With the AIE-KN42 and the NVIDIA TensorRT SDK, mango leaves are analyzed directly in the field, giving farmers instant diagnostics without laboratory testing.
Manually identifying diseases in crops is costly and subjective, highlighting the need for automated systems that ensure accurate detection in the field.
Integrating AIoT and AI vision inspection offers an innovative solution for on-site leaf analysis. This approach enhances the ability to assess crop health and detect leaf diseases, ultimately maximizing both the yield and quality of crops.
Developed in collaboration with an Independent Software Vendor (ISV) partner, this solution leverages Aetina's AIE-KN42 edge AI computing platform and the NVIDIA TensorRT SDK to deliver instant diagnostics of mango leaf health. Using advanced edge AI vision technology, it analyzes leaves directly in the field, eliminating the need for laboratory testing and providing farmers with immediate, actionable insights.
Trained on a comprehensive dataset, the system detects diseases and health issues with precision, enabling timely interventions and better crop management. The AIE-KN42 offers exceptional edge computing performance, eliminating cloud reliance and ensuring real-time, low-latency processing. NVIDIA TensorRT optimizes deep learning models for fast, high-performance inference.
This approach can be adapted to other crops with additional training, offering a versatile solution for broader agricultural applications.
Aetina · NVIDIA Jetson Orin NX / Nano · Super Mode
A fan-based edge AI system that enables Super Mode with JetPack 6.2: 2X generative AI model performance and 50% higher memory bandwidth, for sensor fusion and precise positioning.
View ProductWe plan Aetina AI systems together with POLYRACK enclosures, our thermal management partners and Innodisk storage, memory and cameras as one project.
A palm-sized fanless Mini system, a GPU-expansion workstation or a 2U server: we choose the platform with you based on what the application needs.
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