Deep Learning for Ensuring Food Security in Agriculture: An In-Depth Exploration of Innovations and Challenges
Keywords:
Agriculture, Crop Disease Diagnosis, Deep Learning, Food Security, Precision AgricultureAbstract
Ensuring food security in agriculture has become an increasingly critical challenge amid a growing global population and changing climatic conditions. Deep Learning, a subset of artificial intelligence, has emerged as a promising technology to address these pressing issues in agriculture. This research presents a comprehensive exploration of the potential of Deep Learning in revolutionizing agricultural practices to enhance food security. The study delves into various applications, including crop yield prediction, pest detection and control, crop disease diagnosis, and precision agriculture. A Convolutional Neural Network (CNN) based model is proposed as an example to showcase the transformative power of Deep Learning in crop disease diagnosis. The research discusses the innovations, challenges, and opportunities of integrating Deep Learning algorithms into agricultural systems. Data availability, computational resources, and model interpretability emerged as key challenges. Despite the hurdles, the research highlights the significant potential of Deep Learning to improve food security through increased agricultural productivity, resource optimization, and sustainable farming practices. Policy recommendations and public-private partnerships are proposed to facilitate the adoption of Deep Learning solutions in agriculture. By understanding the innovations and challenges, this research contributes to the ongoing efforts to ensure sustainable food production and meet the demands of the future.
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Copyright (c) 2023 Rudakova Brown Shapiro, Levin Zhao Cashore, Wang Shaikh Yuan
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