[go: up one dir, main page]

Li et al., 2025 - Google Patents

UAV image analysis for detecting rice seedling gaps and gap effect on grain yield

Li et al., 2025

View HTML
Document ID
7607857324606335401
Author
Li S
Yang Y
Zhang J
Wilson L
Samonte S
Dou F
Bera T
Zhou X
Sanchez D
Wang J
Publication year
Publication venue
Smart Agricultural Technology

External Links

Snippet

The spatial distribution of seedlings influences the structure and productivity of rice by impacting canopy light distribution, radiation use efficiency, and ultimately crop yield. This study proposes a novel approach utilizing high-resolution UAV images to detect seedling …
Continue reading at www.sciencedirect.com (HTML) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
    • G06K9/00657—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas of vegetation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
    • G06K9/00147—Matching; Classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
    • G06K9/0014—Pre-processing, e.g. image segmentation ; Feature extraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01H—NEW PLANTS OR PROCESSES FOR OBTAINING THEM; PLANT REPRODUCTION BY TISSUE CULTURE TECHNIQUES
    • A01H5/00—Flowering plants, i.e. angiosperms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details

Similar Documents

Publication Publication Date Title
Vong et al. Early corn stand count of different cropping systems using UAV-imagery and deep learning
Liao et al. On precisely relating the growth of Phalaenopsis leaves to greenhouse environmental factors by using an IoT-based monitoring system
Jiang et al. DeepFlower: a deep learning-based approach to characterize flowering patterns of cotton plants in the field
Anderegg et al. On-farm evaluation of UAV-based aerial imagery for season-long weed monitoring under contrasting management and pedoclimatic conditions in wheat
Dreccer et al. Yielding to the image: How phenotyping reproductive growth can assist crop improvement and production
Vong et al. Corn emergence uniformity estimation and mapping using UAV imagery and deep learning
Jiang et al. Detection of maize drought based on texture and morphological features
Ma et al. Automatic detection of crop root rows in paddy fields based on straight-line clustering algorithm and supervised learning method
Choudhury et al. Automated vegetative stage phenotyping analysis of maize plants using visible light images
Li et al. An automatic approach for detecting seedlings per hill of machine-transplanted hybrid rice utilizing machine vision
Lootens et al. High-throughput phenotyping of lateral expansion and regrowth of spaced Lolium perenne plants using on-field image analysis
CN107680098A (en) A kind of recognition methods of sugarcane sugarcane section feature
CN102663396B (en) Method for automatically detecting rice milky ripe stage
Vamerali et al. An approach to minirhizotron root image analysis
Bai et al. Estimating leaf age of maize seedlings using UAV-based RGB and multispectral images
Chakraborty et al. Early almond yield forecasting by bloom mapping using aerial imagery and deep learning
Mangla et al. Statistical growth prediction analysis of rice crop with pixel-based mapping technique
Zhang et al. Assessing spatio-temporal patterns of sugarcane aphid (Hemiptera: Aphididae) infestations on silage sorghum yield using unmanned aerial systems (UAS)
Zhu et al. Exploring soybean flower and pod variation patterns during reproductive period based on fusion deep learning
Maldaner et al. Spatial–temporal analysis to investigate the influence of in-row plant spacing on the sugarcane yield
Debnath et al. Optimal weighted GAN and U-Net based segmentation for phenotypic trait estimation of crops using Taylor Coot algorithm
Romero et al. Heading and maturity date prediction using vegetation indices: A case study using bread wheat, barley and oat crops
CN116168292A (en) Method and system for judging soybean seedling uniformity
Sun et al. Evaluation of growth recovery grade in lodging maize via UAV-based hyperspectral images
Zheng et al. Strawberry canopy structural parameters estimation and growth analysis from UAV multispectral imagery using a geospatial tool