Plant Pathology 2021 – FGVC8 Competition: Resources

Official link: https://www.kaggle.com/c/plant-pathology-2021-fgvc8/overview

Dataset

• Thapa, Ranjita; Zhang, Kai; Snavely, Noah; Belongie, Serge; Khan, Awais. The Plant Pathology Challenge 2020 data set to classify foliar disease of apples. Applications in Plant Sciences, 8 (9), 2020.

Previous Competition

• Plant Pathology 2020 – Fine Grained Visual Categorization 7 “Identify the category of foliar diseases in apple trees”

Related Competition

• Cassava Leaf Disease Classification, “Identify the type of disease present on a Cassava Leaf image”

Papers on Plant Image Detection

• Zhigang Dai, Bolun Cai, Yugeng Lin, Junying Chen, UP-DETR: Unsupervised Pre-training for Object Detection with Transformers – The model is pre-trained to detect these query patches from the original image. During the pre-training, we address two critical issues: multi-task learning and multi-query localization. (1) To trade-off multi-task learning of classification and localization in the pretext task, we freeze the CNN backbone and propose a patch feature reconstruction branch which is jointly optimized with patch detection. (2) To perform multi-query localization, we introduce UP-DETR from single-query patch and extend it to multi-query patches with object query shuffle and attention mask.
• Strawberry Detection Using a Heterogeneous Multi-Processor Platform.
  This paper proposes using the You Only Look Once version 3 (YOLOv3) Convolutional Neural Network (CNN) in combination with utilising image processing techniques for the application of precision farming robots targeting strawberry detection, accelerated on a heterogeneous multiprocessor platform. The results show a performance acceleration by five times when implemented on a Field-Programmable Gate Array (FPGA) when compared with the same algorithm running on the processor side with an accuracy of 78.3\% over the test set comprised of 146 images.
• Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search – We introduce the concept of prioritized path, which refers to the architecture candidates exhibiting superior performance during training.
• Bag of Freebies for Training Object Detection Neural Networks [paperswithcode]
  In this works, we explore training tweaks that apply to various models including Faster R-CNN and YOLOv3. These tweaks do not change the model architectures, therefore, the inference costs remain the same. Our empirical results demonstrate that, however, these freebies can improve up to 5% absolute precision compared to state-of-the-art baselines.
• MMDetection: Open MMLab Detection Toolbox and Benchmark [github]
  MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project.
• YOLOv4: Optimal Speed and Accuracy of Object Detection [github]
• Cascade R-CNN: Delving into High Quality Object Detection, Conference on Computer Vision and Pattern Recognition, 2018
• EfficientDet: Scalable and Efficient Object Detection [github]
• Cascade Mask R-CNN)CBNet: A Novel Composite Backbone Network Architecture for Object Detection
• Image Processing Based Detection of Fungal Diseases in Plants, ICICT 2014
• Applications of Computer Vision in Plant Pathology: A Survey, Arch Computat Methods Eng 27, 611–632 (2020). https://doi.org/10.1007