When plants and crops are suffering from pests it affects the agricultural production of the country. Usually, farmers or experts observe the plants with eye for detection and identification of disease. But this method is often time processing, expensive and inaccurate. Automatic detection using image processing techniques provide fast and accurate results. This paper cares with a replacement approach to the development of disease recognition model, supported leaf image classification, by the utilization of deep convolutional networks. Advances in computer vision present a chance to expand and enhance the practice of precise plant protection and extend the market of computer vision applications within the field of precision agriculture. a completely unique way of training and therefore the methodology used facilitate a fast and straightforward system implementation in practice. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images to make a database, assessed by agricultural experts, a deep learning framework to perform the deep CNN training. This method paper may be a new approach in detecting plant diseases using the deep convolutional neural network trained and finetuned to suit accurately to the database of a plants leaves that was gathered independently for diverse plant diseases. The advance and novelty of the developed model dwell its simplicity healthy leaves and background images are in line with other classes, enabling the model to distinguish between diseased leaves and healthy ones or from the environment by using CNN. Plants are the source of food on earth. Infections and diseases in plants are therefore a big threat, while the foremost common diagnosis is primarily performed by examining the plant body for the presence of visual symptoms [1]. As an alternative to the traditionally time-consuming process, different research works plan to find feasible approaches towards protecting plants. In recent years, growth in technology has engendered several alternatives to traditional arduous methods [2]. Deep learning techniques are very successful in image classification problems.

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Background:The emergence of coronavirus disease 2019 (COVID-19) has resulted in a pandemic that has significantly impacted healthcare systems at a global level. Health care facilities in Nepal, as in other low- and middle-income countries, have limited resources for the treatment and management of COVID-19 patients. Only critical cases are admitted to the hospital resulting in most patients in home isolation.MethodsHimalaya Home Care (HHC) was initiated to monitor and provide counseling to home isolated COVID-19 patients for disease prevention, control, and treatment. Counselors included one physician and four nurses. Lists of patients were obtained from district and municipal health facilities. HHC counselors called patients to provide basic counseling services. A follow-up check-in phone call was conducted 10 days later. During this second call, patients were asked about their perceptions of the HHC program. Project objects were: (1) To support treatment of home isolated persons with mild to moderate COVID-19, decrease burden of hospitalizations, and decrease risks for disease transmission; and, (2) To improve the health status of marginalized, remote, and vulnerable populations in Nepal during the COVID-19 pandemic.ResultsData from 5823 and 3988 patients from May 2021-February 2022 were entered in initial and follow-up forms on a REDCap database. The majority of patients who received counseling were satisfied. At follow-up, 98.4% of respondents reported that HHC prevented hospitalization, 76.5% reported they could manage their symptoms at home, and 69.5% reported that counseling helped to limit the spread of COVID-19 in their household.ConclusionsTelehealth can be an essential strategy for providing services while keeping patients and health providers safe during the COVID-19 pandemic.

Objective: Aim to assess the incidence of intestinal parasites in government and private school going children. Methods: The work was conducted from October, 2018 to March, 2019 at Microbiology Laboratory of DAV College, Dhobighat, Lalitpur. A total of 100 stool samples of children aged between 5-12 years were collected from both government and private schools situated in Lalitpur metropolitan city, during school hours. The stool samples were examined for intestinal parasites by Saline wet mount; Iodine wet mount and Formal – ether sedimentation technique. The questionnaires accompanying the queries related to the study were filled. Results: Of the total 100 stool samples examined, intestinal parasites were observed in 7% (7/100) of the total stool samples. Among the positive stool samples, 71% (5/7) of the stool samples were from government school’s children whereas 29% (2/7) were from private school’s children. Fifty seven percentage 57% (4/7) girls and 43% (3/7) boys were found to be infected with intestinal parasite in the tested stool samples. Out of total parasite detected, 57% (4/7) were eggs of Ancylostoma duodenale, 29% (2/7) were eggs of Ascaris lumbricoides and 14% (1/7) were cysts of Giardia lamblia. The study indicates that Ancylostoma is the most commonly infecting parasite followed by Ascaris and Giardia. Conclusion: Personal hygiene and sanitary condition were responsible for the incidence of intestinal parasites in the school going children. Environmental sanitation improvement and health education promotion will be helpful to reduce the parasitic infection rate.

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