Three-Dimensional Imaging and Deep Learning for Agricultural Applications

Dear Colleagues,

Sustainable agriculture increasingly relies on precise phenotypic data to accelerate crop breeding, optimize management practices, and enhance resource use efficiency. While 2D imaging struggles to capture structural complexity, 3D imaging (e.g., LiDAR and stereo vision) offers critical depth information, but it faces challenges in data preprocessing (denoising and registration), analysis (segmentation and fusion), and biological interpretation. Deep learning, leveraging architectures like 3D convolutional neural networks (CNNs) and point cloud transformers, enables automated processing of complex 3D datasets to precisely segment plant organs and extract fine-grained traits such as canopy structure, root morphology, and biomass distribution. By learning hierarchical features directly from raw 3D inputs, it eliminates reliance on manual feature engineering, facilitating scalable and robust extraction of phenotypically meaningful data that supports critical agricultural applications, including accelerated crop breeding, optimized resource allocation, and dynamic growth monitoring, thus effectively bridging technical innovations in 3D imaging with practical agricultural needs.

This Special Issue aims to showcase innovative research on integrating 3D imaging and deep learning in crop phenotyping, exploring applications in trait extraction, dynamic growth monitoring, and physiological inversion. Articles may focus on but are not limited to the following topics:

• Three-dimensional data processing (reconstruction, denoising, and segmentation);
• Three-dimensional morphological trait extraction;
• Multimodal data fusion;
• Crop stress detection;
• Biomass estimation;
• Dynamic growth modeling;
• Smart breeding.

Dr. Ziran Ye
Dr. Dedong Kong
Dr. Qing Gu
Guest Editors

Manuscript Submission Information

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