Jammu and Kashmir Approves Agro-Meteorology Centers for Agricultural Growth

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SKUAST-Kashmir to Establish Centre of Artificial Intelligence and Machine Learning in Agriculture

Srinagar: The Sher-e-Kashmir University of Agricultural Sciences and Technology (SKUAST) in Kashmir is set to establish a Centre of Artificial Intelligence and Machine Learning in Agriculture, as announced during a recent meeting. The meeting, attended by Jammu and Kashmir Chief Secretary Atal Dulloo and Principal Secretary of the Finance Department Santosh D Vaidya, among others, focused on various agenda points related to the development of the agricultural sector.

The lieutenant governor of Jammu and Kashmir commended the significant contribution of SKUAST-Kashmir in the growth of the agriculture and allied sector. He praised the university’s innovation-led initiatives and holistic approach, stating that they are contributing to a knowledge-based, technology-driven, and sustainable agri-economy in the region.

During the meeting, the roadmap for developing SKUAST-Kashmir as a leading institution for agricultural education, innovations, entrepreneurship, leadership, and research was discussed. Emphasizing the need to bring research and innovation from the lab to the fields, the lieutenant governor instructed the university to focus on increasing farmers’ productivity.

Furthermore, the meeting highlighted the importance of starting short skill training courses for students and conducting a detailed study on yield gaps. Reforms to promote start-ups and entrepreneurship in the agricultural sector were also deliberated upon, along with the exploration of smart and intelligent farming practices. The university’s goals and mandates will be reset to align with national priorities.

The establishment of the Centre of Artificial Intelligence and Machine Learning in Agriculture at SKUAST-Kashmir is a significant step towards leveraging cutting-edge technologies in the agricultural sector. By harnessing the power of artificial intelligence and machine learning, the centre aims to enhance agricultural productivity, optimize resource utilization, and mitigate climate change impacts on farming practices.

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With this new initiative, SKUAST-Kashmir is positioning itself as a pioneer in agricultural education and research, contributing to the growth and development of the agricultural sector in the region. The centre will serve as a hub for innovation, fostering collaboration between academia, industry, and farmers to address the challenges faced by the agriculture sector.

As the agricultural landscape continues to evolve, the establishment of the Centre of Artificial Intelligence and Machine Learning in Agriculture reflects the commitment of SKUAST-Kashmir to embrace technological advancements and drive sustainable agricultural practices. This transformative step will empower farmers with data-driven insights and facilitate informed decision-making, ultimately leading to enhanced food security and economic growth in the region.

It is anticipated that the Centre of Artificial Intelligence and Machine Learning in Agriculture will play a pivotal role in shaping the future of agriculture in Jammu and Kashmir. Through research, training, and the application of advanced technologies, SKUAST-Kashmir aims to maximize agricultural productivity, improve livelihoods, and ensure the overall well-being of farmers in the region.

With the support of the government and stakeholders, SKUAST-Kashmir is poised to become a leading institution for agricultural education, innovation, and research. The establishment of the centre signifies a major step forward in the efforts to create a sustainable and technology-driven agricultural sector in Jammu and Kashmir.

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Kunal Joshi
Kunal Joshi
Meet Kunal, our insightful writer and manager for the Machine Learning category. Kunal's expertise in machine learning algorithms and applications allows him to provide a deep understanding of this dynamic field. Through his articles, he explores the latest trends, algorithms, and real-world applications of machine learning, making it accessible to all.

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