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

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BIOCELL 2022年第8期46卷 1843-1859页

作者： PRANAB DUTTA GOMATHY MUTHUKRISHNAN SABARINATHAN KUTALINGAM GOPALASUBRAMAIAM RAJAKUMAR DHARMARAJ ANANTHI KARUPPAIAH KARTHIBA LOGANATHAN KALAISELVI PERIYASAMY MARUMUGAM PILLAI GK UPAMANYA SARODEE BORUAH LIPA DEB ARTI KUMARI MADHUSMITA MAHANTA PUNABATI HEISNAM AK MISHRA School of Crop Protection Central Agricultural University(Imphal)UmiamIndia Department of Soil Science and Agricultural Chemistry Agricultural College and Research InstituteTamil Nadu Agricultural UniversityKoyingbatoreIndia Department of Crop physiology Agricultural College and Research InstituteTamil Nadu Agricultural UniversityKoyingbatoreIndia Departments of Pulses Tamil Nadu Agricultural UniversityCoimbatoreKoyingbatoreIndia Department of Environmental Sciences Horticultural College and Research InstituteTamil Nadu Agricultural UniversityKoyingbatoreIndia Department of plant pathology Assam Agricultural UniversityDhubriIndia KVK-Tinsukia Assam Agricultural UniversityTinsukiaIndia Department of plant Pathology Central Agricultural University(Imphal)PasighatIndia Department of plant Pathology Dr Rajendra Prasad Central Agricultural UniversityBiharIndia

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 *** bacteria viz.,Agrobacterium,Arthobacter,Azospirillum,Bacillus,Burkholderia,Flavobacterium,Pseudomonas,Rhizobium,etc.,play important role in plant growth *** addition,such symbiotic associations of PGPRs in the rhizospheric region also confer protection against several diseases caused by bacterial,fungal and viral *** 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 *** 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-*** the suppression of plant diseases potent bio inoculants can be developed by modulating the rhizomicrobiome through rhizospheric *** addition,understandings of different strategies to improve PGPR strains,their competence,colonization efficiency,persistence and its future implications should also be taken into consideration.

关键词： plant growth-promoting rhizobacteria Biocontrol plant diseases PGPR mechanisms Sustainable agriculture

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PeachNet: Peach diseases Detection for Automatic Harvesting

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Computers, Materials & Continua 2021年第5期67卷 1665-1677页

作者： Wael Alosaimi Hashem Alyami M.Irfan Uddin Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan

To meet the food requirements of the seven billion people on Earth,multiple advancements in agriculture and industry have been *** main threat to food items is from diseases and pests which affect the quality and quantity of *** scientific mechanisms have been developed to protect plants and fruits from pests and diseases and to increase the quantity and quality of *** these mechanisms require manual efforts and human expertise to diagnose *** the current decade Artificial Intelligence is used to automate different processes,including agricultural processes,such as automatic *** Learning techniques are becoming popular to process images and identify different *** can use Machine Learning algorithms for disease identification in plants for automatic harvesting that can help us to increase the quantity of the food produced and reduce crop *** this paper,we develop a novel Convolutional Neural Network(CNN)model that can detect diseases in peach plants and *** proposed method can also locate the region of disease and help farmers to find appropriate treatments to protect peach *** the detection of diseases in Peaches VGG-19 architecture is *** the localization of disease regions Mask R-CNN is *** proposed technique is evaluated using different techniques and has demonstrated 94%*** hope that the system can help farmers to increase peach production to meet food demands.

关键词： Convolutional neural network computer vision image processing segmentation plant diseases

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Optimization techniques in deep convolutional neuronal networks applied to olive diseases classification

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Artificial Intelligence in Agriculture 2022年第1期6卷 77-89页

作者： El Mehdi Raouhi Mohamed Lachgar a Hamid Hrimech Ali Kartit LTI Laboratory ENSAChouaib Doukkali University of El JadidaEl JadidaMorocco LAMSAD Laboratory ENSAHassan First UniversityBerrechidMorocco

plants diseases have a detrimental effect on the quality but also on the quantity of agricultural ***,the prediction of these diseases is proving the effect on crop quality and on reducing the risk of production ***,the detection of plant diseases-either with a naked eye or using traditional methods-is largely a cumbersome process in terms of time,availability and results with a high-risk *** present work introduces a depth study of various CNN architectures with different optimization algorithms carried out for olive disease detection using classification techniques that recommend the best model for constructing an effective disease *** study presents a dataset of 5571 olive leaf images collected manually on real conditions from different regions of Morocco,that also includes healthy class to detect olive ***,one of the goals of this research was to study the correlation effects between CNN architectures and optimization algorithms evaluated by the accuracy and other performance *** highest rate in trained models was 100%,while the highest rate in experiments without data augmentation was 92,59%.Another subject of this study is the influence of the optimization algorithms on neuronal network *** a result of the experiments carried out,the MobileNet architecture using Rmsprop algorithms outperformed the others combinations in terms of performance and efficiency of disease detector.

关键词： Convolutional neuronal networks(CNN) Classification Optimization Gradient descent plant diseases Olive dataset diseases(ODD)

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Cross-comparative review of Machine learning for plant disease detection:apple,cassava,cotton and potato plants

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Artificial Intelligence in Agriculture 2024年第2期12卷 127-151页

作者： James Daniel Omaye Emeka Ogbuju Grace Ataguba Oluwayemisi Jaiyeoba Joseph Aneke Francisca Oladipo Department of Computer Science Federal UniversityLokojaNigeria Department of Computer Science Dalhousie UniversityHalifaxCanada Department of Computer Science Hampton UniversityVirginiaUSA

plant disease detection has played a significant role in combating plant diseases that pose a threat to global agri-culture and food *** these diseases early can help mitigate their impact and ensure healthy crop *** learning algorithms have emerged as powerful tools for accurately identifying and classifying a wide range of plant diseases from trained image datasets of affected *** algorithms,including deep learning algorithms,have shown remarkable success in recognizing disease patterns and early signs of plant *** early detection,there are other potential benefits of machine learning algorithms in overall plant disease management,such as soil and climatic condition predictions for plants,pest identification,proximity detection,and many *** the years,research has focused on using machine-learning algorithms for plant disease ***,little is known about the extent to which the research community has ex-plored machine learning algorithms to cover other significant areas of plant disease *** view of this,we present a cross-comparative review of machine learning algorithms and applications designed for plant dis-ease detection with a specific focus on four(4)economically important plants:apple,cassava,cotton,and *** conducted a systematic review of articles published between 2013 and 2023 to explore trends in the research community over the *** filtering a number of articles based on our inclusion criteria,including articles that present individual prediction accuracy for classes of disease associated with the selected plants,113 articles were considered *** these articles,we analyzed the state-of-the-art techniques,challenges,and future prospects of using machine learning for disease identification of the selected *** from our re-view show that deep learning and other algorithms performed significantly well in detecting plant *** addition,we fou

关键词： Machine learning plant diseases Agriculture plant disease management Convolutional neural networks

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Recent advances in Raman technology with applications in agriculture, food and biosystems: A review

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Artificial Intelligence in Agriculture 2019年第3期3卷 1-10页

作者： Shizhuang Weng Wenxiu Zhu Xueyan Zhang Hecai Yuan Ling Zheng Jinling Zhao Linsheng Huang Ping Han Agricultural Product Quality and Safety Risk Assessment Laboratory of Ministry of Agriculture(Beijing) Beijing Academy of Agricultural and Forestry SciencesChina National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui University111 Jiulong RoadHefeiChina

Raman technology,which covers Raman spectroscopy(RS)and its various derivativemethods,has been widely applied in detection of various substances in agriculture,food and *** article reviews the recent advances in two mainstream Raman technologies as RS and SERS,including technical evolution,application and challenges,and spectral processing ***,the origin,principle,defect,and development of RSwere ***,the current situation,existing problems,and development trend of RS and SERS were discussed in agriculture,food,and biosystems,such as adulteration recognition,plant diseases identification,farm chemicals detection,food additives determination,and toxins *** last,the spectral analysis methods include noise reduction,feature extraction or variable selection,and modelingwere introduced in detail,which can realize the automatic and intelligent analysis of spectra without relying on professionals.

关键词： Raman technology Adulteration plant diseases Farm chemicals Toxins Spectral processing methods

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IN VITRO ACTIVITY OF EXTRACTS OF FIVE MEDICINAL plant SPECIES ON plant PATHOGENIC FUNGI

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Frontiers of Agricultural Science and Engineering 2021年第4期8卷 635-644页

作者： Shufeng YAO Jiali XU Haibo ZHANG Hong GAO Shibin SHANG Dan WANG Institute of Chemical Industry of Forest Products Chinese Academy of ForestryKey Laboratory of Biomass Energy and MaterialJiangsu ProvinceKey Laboratory of Chemical Engineering of Forest ProductsNational Forestry and Grassland AdministrationNanjing 210042China Co-Innovation Center of Efficient Processing and Utilization of Forest Resources Nanjing Forestry UniversityNanjing 210037China

The antifungal effectiveness of extracts of five medicinal plant species was *** inhibitory activity of extracts of Eucalyptus tereticornis,Xanthium sibiricum,Artemisia argyi,Tupistra chinensis and Pyrola calliantha were evaluated against the mycelial growth of the plant pathogenic fungi Aspergillus niger,Botrytis cinerea,Penicillium digitatum,***,*** and Rhizopus *** plant extracts were prepared at 60°C using solvents(either water,50%ethanol(v/v),95%ethanol(v/v),ethyl acetate or petroleum ether).Fungicidal effects of all plants tested were *** extracts from the same plant species gave different degrees of *** aqueous extracts had weak or no activity on all fungi *** acetate and 95%ethanol extracts from *** rhizomes gave greater inhibition and a broader spectrum inhibition than the other *** may have potential as a new natural fungicide and may be used for the preservation of agricultural and forestry products such as fruits and vegetables.

关键词： Botrytis cinerea food preservation plant diseases plant pathogenic fungi Tupistra chinensis

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