A Cloud-Based Environment for Generating Yield Estimation Maps From Apple Orchards Using UAV Imagery and a Deep Learning Technique
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A Cloud-Based Environment for Generating Yield Estimation Maps From Apple Orchards Using UAV Imagery and a Deep Learning Technique
Authors
Keywords
-
Journal
Frontiers in Plant Science
Volume 11, Issue -, Pages -
Publisher
Frontiers Media SA
Online
2020-07-16
DOI
10.3389/fpls.2020.01086
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Deep learning for real-time fruit detection and orchard fruit load estimation: benchmarking of ‘MangoYOLO’
- (2019) A. Koirala et al. PRECISION AGRICULTURE
- Apple detection during different growth stages in orchards using the improved YOLO-V3 model
- (2019) Yunong Tian et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Optimizing Crop Load for New Apple Cultivar: “WA38”
- (2019) Brendon Anthony et al. Agronomy-Basel
- Detection of Apple Lesions in Orchards Based on Deep Learning Methods of CycleGAN and YOLOV3-Dense
- (2019) Yunong Tian et al. Journal of Sensors
- Strawberry Yield Prediction Based on a Deep Neural Network Using High-Resolution Aerial Orthoimages
- (2019) Yang Chen et al. Remote Sensing
- Vision-based preharvest yield mapping for apple orchards
- (2019) Pravakar Roy et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- JupyTEP IDE as an Online Tool for Earth Observation Data Processing
- (2019) Rapiński et al. Remote Sensing
- Deep learning based banana plant detection and counting using high-resolution red-green-blue (RGB) images collected from unmanned aerial vehicle (UAV)
- (2019) Bipul Neupane et al. PLoS One
- CropGIS – A web application for the spatial and temporal visualization of past, present and future crop biomass development
- (2018) Miriam Machwitz et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Deep learning in agriculture: A survey
- (2018) Andreas Kamilaris et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review
- (2018) Anna Chlingaryan et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Apple flower detection using deep convolutional networks
- (2018) Philipe A. Dias et al. COMPUTERS IN INDUSTRY
- Recent advances in convolutional neural networks
- (2018) Jiuxiang Gu et al. PATTERN RECOGNITION
- Using Multi-Spectral UAV Imagery to Extract Tree Crop Structural Properties and Assess Pruning Effects
- (2018) Kasper Johansen et al. Remote Sensing
- Deep Learning for Computer Vision: A Brief Review
- (2018) Athanasios Voulodimos et al. Computational Intelligence and Neuroscience
- Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review
- (2018) Diego Inácio Patrício et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Research and development in agricultural robotics: A perspective of digital farming
- (2018) Redmond Ramin Shamshiri et al. International Journal of Agricultural and Biological Engineering
- Mapping the 3D structure of almond trees using UAV acquired photogrammetric point clouds and object-based image analysis
- (2018) Jorge Torres-Sánchez et al. BIOSYSTEMS ENGINEERING
- Big GIS analytics framework for agriculture supply chains: A literature review identifying the current trends and future perspectives
- (2018) Rohit Sharma et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture
- (2018) Wouter H. Maes et al. TRENDS IN PLANT SCIENCE
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- (2017) Shaoqing Ren et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards
- (2017) Suchet Bargoti et al. Journal of Field Robotics
- Deep Count: Fruit Counting Based on Deep Simulated Learning
- (2017) et al. SENSORS
- Detection of tomatoes using spectral-spatial methods in remotely sensed RGB images captured by UAV
- (2016) J. Senthilnath et al. BIOSYSTEMS ENGINEERING
- Open-source GIS application for UAV photogrammetry based on MicMac
- (2016) L. Duarte et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Deep learning for visual understanding: A review
- (2016) Yanming Guo et al. NEUROCOMPUTING
- Image Based Mango Fruit Detection, Localisation and Yield Estimation Using Multiple View Geometry
- (2016) Madeleine Stein et al. SENSORS
- A Hierarchical Approach to Apple Identification for Robotic Harvesting
- (2016) Transactions of the ASABE
- Sensors and systems for fruit detection and localization: A review
- (2015) A. Gongal et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Shake Table Test of Large-Scale Bridge Columns Supported on Rocking Shallow Foundations
- (2015) Grigorios Antonellis et al. JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING
- Deep learning
- (2015) Yann LeCun et al. NATURE
- A Robust Photogrammetric Processing Method of Low-Altitude UAV Images
- (2015) Mingyao Ai et al. Remote Sensing
- High-Throughput 3-D Monitoring of Agricultural-Tree Plantations with Unmanned Aerial Vehicle (UAV) Technology
- (2015) Jorge Torres-Sánchez et al. PLoS One
- Estimating mango crop yield using image analysis using fruit at ‘stone hardening’ stage and night time imaging
- (2013) A. Payne et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Farm management systems and the Future Internet era
- (2012) Alexandros Kaloxylos et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Delineation of management zones in an apple orchard in Greece using a multivariate approach
- (2012) Katerina Aggelopooulou et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started