» Articles » PMID: 31178875

Single-Shot Convolution Neural Networks for Real-Time Fruit Detection Within the Tree

Overview
Journal Front Plant Sci
Date 2019 Jun 11
PMID 31178875
Citations 26
Authors
Affiliations
Soon will be listed here.
Abstract

Highlights: Using new convolutional deep learning techniques based on single-shot detectors to detect and count fruits (apple and pear) within the tree canopy.

Citing Articles

An Evaluation of Multi-Channel Sensors and Density Estimation Learning for Detecting Fire Blight Disease in Pear Orchards.

Veres M, Tarry C, Grigg-McGuffin K, McFadden-Smith W, Moussa M Sensors (Basel). 2024; 24(16).

PMID: 39205081 PMC: 11359518. DOI: 10.3390/s24165387.


Accurate and fast detection of tomatoes based on improved YOLOv5s in natural environments.

Touko Mbouembe P, Liu G, Park S, Kim J Front Plant Sci. 2024; 14:1292766.

PMID: 38273960 PMC: 10808679. DOI: 10.3389/fpls.2023.1292766.


Yeast cell detection using fuzzy automatic contrast enhancement (FACE) and you only look once (YOLO).

Huang Z, Patel B, Lu W, Yang T, Tung W, Bucinskas V Sci Rep. 2023; 13(1):16222.

PMID: 37758830 PMC: 10533879. DOI: 10.1038/s41598-023-43452-9.


Standardizing and Centralizing Datasets for Efficient Training of Agricultural Deep Learning Models.

Joshi A, Guevara D, Earles M Plant Phenomics. 2023; 5:0084.

PMID: 37680999 PMC: 10482164. DOI: 10.34133/plantphenomics.0084.


FCOS-LSC: A Novel Model for Green Fruit Detection in a Complex Orchard Environment.

Zhao R, Guan Y, Lu Y, Ji Z, Yin X, Jia W Plant Phenomics. 2023; 5:0069.

PMID: 37475967 PMC: 10355323. DOI: 10.34133/plantphenomics.0069.


References
1.
Sa I, Ge Z, Dayoub F, Upcroft B, Perez T, McCool C . DeepFruits: A Fruit Detection System Using Deep Neural Networks. Sensors (Basel). 2016; 16(8). PMC: 5017387. DOI: 10.3390/s16081222. View

2.
Shalloo L, O Donovan M, Leso L, Werner J, Ruelle E, Geoghegan A . Review: Grass-based dairy systems, data and precision technologies. Animal. 2018; 12(s2):s262-s271. DOI: 10.1017/S175173111800246X. View

3.
LeCun Y, Bengio Y, Hinton G . Deep learning. Nature. 2015; 521(7553):436-44. DOI: 10.1038/nature14539. View

4.
Lee Y, Rhodes W . Nonlinear image processing by a rotating kernel transformation. Opt Lett. 2009; 15(23):1383-5. DOI: 10.1364/ol.15.001383. View

5.
Rahnemoonfar M, Sheppard C . Deep Count: Fruit Counting Based on Deep Simulated Learning. Sensors (Basel). 2017; 17(4). PMC: 5426829. DOI: 10.3390/s17040905. View