Wheat Head Detection Resources

AICrowd Global Wheat Challenge 2021 Solutions

AICrowd: Global Wheat Challenge 2021 solutions:

• 1st: randomTeamName https://github.com/ksnxr/GWC_solution
• 2nd: david_jeon https://drive.google.com/file/d/1YwntL8wod3ySOhLDT-SelU-EKYaGnCtc/view
• 3rd SMART https://www.swisstransfer.com/d/d19e2ddb-8fe3-47b8-943a-b6bfccfa623b (inactive)

Kaggle Global WHeat Detection 2020 Solution

Kaggle Global Wheat Detection 2020

1st place solution

https://www.kaggle.com/c/global-wheat-detection/discussion/172418

Summary

• Custom mosaic data augmentation
• MixUp
• Heavy augmentation
• Data cleaning
• EfficientDet
• Faster RCNN FPN
• Ensemble multi-scale model: Weighted-Boxes-Fusion, special thank @zfturbo
• Test time augmentation(HorizontalFlip, VerticalFlip, Rotate90)
• Pseudo labeling

2nd Place Solution

Link: https://www.kaggle.com/c/global-wheat-detection/discussion/175961

Repository: https://github.com/liaopeiyuan/TransferDet

3rd Place Solution

Code repository https://github.com/ufownl/global-wheat-detection

• YOLOv3 from GluonCV
• Use Darknet53 backbone
• Use WBF over TTA
• Use pseudo-labeling technique

Dataset Repository

• Kaggle Global Wheat Detection 2020
• Global Wheat Head Dataset 2021 (AICrowd, test data without label)
• Global Wheat Head Dataset 2021 (complete label)

Papers on Dataset

• Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods
• An analysis of last year competition: [2105.06182] Global Wheat Challenge 2020: Analysis of the competition design and winning models (arxiv.org)
• An introduction on the new datasets : [2105.07660] Global Wheat Head Dataset 2021: an update to improve the benchmarking wheat head localization with more diversity (arxiv.org)
• Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods https://hal.archives-ouvertes.fr/hal-02560389
• Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods

Papers on Solutions

• Md Mehedi Hasan, Joshua P. Chopin, Hamid Laga & Stanley J. Miklavcic , Detection and analysis of wheat spikes using Convolutional Neural Networks, 2018
• Wheat Head Detection using Deep, Semi-Supervised and Ensemble Learning , 2020
• Bo Gong, Daji Ergu, Ying Cai, Bo Ma, Real-Time Detection for Wheat Head Applying Deep Neural Network , 2020
• Fares Fourati, Wided Souidene, Rabah Attia, An original framework for Wheat Head Detection using Deep, Semi-supervised and Ensemble Learning within Global Wheat Head Detection (GWHD) Dataset, 2020
• Saeed Khaki, Nima Safaei, Hieu Pham, Lizhi Wang, WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting , 2021
• Zane K J Hartley, Andrew P French, Domain Adaptation of Synthetic Images for Wheat Head Detection [PDF] , 2021, (article)
• Chengxin Liu Kewei Wang Hao Lu Zhiguo Cao*, Dynamic Color Transform for Wheat Head Detection, 2022, [SPJ] [NCBI]
• Yan Zhang1, Manzhou Li2, Xiaoxiao Ma1, Xiaotong Wu3 and Yaojun Wang1* , High-Precision Wheat Head Detection Model Based on One-Stage Network and GAN Model, 2022
• Pengshuo Sun, Jingyi Cui, Xuefeng Hu, Qing Wang, WDN: A One-Stage Detection Network for Wheat Heads with High Performance, 2022
• Sébastien Dandrifossea, Elias Ennadifi, Alexis Carliera, Bernard Gosselin, Benjamin Dumont, Benoît Mercatorisa, Deep learning for wheat ear segmentation and ear density measurement: From heading to maturity, 2022
• Mikhaeil Kozhekin, Mikhail Genaev, Vasily Koval, Andrey Slobodchikov, Dmitry Afonnikov, Wheat yield estimation based on analysis of UAV images at low altitude, ITIA, 2022

Papers on object detection

• EfficientDet: Scalable and Efficient Object Detection
• Roman Solovyev, Wimin Wang, Tatiana Gbruseva, Weighted boxes fusion: Ensembling boxes from different object detection models with [Code]
• Do Thuan, EVOLUTION OF YOLO ALGORITHM AND YOLOV5: THE STATE-OF-THE-ART OBJECT DETECTION ALGORITHM , https://www.theseus.fi/bitstream/handle/10024/452552/Do_Thuan.pdf

Papers on Out Of Domain

• Predicting Out-of-Domain Generalization with Local Manifold Smoothness

Code Solutions

• EfficientDet (Scalable and Efficient Object Detection) implementation in Keras and Tensorflow
• Signatrix/efficientdet: Pytorch implementation
• xuannianz/EfficienDet: Implementation on Keras & Tensorflow
• Google: EfficientDet
• rwightman/efficientdet : A PyTorch implementation of EfficientDet. (used in 1st & 2nd place Kaggle 2020)
• YoloV5 https://github.com/ultralytics/yolov5
• Global Wheat Competition 2021, Starting Notebook using the WILDS library

Yolo5 was not eligible for 2020 Wheat Head Challenge, but it can be used in 2021 Wheat Head Challenge [discussion]

Articles

• EfficientDet: Towards Scalable and Efficient Object Detection
• A Throrough Breakdown of EfficientDet for Object Detection
• Key lessons learned from Global Wheat Kaggle Challenge!
• Using annotations to optimize the plant phenotyping process

Other Wheat Dataset

• Global Wheat Dataset http://www.global-wheat.com/
• Global Wheat Head Dataset – 2020 challenge version
• [Paper: Detection and Analysis of Wheat Spikes Using Convolutional Neural Networks], [Dataset Link]
• Wheat Dataset https://plantimages.nottingham.ac.uk/

Links

• Incorrect Labels on Global Wheat Head Dataset 2021