Investigation and Analysis of Main Diseases of Landscape Plants in the Main Urban Area of Lu'an City

To figure out the disease occurrence of landscape plants in the main urban area of Lu'an City,the author investigated the disease occurrence of landscape plants in park green space,residential green space,unit attached green space and main road in the area under administration.The survey results showed that there were 29 species of urban landscape plant diseases,mainly powdery mildew and spot diseases.According to the characteristics of the diseases,the causes and problems of the diseases were analyzed,and the corresponding prevention and control measures were put forward.

Design and Research on Identification of Typical Tea Plant Diseases Using Small Sample Learning

Tea plants are susceptible to diseases during their growth.These diseases seriously affect the yield and quality of tea.The effective prevention and control of diseases requires accurate identification of diseases.With the development of artificial intelligence and computer vision,automatic recognition of plant diseases using image features has become feasible.As the support vector machine(SVM)is suitable for high dimension,high noise,and small sample learning,this paper uses the support vector machine learning method to realize the segmentation of disease spots of diseased tea plants.An improved Conditional Deep Convolutional Generation Adversarial Network with Gradient Penalty(C-DCGAN-GP)was used to expand the segmentation of tea plant spots.Finally,the Visual Geometry Group 16(VGG16)deep learning classification network was trained by the expanded tea lesion images to realize tea disease recognition.

Assessing the Efficacy of Lemongrass (Cymbopogon citratus) and Sambong (Blumea balsamifera) Extracts in Combating Black Pod Disease: Sustainable Solutions for Controlling Phytophthora megakarya in Cameroon’s Cocoa Plantations

The use of plant extracts as antifungal agents is gaining increasing attention, particularly for the control of black pod disease in cocoa. Despite extensive research, current strategies haven’t been entirely effective. This study evaluated the effectiveness of Cymbopogon citratus and Blumea balsamifora leaf extracts, both individually and in combination, against Phytophthora megakarya. We assessed the efficacy of the most promising combination (75% B. balsamifera, 25% C. citratus) after storage at room temperature for up to 9 days. Agar microdilution and in vivo bioassays were conducted to determine antifungal susceptibility and effectiveness. Blumea extract exhibited the highest overall inhibitory activity, with the lowest minimum inhibitory concentration (117 µl mL−1) while C. citratus had a narrower range of MIC (146 to 233 µl mL−1). The combination of C. citratus and B. balsamifera demonstrated a synergistic effect against P. megakarya, achieving growth inhibition on V8 media (92.72 ± 4.20% to 100%) and on artificially infected detached pod cortex (92.24 ± 4.53% to 98.75 ± 1.25%), which was not significantly different from the positive control (Ridomil). Furthermore, this combination maintained its effectiveness for up to 9 days at room temperature. These findings suggest that combining plant extracts can enhance their antifungal properties.

Potential efficacy and mechanism of medicinal plants on chronic kidney disease-associated vascular calcification:a review

Vascular calcification is a crucial risk factor that affects the incidence and mortality of cardiovascular disease in chronic kidney disease patients.Modern medicine relies on calcium-phosphorus binding agents,calcium mimetics,active vitamin D,and hemodialysis to prevent and treat vascular calcification,however,their efficacy is unsatisfactory and adverse reactions often occur.Medical plant therapy can act as an integrative regulator in patients with chronic kidney disease-associated vascular calcification,which can significantly improve patients’symptoms,but its specific mechanism has not been fully elucidated yet.In this paper,we reviewed the domestic and international theoretical studies on the pathogenesis mechanism of chronic kidney disease-associated vascular calcification in recent years,summarized eight active ingredients of medicinal plants as well as four compound formulas for improving chronic kidney disease-associated vascular calcification,and explored the mechanism of action of herbal medicine,which will provide a new strategy for promoting the prevention and treatment of vascular calcification.

Towards Intelligent Detection and Classification of Rice Plant Diseases Based on Leaf Image Dataset

The detection of rice leaf disease is significant because,as an agricultural and rice exporter country,Pakistan needs to advance in production and lower the risk of diseases.In this rapid globalization era,information technology has increased.A sensing system is mandatory to detect rice diseases using Artificial Intelligence(AI).It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of diseases.Deep Neural Network(DNN)is a novel technique that will help detect disease present on a rice leave because DNN is also considered a state-of-the-art solution in image detection using sensing nodes.Further in this paper,the adoption of the mixed-method approach Deep Convolutional Neural Network(Deep CNN)has assisted the research in increasing the effectiveness of the proposed method.Deep CNN is used for image recognition and is a class of deep-learning neural networks.CNN is popular and mostly used in the field of image recognition.A dataset of images with three main leaf diseases is selected for training and testing the proposed model.After the image acquisition and preprocessing process,the Deep CNN model was trained to detect and classify three rice diseases(Brown spot,bacterial blight,and blast disease).The proposed model achieved 98.3%accuracy in comparison with similar state-of-the-art techniques.

Fermentation, formulation and evaluation of PGPR Bacillus subtilis isolate as a bioagent for reducing occurrence of peanut soil-borne diseases

Four isolates of Bacillus subtilis coded,B4,B7,B8 and B10 were examined as biocontrol agents for their abilities and antagonistic effect on the in vitro growth of certain phytopathogenic fungi of peanut,Rhizoctonia solani and Sclerotium rolfsii.Bacillus subtilis isolate B4(GenBank accession no.EF150884)was the highly effective one for inhibiting the fungal mycelial growth.Batch fermentation of B.subtilis isolate B4 was carried out and the maximum biomass achieved was 4.53 g L-1 at 11 h.Bacillus subtilis isolate B4 was formulated and evaluated as a biofungicide to reduce peanut soil-borne diseases under greenhouse and field conditions at the side of Rizolex-T(fungicide)as standard.Treatments by formulated plant growth-promoting rhizobacteria(PGPR)B.subtilis B4 and Rizolex-T in a soil infested with R.solani,S.rolfsii and mixture of them were more effective in decreasing percentage of damping-off,root and pod rot disease incidence(%)in greenhouse and open field environment during the two seasons 2015 and 2016.Treatments by PGPR gave highly dry weight and number of healthy pods compared to control of fungi treatment which was nearby to dry weights of healthy pods achieved by treatments by Rizolex-T in a soil infested with S.rolfsii,R.solani and mixture of them.Formulated PGPR B.subtilis B4 gave higher increasing of yield percentage than treatment by Rizolex-T in the two evaluated seasons 2015 and 2016.It can conclude that the produced bioforumlated agent was more efficient as fungicide when compared with the other chemical synthesized fungicides,safe for human and the environment and economy.

Prevention and Control Technology of Potato Soil-borne Diseases

In recent years,potato soil-borne diseases have occurred severely.The investigation shows that potato Fusarium wilt greatly affects potato yield,leading to a yield reduction rate of 21.8%.Potato powdery scab shows very mild symptoms on potato tuber,basically with no symptoms in some plots,but shows obvious symptoms on the root system.A large number of nodules which are produced on one side of the root system seriously affect water and fertilizer absorption function of the potato root system.Potato tubers expand slowly,leading to small potato tuber,low yield and low commodity rate.The fungus can survive in soil for more than 10 years.Potato soil-borne diseases are harmful and are difficult to control.Susceptible plants can be detected by high definition chromatographic control method and pathogen detection.For prevention and control of potato soil-borne diseases,first of all,disease-resistant varieties should be chosen and virus-free potato seeds are used for sowing.Secondly,metham and dazomet can be used to treat soil in order to prevent and control potato soil-borne diseases.The results showed that the dead seedling rate treated by metham was reduced from 35%to 12.5%compared with the blank control,and the yield was increased by 44.09 kg/ha,with an increase rate of 18.2%.Compared with the blank control,the dead seedling rate treated by dazomet was decreased from 35%to 7.5%,and the yield was increased by 38.10 kg/ha,with an increase rate of 15.7%.The soil treatment received obvious yield increase effect.

Screening of Two Biocontrol Strains of Bacillus subtilis against Ginseng Soil-borne Disease and Their Antifungal Activities

[ Objective] The paper was to obtain biocontrol strains with good control effects against ginseng soil-borne disease through screening. [ Method] Dilu- tion plate method and plate confrontation culture method were used to isolate and screen biocontrol bacteria from the rhizosphere soil of diseased ginseng. The strains were identified through morphology, physiological and biochemical characteristics and 16S rDNA. [ Result ] With Rhizoctonia solani, Fusarium oxysporum and Fu- sarium solani as the indicator strains, two biocontrol strains B59 and X1 with strong antagonistic effects were screened from the rhizosphere soil of diseased ginseng in Tieli farm of Heilongjiang Province, and they were identified to be Bacillus subtilis. The inhibition rates of two biocontrol strains against eight different fungi were all greater than 90%. The primary study indicated that B59 and X1 strains could secrete antifungal active substances. [ Conclusion] Two biocontrol Bacillus subti- lis strains 1359 and X1 all had strong antagonistic effect against ginseng soil-borne disease, which had certain potential for development and utilization.

Construction of Early-warning Model for Plant Diseases and Pests Based on Improved Neural Network

By studying principles and methods related to early-warning model of plant diseases and using PSO method, parameter optimization was conducted to backward propagation neural network, and a pre-warning model for plant diseases based on particle swarm and neural network algorithm was established. The test results showed that the construction of early-warning model is effective and feasible, which will provide a via- ble model structure to establish the effective early-warning platform.

Enhanced Disease Identification Model for Tea Plant Using Deep Learning

Tea plant cultivation plays a significant role in the Indian economy.The Tea board of India supports tea farmers to increase tea production by preventing various diseases in Tea Plant.Various climatic factors and other parameters cause these diseases.In this paper,the image retrieval model is developed to identify whether the given input tea leaf image has a disease or is healthy.Automation in image retrieval is a hot topic in the industry as it doesn’t require any form of metadata related to the images for storing or retrieval.Deep Hashing with Integrated Autoencoders is our proposed method for image retrieval in Tea Leaf images.It is an efficient andflexible way of retrieving Tea Leaf images.It has an integrated autoencoder which makes it better than the state-of-the-art methods giving better results for the MAP(mean average precision)scores,which is used as a parameter to judge the efficiency of the model.The autoencoders used with skip connections increase the weightage of the prominent features present in the previous tensor.This constitutes a hybrid model for hashing and retrieving images from a tea leaf data set.The proposed model will examine the input tea leaf image and identify the type of tea leaf disease.The relevant image will be retrieved based on the resulting type of disease.This model is only trained on scarce data as a real-life scenario,making it practical for many applications.

Biomaterials-based anti-inflammatory treatment strategies for Alzheimer's disease

The current therapeutic drugs for Alzheimer's disease only improve symptoms,they do not delay disease progression.Therefo re,there is an urgent need for new effective drugs.The underlying pathogenic factors of Alzheimer's disease are not clear,but neuroinflammation can link various hypotheses of Alzheimer's disease;hence,targeting neuroinflammation may be a new hope for Alzheimer's disease treatment.Inhibiting inflammation can restore neuronal function,promote neuro regeneration,reduce the pathological burden of Alzheimer's disease,and improve or even reverse symptoms of Alzheimer's disease.This review focuses on the relationship between inflammation and various pathological hypotheses of Alzheimer's disease;reports the mechanisms and characteristics of small-molecule drugs(e.g.,nonsteroidal anti-inflammatory drugs,neurosteroids,and plant extracts);macromolecule drugs(e.g.,peptides,proteins,and gene therapeutics);and nanocarriers(e.g.,lipid-based nanoparticles,polymeric nanoparticles,nanoemulsions,and inorganic nanoparticles)in the treatment of Alzheimer's disease.The review also makes recommendations for the prospective development of anti-inflammatory strategies based on nanocarriers for the treatment of Alzheimer's disease.

Classification of Citrus Plant Diseases Using Deep Transfer Learning

In recent years,the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits.This in turn has helped in improving the quality and production of vegetables and fruits.Citrus fruits arewell known for their taste and nutritional values.They are one of the natural and well known sources of vitamin C and planted worldwide.There are several diseases which severely affect the quality and yield of citrus fruits.In this paper,a new deep learning based technique is proposed for citrus disease classification.Two different pre-trained deep learning models have been used in this work.To increase the size of the citrus dataset used in this paper,image augmentation techniques are used.Moreover,to improve the visual quality of images,hybrid contrast stretching has been adopted.In addition,transfer learning is used to retrain the pre-trainedmodels and the feature set is enriched by using feature fusion.The fused feature set is optimized using a meta-heuristic algorithm,the Whale Optimization Algorithm(WOA).The selected features are used for the classification of six different diseases of citrus plants.The proposed technique attains a classification accuracy of 95.7%with superior results when compared with recent techniques.

Plant growth-promoting rhizobacteria(PGPR)and its mechanisms against plant diseases for sustainable agriculture and better productivity

Plant growth-promoting rhizobacteria(PGPR)are specialized bacterial communities inhabiting the root rhizosphere and the secretion of root exudates helps to,regulate the microbial dynamics and their interactions with the plants.These bacteria viz.,Agrobacterium,Arthobacter,Azospirillum,Bacillus,Burkholderia,Flavobacterium,Pseudomonas,Rhizobium,etc.,play important role in plant growth promotion.In addition,such symbiotic associations of PGPRs in the rhizospheric region also confer protection against several diseases caused by bacterial,fungal and viral pathogens.The biocontrol mechanism utilized by PGPR includes direct and indirect mechanisms direct PGPR mechanisms include the production of antibiotic,siderophore,and hydrolytic enzymes,competition for space and nutrients,and quorum sensing whereas,indirect mechanisms include rhizomicrobiome regulation via.secretion of root exudates,phytostimulation through the release of phytohormones viz.,auxin,cytokinin,gibberellic acid,1-aminocyclopropane-1-carboxylate and induction of systemic resistance through expression of antioxidant defense enzymes viz.,phenylalanine ammonia lyase(PAL),peroxidase(PO),polyphenyloxidases(PPO),superoxide dismutase(SOD),chitinase andβ-glucanases.For the suppression of plant diseases potent bio inoculants can be developed by modulating the rhizomicrobiome through rhizospheric engineering.In addition,understandings of different strategies to improve PGPR strains,their competence,colonization efficiency,persistence and its future implications should also be taken into consideration.

Main Pests and Diseases of Zingiberaceae and Their Control

The primary diseases affecting Zingiberaceae plants include ginger plague, spot blotch, anthracnose, leaf spot, leaf blight, and soft rot. Insect pests that pose a threat to these plants encompass root-knot nematode disease, drilling bugs, beet nightshade moths, mesquite, thrips, and aphids. This article aims to summarize the defining features of the principal pests and diseases as well as their control methods. The intention is to offer theoretical support for the preservation of ginger plants.

Lightweight Method for Plant Disease Identification Using Deep Learning

In the deep learning approach for identifying plant diseases,the high complexity of the network model,the large number of parameters,and great computational effort make it challenging to deploy the model on terminal devices with limited computational resources.In this study,a lightweight method for plant diseases identification that is an improved version of the ShuffleNetV2 model is proposed.In the proposed model,the depthwise convolution in the basic module of ShuffleNetV2 is replaced with mixed depthwise convolution to capture crop pest images with different resolutions;the efficient channel attention module is added into the ShuffleNetV2 model network structure to enhance the channel features;and the ReLU activation function is replaced with the ReLU6 activation function to prevent the gen-eration of large gradients.Experiments are conducted on the public dataset PlantVillage.The results show that the proposed model achieves an accuracy of 99.43%,which is an improvement of 0.6 percentage points compared to the ShuffleNetV2 model.Compared to lightweight network models,such as MobileNetV2,MobileNetV3,EfficientNet,and EfficientNetV2,and classical convolutional neural network models,such as ResNet34,ResNet50,and ResNet101,the proposed model has fewer parameters and higher recognition accuracy,which provides guidance for deploying crop pest identification methods on resource-constrained devices,including mobile terminals.

Plant Disease Severity Assessment Based on Machine Learning and Deep Learning: A Survey

The world’s agricultural production suffers huge losses estimated between 20% and 40% annually. 40% to 50% of such losses are due to pest and diseases which cause significant economic losses every year. Precise assessment of severity is crucial for suitable management of crop diseases. It helps famers to avoid yield losses, reduce production costs, ensure good disease management and so on. This paper is a review of plant diseases severity estimation solutions proposed by researchers the last few years and based on Image Processing Techniques (IPT), classical Machine Learning (ML) and Deep Learning (DL) algorithms. The analysis of these solutions has allowed us to identify their limitations and potential challenges in plant disease severity assessment.

Towards Sustainable Agricultural Systems:A Lightweight Deep Learning Model for Plant Disease Detection

A country’s economy heavily depends on agricultural development.However,due to several plant diseases,crop growth rate and quality are highly suffered.Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information.Therefore,the agricultural management system is searching for an automatic early disease detection technique.To this end,an efficient and lightweight Deep Learning(DL)-based framework(E-GreenNet)is proposed to overcome these problems and precisely classify the various diseases.In the end-to-end architecture,a MobileNetV3Smallmodel is utilized as a backbone that generates refined,discriminative,and prominent features.Moreover,the proposed model is trained over the PlantVillage(PV),Data Repository of Leaf Images(DRLI),and a new Plant Composite(PC)dataset individually,and later on test samples,its actual performance is evaluated.After extensive experimental analysis,the proposed model obtained 1.00%,0.96%and 0.99%accuracies on all three included datasets.Moreover,the proposed method achieves better inference speed when compared with other State-Of-The-Art(SOTA)approaches.In addition,a comparative analysis is conducted where the proposed strategy shows tremendous discriminative scores as compared to the various pretrained models and other Machine Learning(ML)and DL methods.

Recent advance in natural plant products for treatment of dry eye disease

Dry eye disease(DED),a chronic multifactorial illness of the ocular surface with itching,burning,irritation,eye fatigue and ocular inflammation,may result in potential damage,such as cornea and conjunctiva,and even decreased vision.With the global prevalence of DED on the rise,it is crucial to find treatment options with minimal side effects.Natural plant products have shown promise in alleviating DED symptoms and may serve as a potential approach for its treatment.However,their application as instilled drugs is limited by solubility,stability and biological barriers.This review summarizes recent studies(published in the last 5 years)on natural plant products and their derivatives for the treatment of DED,focusing on efficacy,mechanism,drug delivery systems.Meanwhile,their shortcomings are also discussed.By exploring these aspects,we find polyphenol,flavonoid and others natural plant products can effectively improve or treat DED by different mechanisms,and suitable delivery system and structural modification can enhance their therapeutic effect,suggesting they are likely to become candidates for the treatment of DED.

Compounds from African Medicinal Plants with Activities Against Selected Parasitic Diseases:Schistosomiasis,Trypanosomiasis and Leishmaniasis

Parasitic diseases continue to represent a threat on a global scale,particularly among the poorest countries in the world.This is particularly because of the absence of vaccines,and in some cases,resistance against available drugs,currently being used for their treatment.In this review emphasis is laid on natural products and scaffolds from African medicinal plants(AMPs)for lead drug discovery and possible further development of drugs for the treatment of parasitic diseases.In the discussion,emphasis has been laid on alkaloids,terpenoids,quinones,flavonoids and narrower compound classes of compounds with micromolar range activities against Schistosoma,Trypanosoma and Leishmania species.In each subparagraph,emphasis is laid on the compound subclasses with most promising in vitro and/or in vivo activities of plant extracts and isolated compounds.Suggestions for future drug development from African medicinal plants have also been provided.This review covering 167 references,including 82 compounds,provides information published within two decades(1997-2017).

Ethnomedicinal plants used in the treatment of skin diseases in Hyderabad Karnataka region,Karnataka,India

Objective:To document traditional medicinal plants knowledge used in treating skin diseases at Hyderabad Karnataka Region.Methods:The information on the use of medicinal plants in the treatment of skin diseases was gathered from traditional herbal healers and other villagers through interviews.Results:A total of 60 plants species belonging to 57 genera and 34 families were found useful and herewith described them along with the method of drug preparation,mode of administration,probable dosage and duration of treatment.Several new findings on the traditional rural practices were reported.Conclusions:The present study revealed that the Hyderabad Kamataka rural people is primarily dependent on medicinal plants for treating skin diseases.