ArticleFrontiers in plant science2018
Deep Learning: Individual Maize Segmentation From Terrestrial Lidar Data Using Faster R-CNN and Regional Growth Algorithms.
Article in Frontiers in plant science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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40 citing papers in PubMed.
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- SOY3DSEG: A high-precision universal point cloud segmentation model for soybean full growth period based on improved point transformer.Plant phenomics (Washington, D.C.) · 2026Article
- Leaf bidirectional reflectance distribution function (BRDF) prediction with phenotypic traits in four species: Development of a novel measuring and analyzing framework.Plant phenomics (Washington, D.C.) · 2025Article
- Deep learning for three-dimensional (3D) plant phenomics.Plant phenomics (Washington, D.C.) · 2025Review
- A review on computer-aided diagnostic system to classify the disorders of the gastrointestinal tract.European journal of medical research · 2025Review
- Deep Ordinal Classification in Forest Areas Using Light Detection and Ranging Point Clouds.Sensors (Basel, Switzerland) · 2024Article
- MTSC-Net: A Semi-Supervised Counting Network for Estimating the Number of Slash pine New Shoots.Plant phenomics (Washington, D.C.) · 2024Article
- SCAG: A Stratified, Clustered, and Growing-Based Algorithm for Soybean Branch Angle Extraction and Ideal Plant Architecture Evaluation.Plant phenomics (Washington, D.C.) · 2024Article
- Accurate LAI estimation of soybean plants in the field using deep learning and clustering algorithms.Frontiers in plant science · 2024Article
- RAAWC-UNet: an apple leaf and disease segmentation method based on residual attention and atrous spatial pyramid pooling improved UNet with weight compression loss.Frontiers in plant science · 2024Article
- Crop growth dynamics: Fast automatic analysis of LiDAR images in field-plot experiments by specialized software ALFA.PloS one · 2024Article
- How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques.Plant methods · 2023Review
- Automatic Branch-Leaf Segmentation and Leaf Phenotypic Parameter Estimation of Pear Trees Based on Three-Dimensional Point Clouds.Sensors (Basel, Switzerland) · 2023Article
- Field-measured canopy height may not be as accurate and heritable as believed: evidence from advanced 3D sensing.Plant methods · 2023Article
- A Dual-Modality Imaging Method Based on Polarimetry and Second Harmonic Generation for Characterization and Evaluation of Skin Tissue Structures.International journal of molecular sciences · 2023Article
- Whole-genome resequencing reveals genetic differences and the genetic basis of parapodium number in Russian and Chinese Apostichopus japonicus.BMC genomics · 2023Article
- WheatSpikeNet: an improved wheat spike segmentation model for accurate estimation from field imaging.Frontiers in plant science · 2023Article
- DFSP: A fast and automatic distance field-based stem-leaf segmentation pipeline for point cloud of maize shoot.Frontiers in plant science · 2023Article
- Multi-Source Data Fusion Improves Time-Series Phenotype Accuracy in Maize under a Field High-Throughput Phenotyping Platform.Plant phenomics (Washington, D.C.) · 2023Article
- MASPC_Transform: A Plant Point Cloud Segmentation Network Based on Multi-Head Attention Separation and Position Code.Sensors (Basel, Switzerland) · 2022Article
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12 authors.
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Abstract
The rapid development of light detection and ranging (Lidar) provides a promising way to obtain three-dimensional (3D) phenotype traits with its high ability of recording accurate 3D laser points. Recently, Lidar has been widely used to obtain phenotype data in the greenhouse and field with along other sensors. Individual maize segmentation is the prerequisite for high throughput phenotype data extraction at individual crop or leaf level, which is still a huge challenge. Deep learning, a state-of-the-art machine learning method, has shown high performance in object detection, classification, and segmentation. In this study, we proposed a method to combine deep leaning and regional growth algorithms to segment individual maize from terrestrial Lidar data. The scanned 3D points of the training site were sliced row and row with a fixed 3D window. Points within the window were compressed into deep images, which were used to train the Faster R-CNN (region-based convolutional neural network) model to learn the ability of detecting maize stem. Three sites of different planting densities were used to test the method. Each site was also sliced into many 3D windows, and the testing deep images were generated. The detected stem in the testing images can be mapped into 3D points, which were used as seed points for the regional growth algorithm to grow individual maize from bottom to up. The results showed that the method combing deep leaning and regional growth algorithms was promising in individual maize segmentation, and the values of r, p, and F of the three testing sites with different planting density were all over 0.9. Moreover, the height of the truly segmented maize was highly correlated to the manually measured height (
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