Wenli Zhang , Chao Zheng , Chenhuizi Wang , Pieter M. Blok , Haozhou Wang , Wei Guo
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引用次数: 0
Abstract
The measurement of phenotypic parameters of fresh grapes, especially at the individual berry level, is critical for yield estimation and quality control. Currently, these measurements are done by humans, making it costly, labor-intensive, and often inaccurate. Advances in 3D reconstruction and point cloud analysis allow extraction of detailed traits for grapes, yet current methods struggle incomplete point clouds due to occlusion. This study presents a novel deep-learning-based phenotyping pipeline designed specifically for 3D point cloud data. First, individual berries are segmented from the grape bunch using the SoftGroup deep learning network. Next, a self-supervised point cloud completion network, termed GrapeCPNet, addresses occlusions by completing missing areas. Finally, morphological analyses are applied to extract berry radius and volumes. Validation on a dataset of four fresh grape varieties yielded values of 85.5% for berry radius and 96.9% for berry volume, respectively. These results demonstrate the potential of the proposed method for rapid and practical extraction of 3D phenotypic traits in grape cultivation.
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.