[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Moupojou et al., 2023 - Google Patents

FieldPlant: A dataset of field plant images for plant disease detection and classification with deep learning

Moupojou et al., 2023

View PDF
Document ID
18317035054956873090
Author
Moupojou E
Tagne A
Retraint F
Tadonkemwa A
Wilfried D
Tapamo H
Nkenlifack M
Publication year
Publication venue
IEEE Access

External Links

Snippet

The Food and Agriculture Organization of the United Nations suggests increasing the food supply by 70% to feed the world population by 2050, although approximately one third of all food is wasted because of plant diseases or disorders. To achieve this goal, researchers …
Continue reading at ieeexplore.ieee.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30587—Details of specialised database models
    • G06F17/30595—Relational databases
    • G06F17/30598—Clustering or classification
    • G06F17/30601—Clustering or classification including cluster or class visualization or browsing
    • 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
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • 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
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30244—Information retrieval; Database structures therefor; File system structures therefor in image databases
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass

Similar Documents

Publication Publication Date Title
Moupojou et al. FieldPlant: A dataset of field plant images for plant disease detection and classification with deep learning
Liu et al. Tomato diseases and pests detection based on improved Yolo V3 convolutional neural network
Naranjo-Torres et al. A review of convolutional neural network applied to fruit image processing
Jin et al. A novel deep learning‐based method for detection of weeds in vegetables
Boulent et al. Convolutional neural networks for the automatic identification of plant diseases
Li et al. Semi-supervised few-shot learning approach for plant diseases recognition
Altalak et al. Smart agriculture applications using deep learning technologies: A survey
Sladojevic et al. Deep neural networks based recognition of plant diseases by leaf image classification
Teixeira et al. A systematic review on automatic insect detection using deep learning
Mishra et al. A Deep Learning-Based Novel Approach for Weed Growth Estimation.
Ma et al. Detection and counting of small target apples under complicated environments by using improved YOLOv7-tiny
Badgujar et al. Agricultural object detection with You Only Look Once (YOLO) Algorithm: A bibliometric and systematic literature review
Razmjooy et al. Applications of image processing and soft computing systems in agriculture
Sheng et al. Rice growth stage classification via RF-based machine learning and image processing
Li et al. YOLOv5-ASFF: A Multistage Strawberry Detection Algorithm Based on Improved YOLOv5
Fang et al. Using channel and network layer pruning based on deep learning for real-time detection of ginger images
López-Barrios et al. Green sweet pepper fruit and peduncle detection using mask R-CNN in greenhouses
Hamidon et al. Detection of tip-burn stress on lettuce grown in an indoor environment using deep learning algorithms
Liu et al. A dataset for forestry pest identification
Bonkra et al. Scientific landscape and the road ahead for deep learning: apple leaves disease detection
Jin et al. Hydroponic lettuce defective leaves identification based on improved YOLOv5s
Bai et al. T‐YOLO: a lightweight and efficient detection model for nutrient buds in complex tea‐plantation environments
Sheng et al. Disease diagnostic method based on cascade backbone network for apple leaf disease classification
Yang et al. FCBTYOLO: A Lightweight and High-Performance Fine Grain Detection Strategy for Rice Pests
Peng et al. Application of machine vision technology in citrus production