<b>AI-DRIVEN DIGITAL TRANSFORMATION IN AGRICULTURE: OPPORTUNITIES, CHALLENGES AND FUTURE DIRECTIONS</b>

Engineering notes

Key topics: autonomous driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

The rapid development of artificial intelligence (AI) and digital technologies is driving the transformation in the world of modern agriculture as the new era of data-driven and smart agriculture is emerging. The current review is a compilation of recent papers on AI-driven digital transformation of agriculture and food industries with specific focus on technologies, such as machine learning (ML), deep learning (DL), computer vision, Internet of Things (IoT), big data analytics, and more. The technologies allow real-time monitoring, predictive modelling and intelligent decision support systems to predict crop productivity, soil health, diseases and pests and resource management. In addition, using robotics, autonomous systems and unmanned aerial vehicles (UAVs) is helpful to enhance the efficiency and reduce human involvement. While such initiatives have taken place, there remains a number of challenges such as lack of uniformity and interoperability, cost of investment, cybersecurity and the digital divide, particularly in the developing world. The review also highlights the importance of explainable AI (XAI) solutions, cloud computing and edge computing paradigms in promoting transparency, scalability and acceptance of smart farming tools. Furthermore, emerging technologies (blockchain and digital twins) are explored for improving supply chain transparency and resiliency. The research paper concludes with the need to identify future research opportunities, including the need to build sustainable, scalable and inclusive AI-powered agricultural ecosystems to comply with climate-smart agriculture and global food-security goals.

Links and sources

Comments