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Fengdeng SeedLLM 1.0 is a professional large language model developed by Yazhou Bay National Laboratory, trained on extensive agricultural literature, crop data, and domain knowledge. The model demonstrates exceptional performance in agricultural Q&A, crop analysis, and pest identification tasks, achieving over 85% accuracy. The model is released as open source, supporting free use by research institutions and enterprises.
The system leverages Fengdeng SeedLLM's natural language understanding capabilities, combined with computer vision technology, to rapidly diagnose crop pest and disease issues through farmer descriptions and image recognition, providing targeted solutions. Pilot applications show diagnostic accuracy exceeding 90%, substantially reducing the workload of agricultural technical personnel.
This collaboration will encompass deep cooperation in agricultural talent cultivation, research project partnerships, and technology transfer. Partner universities will gain priority access to Fengdeng SeedLLM and participate in continuous model optimization and application scenario expansion. The initiative is expected to benefit over 100,000 agricultural students and faculty.
The latest version of Fengdeng SeedLLM integrates advanced multimodal fusion technology, enabling comprehensive analysis of soil data, meteorological information, crop images, and farmer feedback to provide more precise agricultural recommendations. This technology has broad application prospects in precision agriculture and smart farming scenarios.
The open source community has received over 100 pull requests covering model optimization, application cases, and documentation improvements. Community members have developed multiple agricultural application plugins based on Fengdeng SeedLLM, including intelligent Q&A robots, crop management assistants, and agricultural knowledge bases, greatly enriching the model's application scenarios.
Through pilot collaborations with agricultural departments across regions, Fengdeng SeedLLM has played important roles in smart agriculture, agricultural product traceability, and agricultural policy interpretation. The model not only improves agricultural production efficiency but also provides convenient technical consulting services for farmers, effectively bridging the digital divide and promoting the widespread application of agricultural technology.