Generative AI for Smart Agri-Equipment Automation and Pharma-Crop Yield Prediction
Keywords:
Generative Artificial Intelligence,Smart Agricultural Automation,Precision Agriculture,Pharmaceutical Crop Yield Prediction,Machine Learning in Farming,Autonomous Agricultural Equipment,AI-Based Crop MonitoringPredictive Analytics in Agriculture,Intelligent Farming Systems,Sustainable Agricultural Technologies.Abstract
Generative AI shows promise in automating intelligent field equipment, enhancing the efficiency of physical work in agriculture. Current research in agricultural machinery automation, encompassing both field equipment and robotic systems, has witnessed groundbreaking advancements aimed at addressing the increasing labor shortage in agriculture. Additionally, autonomous vehicle research has achieved considerable deployment within government and industrial robotics programs. Farm machinery automation primarily employs symbiotic, classical, task-oriented AI techniques, while robotic harvesting and retrieval remains application-specific. These systems rely heavily on robust and specialized sensors to support perception, state estimation, mapping, planning, and control for real-time operation in unstructured outdoor environments.
The second area influenced by GAI is pharmaceutical crop yield prediction. Yield forecasting systems tailored to the pharmaceutical supply chain for cannabis and hemp production have recently emerged. These systems leverage novel generative AI techniques for modelling crop growth and inferring phenotypic characteristics through data fusion involving multispectral and hyperspectral imaging, alongside 2D, 3D, and 4D imaging. A scalable system architecture for GAI implementation in agricultural applications has been presented, addressing aspects related to data pipelines, associated sensor infrastructure, and an edge device network for reducing latency, bandwidth requirements, and privacy concerns.
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