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Feeding the Future: Advancements in Deep Learning for Sustainable Agriculture and Food Security
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Edited by Prof. Yonis Gulzar & Zeynep Ünal

"Feeding the Future: Advancements in Deep Learning for Sustainable Agriculture and Food Security" presents a comprehensive exploration of the transformative role that deep learning technologies play in addressing the pressing challenges of global food security and sustainable agriculture. With the world population projected to reach nearly 10 billion by 2050, ensuring food security while minimizing environmental impact has become an urgent priority.

This collection delves into the innovative applications of deep learning algorithms, neural networks, and artificial intelligence techniques in optimizing agricultural processes, enhancing crop yield, and mitigating the impact of climate change on food production. From precision agriculture and crop monitoring to pest detection and yield prediction, the collection showcases cutting-edge research and practical implementations that revolutionize farming practices.

Contributions from leading experts in the fields of computer science, agriculture, and environmental science provide insights into the latest advancements, challenges, and opportunities in harnessing deep learning for sustainable food production. Topics covered include remote sensing, image analysis, crop disease identification, soil health assessment, and adaptive farming systems.

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