Agricultural Sustainability in the Age of Deep Learning: Current Trends, Challenges, and Future Trajectories

Abstract

Agriculture stands as the essential foundation of human sustenance, confronting the dual challenge of providing for a burgeoning global populace while safeguarding the integrity of the natural environment. This comprehensive review paper undertakes an exhaustive exploration of the continually evolving sphere of agricultural sustainability, traversing the multifaceted terrain of present-day trends, technological innovations, and the promising trajectories that lie ahead. From the vantage point of precision agriculture and climate-smart methodologies to the strategic integration of deep learning technologies, it offers a comprehensive examination of pioneering approaches that are redefining the agricultural domain. Within, it elucidates the intrinsic relationship between agriculture and sustainability, exemplifying how judicious resource management, the preservation of biodiversity, and the implementation of circular agricultural practices herald an epoch of conscientious agrarian practices. Moreover, this study casts an illuminative gaze toward the future of agriculture, wherein quantum intelligence, meta-learning, deep reinforcement learning, curriculum learning, intelligent nanothings, blockchain technology, and CRISPR gene editing converge to furnish innovative solutions. These solutions aspire to optimize crop yields, mitigate ecological footprint, and fortify global food security. As this academic voyage commences, it is incumbent to reiterate the pivotal assertion that sustainability in agriculture is not merely a desideratum; it is a compelling mandate, and the seeds of transformative innovation have been sown to recalibrate the world's approach to food production and environmental stewardship.