Volume 10 | Issue 2 |

 Abstract

Kerala State holds a unique position in the tribal map of India. The study throws light on the reproductive health of the tribal women in Kerala with respect to maternal care. The research design is descriptive design. The aspects related to antenatal care, natal care and postnatal care of tribal women in Kerala is assessed. The various implications of the study in the field of health of tribal women is explicated.

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 Keywords

Reproductive Health, Tribal Women, Kerala

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 Abstract

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Border Dispute, Attitude

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For detecting the incidence and quantifying the severity of variability in crops, image-based plant disease detection is one of the most important activities in precision agriculture. Pathogen-caused diseases account for 70 percent to 80 percent of the variabilities, with 60 percent to 70 percent appearing on the leaves compared to the stem and fruits. This study uses leaf image data to conduct a comparative comparison of model implementation for plant disease diagnosis. Until recently, most of these image processing algorithms relied on what some referred to as "shallow" machine learning architectures, and some still do. The DL network is quickly becoming the gold standard for image recognition and pattern analysis research. Regardless, there aren't many research on its use in detecting disease in plant leaves. Thus, we used conventional settings and took into account the three critical elements of architecture, processing capacity, and the amount of training data in our study on a large plant leaf disease picture dataset. In a developing agricultural economy like India, early detection of plant leaf detection is critical. Leaf diseases in plants must be diagnosed at an early stage and predictive techniques implemented to make them safe and avoid losses to the agri-based economy, not only as an agricultural economy but also as one with a big population to feed. This work proposes to identify Leaf disease utilising image processing techniques based on image segmentation, clustering, and image detection algorithms, all of which contribute to a reliable, safe, and accurate leaf disease detection system with specialisation.

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DETECTION OF MULTIPLE PLANT DISEASE USING DIGITAL IMAGE PROCESSING

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The present study was conducted in the Kadapa district in Andhra Pradesh. Data were collected personally from 60 dairy-owning households hailing from different areas of the Kadapa district using convenience sampling. A structured questionnaire comprised of knowledge about dairy activities, economic motivation, scientific orientation, self-confidence, achievement motivation, and Proneness. The collected data set was analyzed with the help of suitable statistical techniques like multiple regression analysis with jamovi. The results stated that all the factors were significantly influencing the management efficiency among dairy entrepreneurs and knowledge, economic motivation, and scientific orientation influenced largely than other components in the study area.

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Management efficiency; Dairy entrepreneurs; entrepreneurship; influencing factors, Kadapa,

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 Abstract

VARTAMAN SAMAY MEIN MAGHAPAN KE SAMBANDH MEIN PRABHAV EVAN BADHAE

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VARTAMAN SAMAY MEIN MAGHAPAN KE SAMBANDH MEIN PRABHAV EVAN BADHAE

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 Abstract

Various versions of IoT devices for varying use circumstances have been made throughout the years through the company as well as study associations. Smart Houses are one example of such IoT devices. In other places, identical advancements are started, like Connected Cars, Smart Cities, Requirement Side Administration, Smart Grids, or even Smart Manufacturing plant gadgets. This paper provides the future research directions and challenges towards Internet of Things.

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Internet of Things, research directions, challenges

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Terrorism State Terrorism Non-State Terrorism

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THE NORTHEAST IMBROGLIO: A SOCIOLOGICAL PERSPECTIVE

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THE NORTHEAST IMBROGLIO: A SOCIOLOGICAL PERSPECTIVE

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Plants require adequate nutrient content for a total as well as natural life cycle. Six macronutrients such as nitrogen, calcium, phosphorus, potassium, sulfur and magnesium are important for the natural and healthy rise of plants. Regular activities with a lack of nutrients in plants lead to transportation difficulties and ultimately affect crop. Plants show the definite lack of nutrient on their leaves, fruits with notable differences in pattern. Our research suggested is to provide an automated and economically viable method for detecting defects nutritional conditions. Our system uses helpful information to forecast performance of crops. The dataset for faulty leaves and healthy leaves is developed with the help of the RGB Color Extraction Analysis Technique, Disclosure of texture in real time, Identification of bottom edge etc. This dataset will allow supervised machine learning to predict and identify accurate shortages of vitamins and healthy plants to prohibit growth rates.

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 Keywords

RGB Color Extraction Analysis Technique, Supervised machine learning, Deep Learning, Image Processing

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Soft skills, higher education, prejudice, discrimination, ethnicity, intercultural competencies.

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