Plants For Treatment Of Diseases Research Articles

Application analysis of monoclonal antibodies in plant

Abstract. monoclonal antibody is a highly homogeneous antibody produced by the cloning of a single B lymphocyte and directed only against a specific epitope. With the deepening of research on mAb, it can not only be used to treat human diseases, but also be used for the diagnosis of plant bacteria and viruses. In this paper, some applications of monoclonal antibodies in plants were reviewed, and it was found that the recent application direction was mainly focused on plant quarantine and traditional Chinese medicine (TCM) toxicity detection, and it was gradually applied to the research field of TCM, providing a new means for the quality control of TCM. Although McAb has shown good application prospect in many aspects, its development process is complex and there are large differences in the recognition specificity for different strains. Future research can pay more attention to the diagnosis and treatment of plant diseases to provide reference, convenient to speed up the quarantine to reduce the severity of the epidemic; It also provides reference for the research and quality control of toxic TCM containing toxic proteins.

Advanced Deep Learning and Nonlinear Mathematical Analysis for Precision Detection and Targeted Treatment of Plant Diseases

Plant diseases are a big problem in agriculture because they lower yields around the world. Regular ways of finding and treating diseases usually require a lot of work, take a long time, and aren't always accurate. For these problems, this study shows a combined method using advanced deep learning methods like convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and MobileNet, along with nonlinear mathematical analysis to improve the accuracy of finding plant diseases and making the best targeted treatment plans. Image-based diagnosis of plant diseases using deep learning methods, especially CNNs, is a quick and accurate method. They learn to spot tiny visual clues that point to different diseases by looking at large collections of images of healthy and sick plants. Addition of LSTMs and MobileNet with optimization improves the model's ability to handle time cycles and complex patterns, making it more reliable and usable for a wider range of plant types and weather conditions. The deep learning models are also fine-tuned using an optimization method, like the Adam optimizer, which ensures the best performance and faster resolution. Additionally, complex mathematical models are created to show how plant diseases spread and get worse in crop fields. Based on things like disease transfer rates, weather conditions, and plant chemistry, these models can predict how diseases will spread and help with treatment plans. This system gives dynamic, site-specific advice on how to control diseases by combining these models with real-time data from field devices and remote sensing technologies. Testing the suggested method's effectiveness in the field on many different crops shows that it works much better than traditional methods at both finding problems and treating them.

Ultralong‐lasting plasma‐activated water inhibits pathogens and improves plant disease resistance in soft rot‐infected hydroponic lettuce

AbstractTo address the short lifetime of plasma‐activated water (PAW), this work reports on the application of ultralong‐lasting PAW for plant disease treatment. Compared with lettuce without ultralong‐lasting PAW treatment, the treated lettuce solution showed c. 1 log10 colony forming units mL−1 decrease in pathogens, as well as improvements in leaf area, leaf weight, root length, and root weight by 75%, 20%, 108.33%, and 150%, respectively. The leaf withering/root mortality rate of PAW‐treated plants was significantly lower than those of non‐PAW‐treated plants. Besides, disease defense‐related enzymes, such as catalase and β−1,3‐glucanase, were respectively enhanced by 112.5% and 5.13% in PAW‐treated lettuce, while the plant defense‐related genes were upregulated to respond to the pathogen's invasion.

Detecting Three Different Diseases of Plants by Using CNN Model and Image Processing

Agricultural output is critical to Pakistan's economy, and plant disease detection is critical for both the environment and human health. This work presents a CNN-based approach for detecting plant diseases, which is evaluated on sample photos to assess the temporal complexity and infected area. The model was given three disease cases: corn common rust, tomato bacterial spot, and potato early blight. Using the CNN algorithm, the CNN model attained an accuracy of 95.55% for tomato bacterial spot, 96.72% for Corn Common Rust, and 97.63% for Potato Early Blight. This method may aid in the diagnosis and treatment of plant diseases.

The Precious Potential of the Sacred Tree Chamaecyparis obtusa (Siebold & Zucc.) Endl. as a Source of Secondary Metabolites with Broad Biological Applications.

Chamaecyparis obtusa (Siebold & Zucc.) Endl., which belongs to the Cupressaceae family, occurs naturally in North America and Asia, especially in Korea, Taiwan and Japan, where it is an evergreen, coniferous, sacred, ethnic tree. It has many useful varieties that are widespread throughout the world and grown for decorative purposes. It is most commonly used as an ornamental plant in homes, gardens or parks. It is also widely used in many areas of the economy; for example, its wood is used in architecture as well as furniture production. In addition, oil extracted from Chamaecyparis obtusa is increasingly used in cosmetology for skin care. Due to its wide economic demand, mainly in Japan, it represents the largest area of plantation forest. Despite this, it is on the red list of endangered species. Its use in ethnopharmacology has led to more and more research in recent years in an attempt to elucidate the potential mechanisms of its various biological activities, such as antimicrobial, antioxidant, anticancer, antidiabetic, antiasthmatic, anti-inflammatory, antiallergic, analgesic and central nervous system effects. It has also been shown that Chamaecyparis obtusa can be used as an insect repellent and an ingredient in plant disease treatment. This thesis provides a comprehensive review of the biological studies to date, looking at different areas of the economic fields of potential use of Chamaecyparis obtusa.

Porous silicon nanostructures: Synthesis, characterization, and their antifungal activity

Abstract The use of synthetic pesticides has come under scrutiny, and there has been a subsequent shift toward the investigation of alternative methods for the treatment of plant diseases. One notable advancement in this field is the utilization of porous silicon (PS) powder as a sustainable antifungal agent. The synthesis of PS nanoparticle (PS-NP) powder was carried out using the environmentally friendly ultrasonication process. X-ray powder diffraction, Fourier transform infrared spectroscopy, Raman spectroscopy, UV-VIS absorbance, and photoluminescence were some of the methods used to characterize PS-NPs. The different characterization methods revealed the formation of a nanocrystalline structure possessing a cubic Si crystalline quality. The crystal size of PS-NPs, as determined from X-ray diffractometer data, ranges from 36.67 to 52.33 nm. The obtained PS has a high band gap of 3.85 eV and presents a photoluminescence peak at 703 nm. The antifungal activity of the synthesized PS-NPs was assessed against three molecularly characterized fungi, namely Rhizoctonia solani, Fusarium oxysporum, and Botrytis cinerea, which were obtained from tomato plants. The concentration of PS-NPs at 75 µg/mL exhibited the highest enhancement in growth inhibition percentages as compared to the control group. R. solani had the highest inhibition percentage of 82.96%. In conclusion, the encouraging structural properties and antimicrobial capabilities of PS-NPs pave the way for their application across diverse technological industries. To the best of our knowledge, this is the first in vitro study of PS-NPs to evaluate their fungal control efficiency.

Enhanced white rot control in garlic bulbil using organic-inorganic hybrid materials as coating membranes.

Organic-inorganic hybrid materials have a range of applications due to their unique properties. Their application in agriculture brings alternatives for the controlled release of nutrients in the soil, the seed coating, the transport of herbicides, and the treatment of plant diseases. The present study aimed to investigate the use of fungicides incorporated into hybrid membranes formed by synthetic hectorite (LAPONITE®) and polymers in the pre-treatment of garlic bulbils exposed to the pathogen Stromatinia cepivora, which causes white rot. The coatings were selected by a germination test, based on the bulbil sprouting index, and by a mycelial growth inhibition test, based on the percentage of mycelial growth inhibition. The chosen membranes were used to coat the bulbils for bioassays conducted in a biochemical oxygen demand incubator at 17 °C. The coated bulbils were planted in soil samples containing three different densities of Stromatinia cepivora: 0.1 g, 1.0 g, and 10 g of sclerotium per L of soil. Membranes containing 2% carboxymethyl cellulose and 2% LAPONITE® incorporated with (i) the fungicide tebuconazole (36 mg L-1) and (ii) the combination of the actives tebuconazole (36 mg L-1) and triadimenol (62 mg L-1) showed the total rate of sprouting and null indices of incidence of symptoms and mortality in its repetitions. The hybrid membranes were characterized employing several techniques, including X-ray diffraction, infrared and Raman spectroscopy, thermogravimetric analysis and differential scanning calorimetry coupled to mass spectrometry, and optical microscopy. Characterization data confirmed the presence of fungicides incorporated into the membranes. Some concentrations of fungicides were low enough not to be detected in all analyses performed, although they guaranteed a protective character to the bulbils about the fungus S. cepivora present in the soil, with a possibility of antifungal pre-treatment with a potential reduction in the concentration used.

Application of Case Based Reasoning Method to Diagnose Rice Plant Diseases

Rice (Latin: Oryza sativa) is one of the most important cultivated crops in civilization. Although it mainly refers to a type of cultivated plant, rice is also used to refer to several types of the same genus (genus), commonly referred to as wild rice. The problem that often arises is that many rice plants are susceptible to pests and diseases during the planting period. Some pests and diseases that can attack rice plants include: leaf blight, grass, tongguo, rice spout, and dwarf grass. Generally, when rice plants are attacked by pests and diseases, farmers will immediately use pesticides or treatment methods that are sometimes not in accordance with pests. As a result, treatment is not optimal and can even cause new pests and diseases. The purpose of this study is to assist farmers in identifying early symptoms of plant diseases and pests of rice plant diseases using the case base reasoning method, so that the treatment of plant diseases and insect pests is more concentrated and maximal.

Plant disease prescription recommendation based on electronic medical records and sentence embedding retrieval

BackgroundIn the era of Agri 4.0 and the popularity of Plantwise systems, the availability of Plant Electronic Medical Records has provided opportunities to extract valuable disease information and treatment knowledge. However, developing an effective prescription recommendation method based on these records presents unique challenges, such as inadequate labeling data, lack of structural and linguistic specifications, incorporation of new prescriptions, and consideration of multiple factors in practical situations.ResultsThis study proposes a plant disease prescription recommendation method called PRSER, which is based on sentence embedding retrieval. The semantic matching model is created using a pre-trained language model and a sentence embedding method with contrast learning ideas, and the constructed prescription reference database is retrieved for optimal prescription recommendations. A multi-vegetable disease dataset and a multi-fruit disease dataset are constructed to compare three pre-trained language models, four pooling types, and two loss functions. The PRSER model achieves the best semantic matching performance by combining MacBERT, CoSENT, and CLS pooling, resulting in a Pearson coefficient of 86.34% and a Spearman coefficient of 77.67%. The prescription recommendation capability of the model is also verified. PRSER performs well in closed-set testing with Top-1/Top-3/Top-5 accuracy of 88.20%/96.07%/97.70%; and slightly worse in open-set testing with Top-1/Top-3/Top-5 accuracy of 82.04%/91.50%/94.90%. Finally, a plant disease prescription recommendation system for mobile terminals is constructed and its generalization ability with incomplete inputs is verified. When only symptom information is available without environment and plant information, our model shows slightly lower accuracy with Top-1/Top-3/Top-5 accuracy of 75.24%/88.35%/91.99% in closed-set testing and Top-1/Top-3/Top-5 accuracy of 75.08%/87.54%/89.84% in open-set testing.ConclusionsThe experiments validate the effectiveness and generalization ability of the proposed approach for recommending plant disease prescriptions. This research has significant potential to facilitate the implementation of artificial intelligence in plant disease treatment, addressing the needs of farmers and advancing scientific plant disease management.

Potential and Prospects of Trichoderma in Plant Protection

In order to feed the growing population, agriculture is a vital component of any country; however, pests pose a constant threat to it. Chemical pesticides are employed to safeguard the crops from the enormous yield loss. These chemical pesticides are boundless in killing crop pests; however, they have detrimental effects on the health of both humans and the environment. Therefore, biological control techniques are being utilised after identifying an environmentally suitable substitute. Due to its well-known biological control mechanism, Trichoderma spp. have been utilised extensively in agricultural applications. The host plant’s soil and rhizosphere serve as shelter place for Trichoderma. It has the ability to create a variety of secondary metabolites and secretion of important enzymes. Clarification of Trichoderma’s importance in the prevention and treatment of plant diseases is thus important in order to advance sustainable agriculture. The mechanisms, which include mycoparasitism, antibiosis and competition aid in the management of insect pests and plant pathogens in the soil, seeds, roots, stems, leaves, etc. It is crucial to create new formulations of biocontrol microorganisms with a greater level of stability and survivability in order to implement biocontrol technology in the field and enhance its commercialisation.

Identification of Plant Leaf Disease Using a Novel Convolutional Neural Network

A critical element in preventing a major outbreak is the detection of plant leaves. An important research issue is the automatic detection of plant diseases. For both human life and condition, a plant's dedication is essential. Like humans and other animals, plants do suffer the negative impacts of illnesses. A plant's normal development is influenced by the frequency of plant diseases that occur. The entire plant, including the leaf, stem, organic material, root, and flower, is affected by these diseases. Most of the time, if a plant's ailment is not treated, it dies or may cause leaves, blooms, organic products, and so forth to fall off. For accurate identification and treatment of plant diseases, appropriate determination of these disorders is necessary. Plant pathology is the study of plant infections, their causes, and methods for preventing, managing, and eradicating them. However, the current approach includes human inclusion for structure and identifying disease proof. This tactic is time-consuming and expensive. Instead of using the current method, a programmed division of diseases from plant leaf images utilising a delicate registration methodology may be more beneficial. In this study, we describe a method for identifying and characterising plant leaf diseases naturally called Bacterial Searching Improvement Based Radial Basis Function Neural Network (BRBFNN). We use bacterial search streamlining (BFO), which increases the speed and accuracy of the system to recognise and organise the regions contaminated by diverse illnesses on the plant leaves, to assign Radial Basis Function Neural Network (RBFNN) the proper weight. The location development calculation increases the system's efficiency by searching for and gathering seed focuses on typical traits for the highlighted extraction operation. To make progress against parasite diseases including early curse, leaf twist, leaf spot, late scourge, and basic, cedar apple, and leaf rust. The suggested approach achieves more accuracy in identifying evidence and characterizing infections.

Silver Nanochito Oligomer Hybrid Solution for the Treatment of Citrus Greening Disease (CGD) and Biostimulants in Citrus Horticulture

Citrus greening disease (CGD), or Huanglongbing (HLB), is principal in Citrus farming because of its severe damage, decreased yield, poor quality of fruit, and frequent disappearance before harvest. Present research blended silver nanoparticles (AgNPs) colloid with chitooligomers and monomers (COAMs) to create the composites referred to as a “hybrid solution”. The hybrid solution has been synthesized for antimicrobial effects and plant growth stimulation. The hybrid solution was applied to cure the CGD, which was then tested using the results of the iodine test kit. Study results revealed that the mechanism for antibacterial properties is efficient and leads to cell death by silver ions (positive charge) attached to the cell wall of pathogens (negative charge). COAMs increase AgNPs’ ability to adhere to negatively charged bacterial cytoplasmic membranes and amplify their electrostatic interaction. There are synergistic antibacterial effects of AgNPs–COAMs. The findings reveal a notable increase in the shoot length of leaves which may be the reason for the growth stimulation function of the hybrid solution due to the properties of COAMs as a biostimulant. The hybrid solution-treated trees yielded around 104.50 ± 4.56 kg of fruits by the trunk or foliar application. Citrus fruits did not show the presence of any silver residues, as displayed by the results of the accumulation test. Compared to conventional ampicillin, the hybrid treatment of CGD was significantly more cost-effective, increased productivity, and had minor accumulation. The acquired results may also be applied to further plant disease treatment.

Developmental Neurotoxicity of Difenoconazole in Zebrafish Embryos.

Difenoconazole is a type of triazole fungicide that is widely used in the treatment of plant diseases. Triazole fungicides have been shown in several studies to impair the development of the nervous system in zebrafish embryos. There is still little known about difenoconazole-induced neurotoxicity in fish. In this study, zebrafish embryos were exposed to 0.25, 0.5, and 1 mg/L of difenoconazole solution until 120 h post-fertilization (hpf). The difenoconazole-exposed groups showed concentration-dependent inhibitory tendencies in heart rate and body length. Malformation rate and spontaneous movement of zebrafish embryos increased, and the locomotor activity decreased in the highest exposure group. The content of dopamine and acetylcholine was reduced significantly in difenoconazole treatment groups. The activity of acetylcholinesterase (AChE) was also increased after treatment with difenoconazole. Furthermore, the expression of genes involved in neurodevelopment was remarkably altered, which corresponded with the alterations of neurotransmitter content and AChE activity. These results indicated that difenoconazole might affect the development of the nervous system through influencing neurotransmitter levels, enzyme activity, and the expression of neural-related genes, ultimately leading to abnormal locomotor activity in the early stages of zebrafish.

The medicinal plants for Covid-19 treatments

It’s well know that the use of medicinal plants for treatment of diseases are tradicional practice and sterted at the beginning of medicine and the knowledge the went through generation to generation. Goal: Analyze studies about the use of medicinal palnts to treatment and prevantion for COVID-19 inside the national and internacional scientific journals in the last three years. Method: It’s an integrative literature review which analyzed the quantitatives data. The Virtual Library Health was used for research during the months of Octuber to November. Results:About the mean question was found just fews studies that could answer this research goals, 8 (0,65%) researchs show that the use of medicinal palnts need be more explore for achieved a better understament. Coclusion: the data collect show that some plants like Turmeric working a positive way for treating symptoms of COVID-19, also was found another kinds of plants, we have the exemplo of the maoto as a pre-exposure treatment measure for the virus. So medicinal plants have the potential to be used as a treatment for COVID-19 but we need more research about the properties of these plants.

Bacteria-targeting photoactivated antibacterial nanosystem based on oligoalginate-protoporphyrin IX for plant disease treatment

To enhance bacteria-targeting and improve bactericidal activity, a novel GSH-responsive photodynamic antimicrobial nanosystem based on Alginate oligosaccharide-protoporphyrin IX (AOS-ss-PpIX) conjugates were constructed. The fluorescence self-quenching caused by the aggregation of PpIX groups into hydrophobic core could promote the photostability of PpIX and avoid the phototoxicity of the non-infected sites of the target plants. But once exposed to GSH stimulus, the recovery of high photodynamic activity were observed due to the destruction of the system and accompanied by the release of thiolated PpIX, and the photodynamic activity after 2 h was higher than that of PpIX under the same conditions. Furthermore the system showed more efficient photodynamic antibacterial capacity under light exposure. Moreover, the pot experiment also verified that the system showed outstanding control efficacy against cabbage soft rot caused by Erwinia carotovora Sclerotinia. Safety test exhibited that the size of nanoparticles can restrict the entry of nanoparticles into the plant cells to trigger photocytotoxicity. The design strategy provides a platform for promoting the utilization of photoactivated pesticides and reducing or avoiding their harm to target plants and the environment.

A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images.

The agricultural crop productivity can be affected and reduced due to many factors such as weeds, pests, and diseases. Traditional methods that are based on terrestrial engines, devices, and farmers’ naked eyes are facing many limitations in terms of accuracy and the required time to cover large fields. Currently, precision agriculture that is based on the use of deep learning algorithms and Unmanned Aerial Vehicles (UAVs) provides an effective solution to achieve agriculture applications, including plant disease identification and treatment. In the last few years, plant disease monitoring using UAV platforms is one of the most important agriculture applications that have gained increasing interest by researchers. Accurate detection and treatment of plant diseases at early stages is crucial to improving agricultural production. To this end, in this review, we analyze the recent advances in the use of computer vision techniques that are based on deep learning algorithms and UAV technologies to identify and treat crop diseases.

Pleiotropic Effects of Icariside II on the Cardiovascular System: Novel Applications of Ethnopharmacology in Targeting Vascular Remodeling.

Pleiotropic Effects of Icariside II on the Cardiovascular System: Novel Applications of Ethnopharmacology in Targeting Vascular Remodeling.

Early-Stage Brown Spot Disease Recognition in Paddy Using Image Processing and Deep Learning Techniques

India is an agricultural country. Paddy is the main crop here on which the livelihood of millions of people depends. Brown spot disease caused by fungus is the most predominant infection that appears as oval and round lesions on the paddy leaves. If not addressed on time, it might result in serious crop loss. Pesticide use for plant disease treatment should be limited because it raises costs and pollutes the environment. Usage of pesticide and crop loss both can be minimized if we recognize the disease in a timely manner. Our aim is to develop a simple, fast, and effective deep learning structure for early-stage brown spot disease detection by utilizing infection severity estimation using image processing techniques. The suggested approach consists of two phases. In the first phase, the brown spot infected leaf image dataset is partitioned into two sets named as early-stage brown spot and developed stage brown spot. This partition is done on the basis of calculated infection severity. Infection severity is computed as a ratio of infected pixel count to total leaf pixel count. Total leaf pixel counts are determined by segmenting the leaf region from the background image using Otsu's thresholding technique. Infected pixel counts are determined by segmenting infected regions from leaf regions using Triangle thresholding segmentation. In the second phase, a fully connected CNN architecture is built for automatic feature extraction and classification. The CNN-based classification model is trained and validated using early-stage brown spot, developed stage brown spot, and healthy leaves images of rice plants. Early-stage brown spot and developed stage brown spot images used in training and validation are the same images that are obtained in phase 1. The experimental analysis shows that the proposed fully connected CNN-based early-stage brown spot disease recognition model is an effective approach. The classification accuracy of the suggested model is found to be 99.20%. The result of the suggested method is compared with those existing CNN-based disease recognition and classification methods that have used leaf images to recognize the diseases. It is observed that the performance of our method is significantly better than compared methods.

Analysis of flora of the medicinal plants of the Atyrau region

Medicinal plants are important sources of biologically active substances and phytopreparations for the prevention and treatment of diseases. In Kazakhstan, the regions of the Western Kazakhstan remain poorly studied in relation to wild medicinal plants. The purpose of the present study was to analyze the species composition of medicinal plants of the flora of the Atyrau region and their ranking by life forms, ecological groups, the degree of distribution and possibility of application in medical practice. Based on the analysis of literary sources and the results of own field studies in the Atyrau region, 177 species of medicinal plants belonging to 118 genera and 46 families are identified. The largest number of species of medicinal plants is noted in Asteraceae, Lamiaceae, Brassicaceae, Rosaceae, Chenopodiaceae, Fabaceae and Polygonaceae families. According to the degree of distribution, 4 groups of plants are identified: i) widespread plants, but growing sporadically, not forming thickets (128 species); ii) rare and endangered plants, not to be harvested for raw materials (11 species); iii) plants, forming small thickets (23 taxa); iv) plants, forming significant thickets suitable for industrial harvesting (15 taxa). Among the medicinal plants of the Atyrau region, 6 ecological groups were identified in relation to humidification conditions: hydrophytes, hygrophytes, mesophytes, mesoxerophytes, xerosesophytes and xerophytes. 8 groups of life forms are defined. The largest number of species is concentrated in the group of perennial herbaceous plants — 105 taxa. 128 taxa are allocated for use in folk medicine, 49 species — in official medicine. 12 pharmaceutical-therapeutic groups of plants for treatment of diseases of various etiologies are identified.

Cytogenetic and genotoxic assessment in Allium cepa exposed to imazalil fungicide.

Imazalil (IMZ), a fungicide containing imidazole group, is extensively used for the prevention and treatment of fungal diseases in plants. Current study was performed to examine cyto-genotoxic potential of IMZ on Allium cepa roots by following Allium ana-telophase and single cell gel electrophoresis (comet) assays. The concentration which reduced the growth of the root tips of IMZ by 50% compared to the negative control group (EC50) was found to be 1μg/mL by Allium root growth inhibition test. 0.5, 1, and 2μg/mL concentrations of IMZ were exposed to Allium roots for intervals of 24, 48, 72, and 96h. 10 μg/mL of methyl methane sulfonate (MMS) and distilled water were used as control groups, both positive and negative. Statistical analysis was performed by using one-way ANOVA with Duncan's multiple comparison tests at p ≤ 0.05 and Pearson correlation test at p = 0.01. IMZ showed cytotoxic effect by statistically decreasing root growth and mitotic index (MI) and also genotoxic effect by statistically increasing chromosomal aberrations (CAs) and DNA damage compared to the negative control group. With these cyto-genotoxic effects, it should be used carefully and further cyto-genotoxic mechanisms should be investigated along with other toxicity tests.