The University of Tokyo · Environmental Science
Professor Fan Zhao's research lab specializes in advancing environmental monitoring and precision agriculture through the integration of unmanned aerial vehicles (UAVs), deep learning, and super-resolution reconstruction (SRR) techniques. The lab focuses on developing cost-effective, high-efficiency solutions for detecting underwater debris, monitoring aquatic ecosystems, and enabling large-scale, accurate plant monitoring in challenging environments such as turbid waters and low-light greenhouses. Key research directions include optimizing YOLO-based object detection models, designing novel SRR architectures like MambaIR, and applying these technologies to real-world ecological and agricultural challenges.
Figures are computed from collected data and may differ slightly.
Underwater litter is widely spread across aquatic environments such as lakes, rivers, and oceans, significantly impacting natural ecosystems. Current automated monitoring technologies for detecting this litter face limitations in survey efficiency, cost, and environmental conditions, highlighting the need for efficient, consumer-grade technologies for automatic detection. This research introduces the Aerial-Aquatic Speedy Scanner (AASS) combined with Super-Resolution Reconstruction (SRR) and an
Traditional detection and monitoring of seafloor debris present considerable challenges due to the high costs associated with underwater imaging devices and the complex environmental conditions in marine ecosystems. In response to these challenges, this field study conducted in Koh Tao, Thailand, proposed an innovative and cost-effective approach that leverages super-resolution reconstruction (SRR) technology in conjunction with an optimized object detection model based on YOLOv8. Super-resoluti
Recent advances in unmanned aerial vehicle (UAV) technology combined with deep learning techniques have greatly improved agricultural monitoring. However, accurately processing images at low resolutions remains challenging for precision cultivation of succulents. To address this issue, this study proposes a novel method that combines cutting-edge super-resolution reconstruction (SRR) techniques with object detection and then applies the above model in a unified drone framework to achieve large-s
Recent advances in unmanned aerial vehicles (UAVs) technology and deep learning have revolutionized agricultural monitoring, yet challenges remain in processing low-resolution field imagery for precision floriculture. Here, we presented an innovative approach combining state-of-the-art super-resolution reconstruction (SRR) and object detection for accurate rose growth monitoring in large-scale greenhouse environments. We introduced MambaIR, a novel SRR algorithm based on selective state-space mo
Abstract The use of traditional in situ methods for underwater surveys to map freshwater mussel habitats is limited by challenges such as water transparency, depth and high labour demands. In this study, adaptive resolution imaging sonar (ARIS) was applied to monitor mussel distribution and abundance. In contrast to conventional quadrat surveys, this acoustic survey is non‐invasive and enables direct observation of mussels to allow their survival status to be determined in turbid water. ARIS pro
Abstract Purpose Precise segmentation of blueberry maturity is critical for optimizing harvestschedules and maintaining product quality. Traditional methods, which rely on manualinspection, are not only labor-intensive but also cost-inefficient. This study presents a novelframework that integrates deep learning-based super-resolution reconstruction (SRR) withsemantic segmentation to provide a fast and accurate solution for maturity assessment. Methods The SRR module enhances image resolution, en
Freshwater mussels are essential for maintaining the balance and ecological integrity of aquatic environments. Continuous monitoring of mussel populations provides critical insights into ecosystem dynamics, and supports conservation and benthic resource management. However, traditional monitoring methods face challenges, such as water clarity issues, labour-intensive surveys, outdated technologies, incomplete datasets, and limited spatial coverage. In a field survey at Lake Izunuma, Japan, a new
Accurately detecting roses in UAV-captured greenhouse imagery presents significant challenges due to occlusions, scale variability, and complex environmental conditions. To address these issues, this study introduces ROSE-MAMBA-YOLO, a hybrid detection framework that combines the efficiency of YOLOv11 with Mamba-inspired state-space modeling to enhance feature extraction, multi-scale fusion, and contextual representation. The model achieves a mAP@50 of 87.5%, precision of 90.4%, and recall of 83
Hermit crabs are vital to coastal ecosystems, serving as environmental health indicators and contributing to seed dispersal, debris cleanup, and soil disturbance. Traditional hermit crabs survey methods, like quadrat sampling, are labor-intensive and environmentally dependent. This study presents an innovative approach combining UAV (Unmanned Aerial Vehicles)-based remote sensing with Super-Resolution Reconstruction (SRR) and the CRAB-YOLO detection network, a modification of YOLOv8s, to monitor
Accurate segmentation of crops and weeds is essential for enhancing crop yield, optimizing herbicide usage, and mitigating environmental impacts. Traditional weed management practices, such as manual weeding or broad-spectrum herbicide application, are labor-intensive, environmentally harmful, and economically inefficient. In response, this study introduces a novel precision agriculture framework integrating Unmanned Aerial Vehicle (UAV)-based remote sensing with advanced deep learning technique
Detecting underwater debris is important for monitoring the marine environment but remains challenging due to poor image quality, visual noise, object occlusions, and diverse debris appearances in underwater scenes. This study proposes UDD-YOLO, a novel detection framework that, for the first time, applies a diffusion-based model to underwater image enhancement, introducing a new paradigm for improving perceptual quality in marine vision tasks. Specifically, the proposed framework integrates thr
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