The common bacteria found in fruit and vegetables are Pseudomonas fluorescens which is Germ-negative and is rod-shaped. Pseudomonas fluorescens has been originated from the rhizosphere of Roorkee-grown okra. The presented work involves recognizing and controlling the isolates of Pseudomonas fluorescens. The scope of the proposed work is that the technique used here is a unique strategy to plant protection and control of rotting fungus diseases based on the recognition and management of Pseudomonas fluorescens isolates. Antagonist effect occurs commonly in vegetable and fruit plants. The main goal of this study is to isolate, identify, and evaluate the development of these bacteria which effects on plant growth. In this research work, five isolates have been chosen for further research based on their morphological, biochemical, and physiological characteristics. All five isolates have been identified as Pseudomonas fluorescens from Bergey’s Manual for the determination of bacteriology. Catalase, urease, amylase, and citrate utilization test were all positive in all of the isolates. PFTT4 was identified to be a likely strain for all plant growth promoting exercises such as age of IAA, HCN, ammonia, and phosphate solubilization subsequent to being assessed for their plant development advancing properties. Further, in vitro exploring uncovered that PFTT4 diminished the development of phytopathogens such as Fusarium solani and extraordinarily further developed seed germination just as all development boundaries like shoot and root length. Furthermore, Pseudomonas sp. PFTT4’s plant growth promoting and antifungal activities put forward to it could be there used because of bioinoculant agents for Abelmoschus esculentus.

In this research, citron peel biochar and opuntia-cladode fibers (OCF) reinforced epoxy composites were fabricated and characterized for mechanical, wear, and electrical properties. The biochar was prepared from the waste peels of citron edible fruit whereas the opuntia fiber was from the cladode of the opuntia plant. The laminates were fabricated by hand layup process and evaluated in accordance with the ASTM standards. The results revealed that the mechanical properties such as tensile strength, flexural strength, impact toughness, hardness and adhesion strength were increased by 36.2%, 30.6%, 91.3%, 1.1%, and 5.3% for composite designation EC containing 30 vol% of OCF. Similarly, the addition of citron biochar of 2 vol% increased the load bearing and dielectric properties. However, the inclusion of 30 vol% of OCF on composite designation EC the sp. wear rate recorded 0.018 mm 3 /Nm. Similarly, the lowest coefficient of friction and sp. wear rate is observed to be 0.42 and 0.008 mm 3 /Nm for the composite with 2.0 vol% biochar. The ECO 4 composite designation represented a maximum dielectric constant and dielectric loss of about 7.4 and 1.1, respectively. The SEM fractography demonstrates that the silane-treatment strengthened the fiber-matrix interface and improved the interlocking mechanism. Such mechanically robust, wear-resistant improved and electrically conductive composites could be utilized in applications such as industrial sectors, spacecraft, automobile parts, packaging industries, and electrical appliances.

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The citrus industry depends on the early identification of fungal infections, since a few infected fruits may spread the disease to a whole batch, resulting in substantial economic losses. In recent years, deep learning has played a significant role in the automated identification and categorization of illnesses in vegetables and fruits. This has increased the quality and quantity of vegetables and fruits. Numerous illnesses have a negative influence on the quality of citrus crops. Different pre-trained CNN models were employed in this research to identify and classify citrus diseases. The different CNN Models are compared with five pre-trained CNN models for detecting citrus diseases. Different combinations of training and learning methods are used with pre-trained architectures, such as VGG16, InceptionNet, ResNet, NasNet, MobileNet, and CNN for disease detection. A dataset of around 1500 images of diseases and healthy citrus leaves has been collected from different sources. The simulation shows that, of all the models, the MobileNet architecture is the most accurate, with an accuracy rate of 96%.

LNPs mix liposomes and inorganic/ organic nanoparticles. Liposomes and nanoparticles are therapeutic. LNPs are a research tool (e.g., spatiotemporal control of drug release, hyperthermia, photothermal therapy, and biological imaging). Nanoparticles determine LNP characteristics. Nanoparticles enable liposomes overcome weak stability, few functions, and fast blood elimination. Structure, physicochemical properties, modification, and biological uses of nanoparticle materials and LNPs are reviewed.