriculture using different methods including mechanic and agronomy strategies. These pests have generated significant losses of agrarian and herding production, constituting a serious impediment for agricultural activities in reforestation practices and in important infrastructures. All these serious problems have induced the expansive and massive use of synthetic herbicides, which represents one of the main cause of environmental pollution, as well as serious risks for human and animal health. An alternative environmental friendly control method could be the use of bioherbicides based on suitably bioformulated natural products, of which the main ones are fungal phytotoxins. This review covers the literature from 1980 to the present (2022) and concerns fungal phytotoxins with potential herbicidal activity in order to obtain their efficacy as bioherbicides for practical application in agriculture. Furthermore, some bioherbicides based on microbial toxic metabolites are commercially available, and their application in field, mode of action and future perspectives are also discussed.

Comparisons between Plant and Animal Stem Cells Regarding Regeneration Potential and Application

Regeneration refers to the process by which organisms repair and replace lost tissues and organs. Regeneration is widespread in plants and animals; however, the regeneration capabilities of different species vary greatly. Stem cells form the basis for animal and plant regeneration. The essential developmental processes of animals and plants involve totipotent stem cells (fertilized eggs), which develop into pluripotent stem cells and unipotent stem cells. Stem cells and their metabolites are widely used in agriculture, animal husbandry, environmental protection, and regenerative medicine. In this review, we discuss the similarities and differences in animal and plant tissue regeneration, as well as the signaling pathways and key genes involved in the regulation of regeneration, to provide ideas for practical applications in agriculture and human organ regeneration and to expand the application of regeneration technology in the future.

EADD-YOLO: An efficient and accurate disease detector for apple leaf using improved lightweight YOLOv5.

Current detection methods for apple leaf diseases still suffer some challenges, such as the high number of parameters, low detection speed and poor detection performance for small dense spots, which limit the practical applications in agriculture. Therefore, an efficient and accurate model for apple leaf disease detection based on YOLOv5 is proposed and named EADD-YOLO. In the EADD-YOLO, the lightweight shufflenet inverted residual module is utilized to reconstruct the backbone network, and an efficient feature learning module designed through depthwise convolution is proposed and introduced to the neck network. The aim is to reduce the number of parameters and floating point of operations (FLOPs) during feature extraction and feature fusion, thus increasing the operational efficiency of the network with less impact on detection performance. In addition, the coordinate attention module is embedded into the critical locations of the network to select the critical spot information and suppress useless information, which is to enhance the detection accuracy of diseases with various sizes from different scenes. Furthermore, the SIoU loss replaces CIoU loss as the bounding box regression loss function to improve the accuracy of prediction box localization. The experimental results indicate that the proposed method can achieve the detection performance of 95.5% on the mean average precision and a speed of 625 frames per second (FPS) on the apple leaf disease dataset (ALDD). Compared to the latest research method on the ALDD, the detection accuracy and speed of the proposed method were improved by 12.3% and 596 FPS, respectively. In addition, the parameter quantity and FLOPs of the proposed method were much less than other relevant popular algorithms. In summary, the proposed method not only has a satisfactory detection effect, but also has fewer parameters and high calculation efficiency compared with the existing approaches. Therefore, the proposed method provides a high-performance solution for the early diagnosis of apple leaf disease and can be applied in agricultural robots. The code repository is open-sourced at https://github.com/AWANWY/EADD-YOLO.

Insights into the Methodological, Biotic and Abiotic Factors Influencing the Characterization of Xylem-Inhabiting Microbial Communities of Olive Trees.

Vascular pathogens are the causal agents of some of the most devastating plant diseases in the world, which can cause, under specific conditions, the destruction of entire crops. These plant pathogens activate a range of physiological and immune reactions in the host plant following infection, which may trigger the proliferation of a specific microbiome to combat them by, among others, inhibiting their growth and/or competing for space. Nowadays, it has been demonstrated that the plant microbiome can be modified by transplanting specific members of the microbiome, with exciting results for the control of plant diseases. However, its practical application in agriculture for the control of vascular plant pathogens is hampered by the limited knowledge of the plant endosphere, and, in particular, of the xylem niche. In this review, we present a comprehensive overview of how research on the plant microbiome has evolved during the last decades to unravel the factors and complex interactions that affect the associated microbial communities and their surrounding environment, focusing on the microbial communities inhabiting the xylem vessels of olive trees (Olea europaea subsp. europaea), the most ancient and important woody crop in the Mediterranean Basin. For that purpose, we have highlighted the role of xylem composition and its associated microorganisms in plants by describing the methodological approaches explored to study xylem microbiota, starting from the methods used to extract xylem microbial communities to their assessment by culture-dependent and next-generation sequencing approaches. Additionally, we have categorized some of the key biotic and abiotic factors, such as the host plant niche and genotype, the environment and the infection with vascular pathogens, that can be potential determinants to critically affect olive physiology and health status in a holobiont context (host and its associated organisms). Finally, we have outlined future directions and challenges for xylem microbiome studies based on the recent advances in molecular biology, focusing on metagenomics and culturomics, and bioinformatics network analysis. A better understanding of the xylem olive microbiome will contribute to facilitate the exploration and selection of specific keystone microorganisms that can live in close association with olives under a range of environmental/agronomic conditions. These microorganisms could be ideal targets for the design of microbial consortia that can be applied by endotherapy treatments to prevent or control diseases caused by vascular pathogens or modify the physiology and growth of olive trees.

Study on plasma‐modified corn stover‐humic acid‐based superabsorbent resin

AbstractThis research aimed to prepare superabsorbent resin from agro‐mining waste, which is important for sustainable development and practical application in agriculture and forestry. Corn stover (CS), humic acid (HA), acrylic acid (AA), and acrylamide (AM) were used as raw materials to produce CS–HA‐based superabsorbent resin (p‐CS‐HA‐PAA) by plasma modification, and the optimum modification conditions were obtained. The superabsorbent resin was characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, and contact angle analysis. The adsorption characteristics of the superabsorbent resin were analyzed by kinetic model fitting, and the performances were tested. The results showed that the water absorption capacity could reach 1106.9 g/g under the following optimal process conditions: a discharge time of 180 s, a discharge power of 40 W, and a discharge pressure of 20 Pa. The characterization confirmed that plasma modification could make the surface of the superabsorbent resin rougher, produce more and larger pores, and introduce more hydrophilic groups. The p‐CS‐HA‐PAA conformed to the pseudo‐second‐order kinetic equation and non‐Fickian intradiffusion process in aqueous solution. The chemical resistance, repeated liquid absorption, and water retention properties of the modified resin were better than those of the unmodified resin, which provided a viable way for industrial and agricultural applications.

Allelopathic potential and competitive traits of sweetpotato cultivars

Allelopathy and competition are components of plant-plant interactions, delimiting the level of interference. Understanding this interaction has practical applications in agriculture. Crop cultivars possessing high allelopathic ability and competitive traits are themselves tools for sustainable weed management, enabling reduced use of herbicides. Greenhouse and field experiments were conducted to assess the weed suppressive ability of selected sweetpotato (Ipomoea batatas L.) cultivars. The effect of nine cultivars on Palmer amaranth (Amaranthus palmeri S. Watson), junglerice (Echinochloa colona L.), and hemp sesbania (Sesbania hederacea P. Mill.) was first evaluated in the greenhouse. The experiment was set up in a completely randomized design with four replications and conducted twice. Sweetpotatoes were cultured in sand. The target weeds were seeded in pots filled with a 2:1 mix of field soil:potting mix and watered with 100-ml aliquot of sweetpotato root leachates once every 2 d. Weed height and shoot biomass were measured. ‘Heartogold’, ‘Centennial’, and ‘Stokes Purple’ were the most allelopathic cultivars. Junglerice was most inhibited by sweetpotato leachates. Nine cultivars were evaluated in the field. Experiments were conducted at Fayetteville and Kibler, Arkansas, USA, in a split-plot design, with weed infestation (broadleaf spp., grass spp., or weed-free) as whole plot and the cultivars as split-plot. Across locations, ‘Beauregard-14’ had the longest vines, whereas ‘Hatteras’ and ‘Heartogold’ had the tallest canopy. ‘Heartogold’ had the largest leaf area. This cultivar reduced weed biomass 2- to 4-fold in both locations. Yield was reduced on average 55 and 68% with grass and broadleaf weeds across locations, respectively. ‘Beauregard-14’ and ‘Bayou Belle-6’were the high-yielding cultivars in Kibler and Fayetteville. The highest yielding cultivars were not the most weed suppressive but did not incur the highest yield loss from weed competition, indicating the ability to withstand weed interference. Cultivar competitiveness, weed-suppressive ability, and weed tolerance are important factors that should be considered before making a cultivar selection.

Emerging Research Topics in the Vibrionaceae and the Squid-Vibrio Symbiosis.

The Vibrionaceae encompasses a cosmopolitan group that is mostly aquatic and possesses tremendous metabolic and genetic diversity. Given the importance of this taxon, it deserves continued and deeper research in a multitude of areas. This review outlines emerging topics of interest within the Vibrionaceae. Moreover, previously understudied research areas are highlighted that merit further exploration, including affiliations with marine plants (seagrasses), microbial predators, intracellular niches, and resistance to heavy metal toxicity. Agarases, phototrophy, phage shock protein response, and microbial experimental evolution are also fields discussed. The squid–Vibrio symbiosis is a stellar model system, which can be a useful guiding light on deeper expeditions and voyages traversing these “seas of interest”. Where appropriate, the squid–Vibrio mutualism is mentioned in how it has or could facilitate the illumination of these various subjects. Additional research is warranted on the topics specified herein, since they have critical relevance for biomedical science, pharmaceuticals, and health care. There are also practical applications in agriculture, zymology, food science, and culinary use. The tractability of microbial experimental evolution is explained. Examples are given of how microbial selection studies can be used to examine the roles of chance, contingency, and determinism (natural selection) in shaping Earth’s natural history.