The purity of seeds is the most important factor in agriculture that determines crop yield, price, and quality. Rice is a major staple food consumed in different forms globally. The identification of high yielding and good quality paddy seeds is a challenging job and mainly dependent on expensive molecular techniques. The practical and day-today usage of the molecular-laboratory based techniques are very costly and time-consuming, and involves several logistical issues too. Moreover, such techniques are not easily accessible to paddy farmers. Thus, there is an unmet need to develop alternative, easily accessible and rapid methods for correct identification of paddy seed varieties, especially of commercial importance. We have developed iRSVPred, deep learning based on seed images, for the identification and differentiation of ten major varieties of basmati rice namely,
The dataset contains images of 10 out of 32 notified Indian basmati seeds varieties (by the Government of India). Indian basmati paddy varieties included in the dataset are 1121, 1509, 1637, 1718, 1728, BAS-370, CSR 30, Type-3/Dehraduni Basmati, PB-1 and PB-6. Moreover, several images of other seeds and related entities available in the household have also been included in the dataset. Thus, the dataset contains 11 classes such that ten classes contain images from ten different basmati paddy varieties. In contrast, the 11th class- named “Unknown” contains images from a mixture of two morphologically similar paddy varieties (1121 and 1509), different pulses, other grains and related food entities. The Unknown class is useful in discriminating the paddy seeds from other types of seeds and related food entities. All the images were captured (in standard conditions) manually using an apparatus developed in-house and a tablet with a five-megapixel camera (5MP). The camera was used to capture 3210 RGB coloured images in JPG format. The data pre-processing was performed to generate the ready-to-use images for training and testing machine learning-based models. AI-based paddy seed variety classification models have been developed using the dataset. The dataset can be used to generate different types of AI-based models for adulteration detection, automated classification models (along with independent devices) at the time of rice threshing, and to increase the classification potential (Supplementing images representing additional basmati varieties).
The study was conducted to analyze the value chain of mandarin orange in Jajarkot district with the objective of drawing value chain map, defining linkage and value governance and finding major constraints. Total 82 respondents were interviewed by a semi-structured questionnaire including 60 farmers, 5 retailers, 5 collectors, 10 consumers, and 2 processors. EXCEL 2019 and SPSS 20 were used to enter and analyze data. Grading and sorting were major value-adding activities while processing was done at the retailer level in end markets. Grading fetched 4.188% and 3.94% more profit in contractor and consumer level respectively. The Most dominating channel was farmer-local consumer (46%) where farmers sold produce to Jajarkot fair. The Average price at farmgate, retailer, collector and contractor were 39.08/kg, 61.2/kg, 46.75/kg, and 49.75/kg respectively. Productivity of mandarin was found 8.54 mt/ha and B/C ratio was found 2.56. Margin in farmer-collector-retailer-consumer channel was 29.25 and in farmer-retailer-consumer channel was 23.08. Producer share was found highest in channel 5 (60.13%) and market efficiency was found higher in channel 3 (4.88%). Similarly, price spread in channels 3,4 and 5 were 34.33%, 64.19%, and 66.75% respectively. Vertical Integration included farmer and nurseries in backward linkage and farmer and farmer collector in the forward linkage. High transport cost was the reason for the high price of mandarin. Overall, the trade of mandarin in Jajarkot was found buyer-driven. Major problems related to marketing were poor storage (0.877) and processing facilities (0.833). The study revealed that mandarin production is profitable and potential in Jajarkot.
To know the level of adoption of different technologies in mandarin orange, survey was conducted on April 2019 in Jajarkot district. Survey was done with 70 farmers of Nalgad municipality, Bheri municipality and Kushe rural municipal. Focus group discussion and Key Informant Survey was done with progressive farmers and JTs. The study revealed that majority of high adopters are male (66.10%) and had education level of primary and secondary. Farmers with highest number of bearing trees had highest rate of adaptation. Overall increase in adaptation over two years was 29.55%. Most adapted technology was use of bordopaste (84%) and least adopted technology was sprinkler irrigation. Main reason for increase in adoption was subsidy. There was increase in production by 52% due to increase in adoption level. Major constraint for adoption was poor access to market followed by less technical knowhow. So, it is recommended to provide appropriate market for farmers to improve the adaptation level in mandarin orange production.
Proteases regulate cell proliferation, cell growth, biological processes, and overall homeostasis. Several proteases are extensively annotated and well-characterized in pathogenic organisms such as bacteria, parasites, and microbial species as anti-bacterial, anti-parasitic and anti-microbial. Several of these proteins are being explored as viable targets for various drug discovery research in various microbial diseases. Despite multiple studies on pathogen proteases, the comprehensive information on pathogen proteases is scattered or redundant, if available. We have developed a comprehensive and integrative protease database resource, ProtPathDB, for 23 pathogen species distributed among five taxa, Amoebozoa, Apicomplexa, Heteroloblosea, Kinetoplastida and Fungi. ProtPathDB collects and organizes sequences, class division, signal peptides, localization, post-translational modifications, three-dimensional structure and related structural information regarding binding sites, and binding scores of annotated proteases. The ProtPathDB is publicly available at http://bioinfo.icgeb.res.in/ProtPathDB. We believe that the database will be a one-stop resource for integrative and comparative analysis of pathogen proteases to better understand the functions of the microbial proteases and help drug discovery efforts target proteases.
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