The surge in population growth coupled with the diminishing availability of cultivable land has significantly heightened the strain on arable areas. While the provision of sustenance for the underprivileged remains a pressing issue, the focus has expanded beyond mere food provision to ensuring a nutritionally balanced diet. Consequently, there arises an urgent imperative to delve into contemporary methodologies aimed at not only feeding the burgeoning population but also preserving essential food reserves for the future, mitigating the impacts of water scarcity, and grappling with the shrinking expanses of cultivable land. Moreover, the emergence of a new demographic comprising vegans necessitates further advancements to meet their distinct dietary needs, which are divergent from conventional dietary preferences. In summary, to adhere to the nutritional guidelines set forth by organizations such as the World Health Organization and ensure access to a healthy diet for the expanding populace, the adoption of novel technologies becomes indispensable in the quest to eliminate the specter of global famine.

Keywords:Agriculture, technology, convolutional neural network (CNN), machine learning (ML), CV

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1. Encyclopedia Britannica | Britannica. In: Encyclopædia Britannica. 2024. Available at https://www.britannica.com/
2. Indian Council of Agricultural Research. DARE-ICAR Significant Achievements (2014–2021). New Delhi, India: Indian Council of Agricultural Research; 2021.
3. Pathak H. Greenhouse gas emission from Indian agriculture: trends, drivers and mitigation strategies. Proc Indian Natl Sci Acad. 2015; 81 (5): 1133–1149.
4. Imperial Gazetteer of India. Imperial Gazetteer of India, The Indian Empire, Economic, Published under the authority of His Majesty’s Secretary of State for India in Council, Oxford at the Clarendon Press; 1907.
5. Mohapatra T, Raut PK. Indian agriculture – a journey from begging bowl to sustainable food security. Sci Reporter. 2021; 58: 63–69.
6. Pathak H, Ayyappan S. Sustainable agriculture in a changing world. Curr Sci. 2020; 119 (11): 1731–1732.
7. Shyam R, Singh R. A taxonomy of machine learning techniques. J Adv Robotics. 2021; 8 (3): 18–25.
8. Economics of Climate Adaptation. Shaping Climate-Resilient Development: A Framework for Decision-Making. A Report of the Economics of Climate Adaptation Working Group. Climate Works Foundation (CWF), Global Environment Facility (GEF), European Commission (EC), McKinsey & Company (MC), The Rockefeller Foundation (TRF), Standard Chartered Bank (SCB), and Swiss Re (SR). 2009.
9. Intergovernmental Panel on Climate Change (IPCC). Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, Switzerland: IPCC; 2021.
10. Shyam R. Convolutional neural network and its architectures. J Computer Technol Appl. 2021; 12(2): 6–14.
11. Wikipedia Contributors. World Population. [Online]. Wikipedia. Wikimedia Foundation. 2024. Available at https://en.wikipedia.org/wiki/World_population.

The surge in population growth coupled with the diminishing availability of cultivable land has significantly heightened the strain on arable areas. While the provision of sustenance for the underprivileged remains a pressing issue, the focus has expanded beyond mere food provision to ensuring a nutritionally balanced diet. Consequently, there arises an urgent imperative to delve into contemporary methodologies aimed at not only feeding the burgeoning population but also preserving essential food reserves for the future, mitigating the impacts of water scarcity, and grappling with the shrinking expanses of cultivable land. Moreover, the emergence of a new demographic comprising vegans necessitates further advancements to meet their distinct dietary needs, which are divergent from conventional dietary preferences. In summary, to adhere to the nutritional guidelines set forth by organizations such as the World Health Organization and ensure access to a healthy diet for the expanding populace, the adoption of novel technologies becomes indispensable in the quest to eliminate the specter of global famine.

Keywords:Agriculture, technology, convolutional neural network (CNN), machine learning (ML), CV

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Encyclopedia Britannica | Britannica. In: Encyclopædia Britannica. 2024. Available at https://www.britannica.com/
2. Indian Council of Agricultural Research. DARE-ICAR Significant Achievements (2014–2021). New Delhi, India: Indian Council of Agricultural Research; 2021.
3. Pathak H. Greenhouse gas emission from Indian agriculture: trends, drivers and mitigation strategies. Proc Indian Natl Sci Acad. 2015; 81 (5): 1133–1149.
4. Imperial Gazetteer of India. Imperial Gazetteer of India, The Indian Empire, Economic, Published under the authority of His Majesty’s Secretary of State for India in Council, Oxford at the Clarendon Press; 1907.
5. Mohapatra T, Raut PK. Indian agriculture – a journey from begging bowl to sustainable food security. Sci Reporter. 2021; 58: 63–69.
6. Pathak H, Ayyappan S. Sustainable agriculture in a changing world. Curr Sci. 2020; 119 (11): 1731–1732.
7. Shyam R, Singh R. A taxonomy of machine learning techniques. J Adv Robotics. 2021; 8 (3): 18–25.
8. Economics of Climate Adaptation. Shaping Climate-Resilient Development: A Framework for Decision-Making. A Report of the Economics of Climate Adaptation Working Group. Climate Works Foundation (CWF), Global Environment Facility (GEF), European Commission (EC), McKinsey & Company (MC), The Rockefeller Foundation (TRF), Standard Chartered Bank (SCB), and Swiss Re (SR). 2009.
9. Intergovernmental Panel on Climate Change (IPCC). Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, Switzerland: IPCC; 2021.
10. Shyam R. Convolutional neural network and its architectures. J Computer Technol Appl. 2021; 12(2): 6–14.
11. Wikipedia Contributors. World Population. [Online]. Wikipedia. Wikimedia Foundation. 2024. Available at https://en.wikipedia.org/wiki/World_population.