Plant Phenotyping
23 papers with code • 0 benchmarks • 2 datasets
Plant Phenotyping refers to the use of various techniques and methods to measure and describe the external characteristics and traits of plants. In the field of machine learning, Plant Phenotyping typically involves the use of tools such as image processing, computer vision, sensor technologies, etc., to automatically capture and analyze data related to the morphology, structure, and growth patterns of plants.
Benchmarks
These leaderboards are used to track progress in Plant Phenotyping
CropAndWeed Dataset
SICKLE
Most implemented papers
How useful is Active Learning for Image-based Plant Phenotyping?
koushik-n/Active-Learning-Plant-Phenotyping
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To overcome this challenge, active learning algorithms have been proposed that reduce the amount of labeling needed by deep learning models to achieve good predictive performance.
Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images
yijingru/ObjGuided-Instance-Segmentation
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To deal with this problem, in this paper, we propose an object-guided instance segmentation method.
Pre-Clustering Point Clouds of Crop Fields Using Scalable Methods
In order to apply the recent successes of machine learning and automated plant phenotyping on a large scale using agricultural robotics, efficient and general algorithms must be designed to intelligently split crop fields into small, yet actionable, portions that can then be processed by more complex algorithms.
LeafMask: Towards Greater Accuracy on Leaf Segmentation
In this work, we present the LeafMask neural network, a new end-to-end model to delineate each leaf region and count the number of leaves, with two main components: 1) the mask assembly module merging position-sensitive bases of each predicted box after non-maximum suppression (NMS) and corresponding coefficients to generate original masks; 2) the mask refining module elaborating leaf boundaries from the mask assembly module by the point selection strategy and predictor.
Self-Supervised Leaf Segmentation under Complex Lighting Conditions
lxfhfut/self-supervised-leaf-segmentation
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As an essential prerequisite task in image-based plant phenotyping, leaf segmentation has garnered increasing attention in recent years.
Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review
In agricultural image analysis, optimal model performance is keenly pursued for better fulfilling visual recognition tasks (e. g., image classification, segmentation, object detection and localization), in the presence of challenges with biological variability and unstructured environments.
Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain
prbonn/hapt
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In this paper, we address the problem of joint semantic, plant instance, and leaf instance segmentation of crop fields from RGB data.
Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa
jlager/few-shot-leaf-segmentation
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In this way, the current work is designed to provide the plant phenotyping community with (i) methods for fast and accurate image-based feature extraction that require minimal training data, and (ii) a new population-scale data set, including 68 different leaf phenotypes, for domain scientists and machine learning researchers.
CherryPicker: Semantic Skeletonization and Topological Reconstruction of Cherry Trees
Therefore, we present CherryPicker, an automatic pipeline that reconstructs photo-metric point clouds of trees, performs semantic segmentation and extracts their topological structure in form of a skeleton.
Leaf Only SAM: A Segment Anything Pipeline for Zero-Shot Automated Leaf Segmentation
Leaf Only SAM does not perform better than the fine-tuned Mask R-CNN model on our data, but the SAM based model does not require any extra training or annotation of our new dataset.
