Dr. Jiahao Fan is a highly accomplished researcher whose work lies at the intersection of artificial intelligence, computer vision, and biological systems engineering. His professional journey reflects a commitment to developing innovative, data-driven technologies that enhance agricultural productivity and environmental sustainability. He completed his advanced studies in Biological Systems Engineering with a focus on integrating machine learning and computational modeling into precision agriculture. His educational background also includes strong foundations in machine learning, data mining, and remote sensing, equipping him with the expertise to address complex, multidisciplinary challenges in modern biological sciences. Throughout his research career, Dr. Jiahao Fan has contributed to the advancement of multimodal deep learning frameworks, vision-language modeling, and sensor-based data fusion for agricultural applications. His pioneering projects include developing object detection and segmentation systems for plant monitoring, multimodal feature fusion methods for crop analysis, and efficient retrieval-based prediction algorithms that optimize computational performance in data-limited environments. His academic work demonstrates a deep understanding of artificial intelligence applied to biological processes, leading to several peer-reviewed publications and open-source innovations that support sustainable farming systems. Dr. Jiahao Fan’s research is characterized by technical precision, interdisciplinary collaboration, and a vision to bridge computational sciences with real-world agricultural needs. His efforts have earned notable recognition within the research community, as reflected in his 476 citations by 474 documents, 6 published works, and an h-index of 5, highlighting the scholarly influence and practical value of his contributions. Through his continuous pursuit of excellence in both theory and application, Dr. Jiahao Fan remains dedicated to advancing the frontiers of agricultural automation, digital phenotyping, and sustainable resource management. His work not only enriches the academic discourse in agricultural and biological sciences but also lays the groundwork for future innovations that integrate artificial intelligence with global food security and environmental resilience.

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Featured Publications:

• Fan, J.(2022). Estimation of maize yield and flowering time using multi-temporal UAV-based hyperspectral data. Remote Sensing, 14(13), 3052.

• Fan, J.(2023). Enhancing maize silage quality assessment through UAV-based high-throughput phenotyping and deep transfer learning. In AGU Fall Meeting, San Francisco.

• Fan, J.(2023). Uncrewed aerial vehicle (UAV)-based high‐throughput phenotyping of maize silage yield and nutritive values using multi-sensory feature fusion and multi-task learning with attention mechanism. Remote Sensing, 17(21), 3654.

• Fan, J.(2025). Mitigating NDVI saturation in imagery of dense and healthy vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 227,

• Fan, J.(2022). Multi-temporal estimation of maize yield and flowering time using UAV-based hyperspectral data. Proceedings of the North American Plant Phenotyping Network (NAPPN) Annual Conference.