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पशुपालन में कृत्रिम बुद्धिमत्ता का उपयोग

Not Availableआर्टिफिशियल इंटेलिजेंस (एआई) अर्थात् कृत्रिम बुद्धिमत्ता कंप्यूटर विज्ञान का एक तेजी से उभरता हुआ क्षेत्र है, जिससे सामाजिक परिदृश्य में जमीनी बदलव की उम्मीद है । आर्टिफिशियल इंटेलि जेंस पहल े ही अन्य क्षेत्रों में दक्षता और उत्पाद कता बढ़ाने में अपना लोह मनवा चुका है और अब पशु स्वास्थ्य में इसके कार्यान्व यन की कसौटी पर खरा उतरने का समय है । एआई एक ऐसे उपकरण के रूप में उभरा है जो किसानों को निगरानी, पूर्वानुमान, साथ ही साथ कृषि पशु विकास को अनुकूलित करने में सक्षम बनाता है । पशु स्वास्थ्य की निगरानी में एआई का अनुप्रयोग आनुवंशिक गुणों और व्यवहार पता करने सहित कृषि के सर्वोत्तम स्वास्थ्य का प्रबंधन, प्रौद्योगिकि यों और उपकरणों द्वारा सुनिश् चित करता है । एआई तकनीक को कई उद्योगों द्वारा सफल तापूर्वक अपनाया गया है, और अब यह ड्रोन, रोबोट और बुद्धिमान निगरानी प्रणाली के साथ पशुपालन के भविष्य में क्रांति लाने के लि ए तैयार है ।Not Availabl

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Not AvailableThe term “foresight” has long been used to describe readiness to deal with long-term issues (especially on the part of governments). The term “Technology Foresight” took off in the 1990s when countries sought new policy tools to deal with problems in their science, technology, and innovation systems. Technology Foresight (TF) refers to the process involved in systematically attempting to look into the long-term future of science, technology, the economy, and society to identify the areas of strategic research and the emerging generic technologies likely to yield the greatest economic and social benefits (Pietrobelli and Puppato, 2016). Overall, TF studies provide strategic information for decision-making and long-term planning in science and technology, being used by governments, and organizations to prepare for the future. TF is different from technology forecasting. TF means a new future-oriented approach while Technology Forecasting is a traditional forward-looking approach developed in the 1950s in the USA. TF helps to shape the future of science and technology as well as society. TF is often seen as a set of tools for informing decisions about science, technology, and innovation (STI) priorities within established innovation systems (Miles et al., 2017). As the world faces a wide range of critical challenges, from conflicts and climate change to population growth, countries increasingly need to harness the right technologies to leverage comparative advantages, drive economic growth, and fulfil strategic needs. National-level TF is a vital tool for policymakers to identify domains of high importance or potential. This viewpoint outlines key imperatives for countries to ensure foresight best delivers value in a pragmatic, repeatable manner. In this article, we elaborate on TF for agriculture in the next 25 years. The foresight is based on recent trends in research in developed and developing countries. In Section 2, we focus on the most prevalent technologies of the 21 century. We anticipate the present and future application of disruptive technologies in agriculture in Section 3. Section 4 briefs on the challenges and opportunities of these emerging applications. Finally, we conclude in Section 5Not Availabl

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Not AvailableAgriculture and its allied sectors significantly contribute to the Indian economy's long-term growth and development and a livelihood for the majority of the population that can never be deprecated. Since it has made significant progress toward the agricultural development goals of food security, abundance, and accessibility, it started to pose a serious agricultural crisis to satisfy hunger in the coming period. Although farmers are very innovative and always ready to accept and adopt the latest technologies developed in the farming sector for increasing the farmers’ income but there are many more advanced technologies available and one outcoming technology is the use of robots in the field of agriculture in various farm operations like weed control fertilizer application, automated irrigation, etc. So here the goal of Artificial Intelligence (AI) comes. AI is a discipline that can empower machines to execute tasks in real- time situations and cognitive processing like the human mind. AI in agriculture not only helps the farmers to automate their farming operations like sowing, harvesting, crop monitoring, and sale purchase of their product but also shifts to precise cultivation for precision farming for higher crop yield and better quality by optimizing the inputs/resources. Using AI techniques in various fields of agriculture will provide more useful applications to the sector, which are directly or indirectly helping the world to deal with food production issues for the growing population. Some of the challenges faced by farmers using traditional agricultural methods are as follows: harsh weather conditions, the gap between demand and supplies, competition for growth factors by pests, etc. can be optimized using AI techniques.Not Availabl

ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features

MicroRNAs (miRNAs) play a significant role in plant response to different abiotic stresses. Thus, identification of abiotic stress-responsive miRNAs holds immense importance in crop breeding programmes to develop cultivars resistant to abiotic stresses. In this study, we developed a machine learning-based computational method for prediction of miRNAs associated with abiotic stresses. Three types of datasets were used for prediction, i.e., miRNA, Pre-miRNA, and Pre-miRNA + miRNA. The pseudo K-tuple nucleotide compositional features were generated for each sequence to transform the sequence data into numeric feature vectors. Support vector machine (SVM) was employed for prediction. The area under receiver operating characteristics curve (auROC) of 70.21, 69.71, 77.94 and area under precision-recall curve (auPRC) of 69.96, 65.64, 77.32 percentages were obtained for miRNA, Pre-miRNA, and Pre-miRNA + miRNA datasets, respectively. Overall prediction accuracies for the independent test set were 62.33, 64.85, 69.21 percentages, respectively, for the three datasets. The SVM also achieved higher accuracy than other learning methods such as random forest, extreme gradient boosting, and adaptive boosting. To implement our method with ease, an online prediction server “ASRmiRNA” has been developed. The proposed approach is believed to supplement the existing effort for identification of abiotic stress-responsive miRNAs and Pre-miRNAs

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Not AvailableArtificial Intelligence (AI) and Internet of Things (IoT) have a huge potential to effectively address the issues of climate change. The aim of this study is to present the role of AI and IoT based technologies in making environment friendly smarter and higher performing systems. These technologies aid in managing the climate change impacts by utilizing limited resources and less human interference. Integrating IoT and AI technologies, data is collected from sensors in the  eld about soil moisture, weather conditions, fertilization levels, irrigation system, soil composition and temperature. This helps in increasing the crop production which eventually lead to higher income for farmers.Not Availabl