Crop Security Model for Improvement in Agricultural Productivity Using Iot: Smart Farming

Most of the time in agriculture field, crops ravaged by local animals that leads to huge losses for the farmers. It’s very difficult for farmers to barricade entire fields and monitors continuously. Here the crop protection system model is developed for the farmers to prevent the crops from the animals. The model adopts the Arduino Uno based system and uses wired security that gives the shock to animals if they are approaching the field. The fire sensor is also used in the model to detect fire issues. In such situations, the microcontroller will turn ON the motor if there is a fire that interns intimate the farmers through mobile application. The temperature sensor and humidity sensors are also used in the model to provide the details of temperature and soil moisture of the field. The experimental values obtained by the model ensure complete safety of crops from animals and from fire thus protecting the farmer’s loss. In addition, mobile applications are also developed to provide the details of parameters such as temperature, moisture, water levels to farmers

A Comparative Study on the Phenotypic Versus Molecular Identification of Clinical Dermatophytes

Dermatophytosis is the superficial infection of keratinized tissue like skin, hair, and nails, in humans and animals, by a group of closely related fungi known as dermatophytes. Phenotypic identification of dermatophytes, especially through classical methods can be difficult and uncertain at times, especially when differentiating species with overlapping characteristics. Alternative identification methods based on amplification and sequence analysis of the highly polymorphic internal transcribed spacer (ITS) sequences flanking the 5.8S ribosomal RNA gene has proven to be quite sensitive and reliable. The objective of our study was to compare the phenotypic and the ITS sequencing-based methods for the identification of clinically isolated dermatophyte specimens from Puducherry, India. A total of 13 clinical samples from 39 suspected cases were found positive for dermatophytes using KOH/DMSO preparations. Specimens were subsequently cultured in Sabouraud dextrose agar (SDA) supplemented with chloramphenicol, gentamicin, and cycloheximide. Dermatophytes were identified based on culture characteristics and microscopic examination in lactophenol cotton blue preparations. ITS sequencing was additionally performed after PCR amplification for species identification. Identification based on phenotype through microscopy and culture methods confirmed infections with Trichophyton mentagrophytes (n = 11), T. rubrum (n = 1), and Microsporum gypseum (n = 1). The strains were confirmed by ITS sequencing without any discrepancy with phenotypic identification. Identification of common dermatophytes based on phenotypic characteristics may be used as a reliable method of diagnosis where sophisticated methods like ITS sequencing and PCR are unavailable