5 min
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Image credit: @nstomar/Twitter[/caption]
The BJP-led Narendra Modi government has been giving a great deal of importance to emerging tech and had, in fact, proposed various programmes and new developments in their Lok Sabha election manifesto. Following the same cue, Narendra Singh Tomar, Union Minister for Agriculture and Farmers Welfare emphasised the importance of using artificial intelligence in agriculture in order to double the income of farmers.
Earlier this week, the Government announced that they have set a target of doubling the Indian farmers’ income by 2022. The Centre also established an inter-ministerial committee to examine farmers’ issues to recommend a strategy to achieve the goal.
Speaking at the occasion, Tomar said, "The role of digital technology, which can play a transformational role in modernizing and organizing how rural India performs its agricultural activities. The technologies include AI, big data analytics, blockchain, IoT etc. By using these technologies and AI and giving accurate and timely information regarding crops, weather and insects etc, the farmers may improve the crop productivity, reduce the risk and improve the income of the farmers," he said.
https://twitter.com/narendramodi/status/1145543315682680836
The committee also said that they would be focussing on emerging technology and asserted that it was the only way to achieve a swift transformation in the Indian rural diaspora. Most changes and recommendations by the committee will include AI, big data analytics, Blockchain and IoT among other emerging technologies. Some of their other tech interventions are:
Image credit: @nstomar/Twitter[/caption]
The BJP-led Narendra Modi government has been giving a great deal of importance to emerging tech and had, in fact, proposed various programmes and new developments in their Lok Sabha election manifesto. Following the same cue, Narendra Singh Tomar, Union Minister for Agriculture and Farmers Welfare emphasised the importance of using artificial intelligence in agriculture in order to double the income of farmers.
Earlier this week, the Government announced that they have set a target of doubling the Indian farmers’ income by 2022. The Centre also established an inter-ministerial committee to examine farmers’ issues to recommend a strategy to achieve the goal.
Speaking at the occasion, Tomar said, "The role of digital technology, which can play a transformational role in modernizing and organizing how rural India performs its agricultural activities. The technologies include AI, big data analytics, blockchain, IoT etc. By using these technologies and AI and giving accurate and timely information regarding crops, weather and insects etc, the farmers may improve the crop productivity, reduce the risk and improve the income of the farmers," he said.
https://twitter.com/narendramodi/status/1145543315682680836
The committee also said that they would be focussing on emerging technology and asserted that it was the only way to achieve a swift transformation in the Indian rural diaspora. Most changes and recommendations by the committee will include AI, big data analytics, Blockchain and IoT among other emerging technologies. Some of their other tech interventions are:
- Development of Kisan Suvidha mobile app
- Development of mKisan Portal for sending advisories on the various crop-related matter to the registered farmers through text messages
- Launching of e-National Agriculture Market initiative to provide farmers with an electronic online trading platform
- Use of space technology for various programmes and areas such as Forecasting Agricultural Output using Space, Agrometeorology and Land-based Observations project, Coordinated programme on Horticulture Assessment and Management using geo-informatics project, National Agricultural Drought Assessment and Monitoring System, Rice-Fallow Area Mapping and intensification, geo-tagging of infrastructure and assets created under Rashtriya Krishi Vikas Yojana and Crop Insurance
- Using ML along with other algorithms for crop classification and area estimation
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