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Medicinal plants are very essential in maintaining the physical and mental health of human beings. For providing better treatment, Identification and classification of medicinal plants is essential. In this research paper, main objective is to create a medicinal plant identification system using Deep Learning concept. This system identifies and classifies the medicinal plant species with high accuracy. In this system, five different Indian medicinal plant species namely Pungai, Jamun (Naval), Jatropha curcas, kuppaimeni and Basil are used for identification and classification. The dataset contains 58,280 images, includes approximately 10,000 images for each species. The leaf texture, shape, color, physiological or morphological as the features set for leaf identification. The CNN architecture is used to train the collected dataset and develop the system with high accuracy. As result of this model, 96.67% success rate in finding the corresponding medicinal plant. This model is advisable to use as early detection tool for finding the medicinal plant because of its best success rate

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The experiment was carried out during 2019-20 at P-2 Pure Mysore Multivoltine Basic Seed Farm (BSF), National Silkworm Seed Organisation, Nagenahalli, Karnataka to know the effect of nutrient management on the growth and yield of G4 mulberry variety and its subsequent bioassay of multivoltine silkworm, Pure Mysore. The mulberry garden (three years old) with G4 variety planted in paired row system was used for the experiment with seven treatments and three replications. The growth parameters (average of 5 crops) viz., plant height, number of branches per plant, lowest number of leaf in 100g weight, weight of individual leaf, weight of 100 fresh leaf, leaf yield per plant and leaf yield per ha-1were significantly highest (134.6 cm, 14.0, 24.0, 4.18g, 384g, 800g and 55.55 Mt) with the application of 100 % RDF, poshan spray and application of vermicompost at5 t/ha/year. The results of the bio-assay (average of five rearings) also showed the superiority forweight of 10 full grown larvae (27.2 gm), single cocoon weight (1.27g), single shell weight (0.18g), shell ratio (14.25%), ERR (95.00%) pupation (92.00%), number of cocoons/kg (787) and yield per 100 dfls (52.10 kg) in the treatment having 100 % RDF, poshan spray and application of vermicompost of at5 t/ha than other treatments. Combined application of organic and inorganic sources of nutrients increased the productivity of the mulberry in G4 variety and subsequently better performance of Pure Mysore multivoltine seed cocoon parameters.

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Study was conducted for screening and evaluation of selected breed’s for nutrigenetic traits in silkworm, Bombyx mori L. (Lepidoptera: Bombycidae) is an essential prerequisite for better understanding and development of nutritionally efficient breeds under Subtropical condition of North Western India based on the breeds which shows less food consumption with higher efficiency conversion. The aim of this study was to identify nutritionally efficient bivoltine silkworm breeds selected from different regions of our country. Highly significant differences were found among all nutrigenetic traits of bivoltine silkworm breeds in the study. The nutritionally efficient silkworm breeds were resulted by utilizing nutrition consumption index and efficiency for conversion of ingesta/cocoon traits as the index for selection of highly promising breeds. Higher nutritional efficiency conversions were found in the bivoltine silkworm breeds on efficiency of conversion of ingesta to cocoon and shell were shortlisted during spring season. Furthermore, based on the overall nutrigenetic traits utilized as index, eight bivoltine silkworm breeds (B.con 1, B.con 4, BHR 2, ATR 16, BHR 3, CSR 50, RSJ 14 and NB4D2) were identified as having the potential for nutrition efficiency conversion and can be utilized for further breeding programme. The data from the present study advances our knowledge for the development of nutritionally efficient silkworm breeds/hybrids and their effective commercial utilization in the sericulture industry.