Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review
In agricultural image analysis, optimal model performance is keenly pursued for better
fulfilling visual recognition tasks (eg, image classification, segmentation, object detection …
fulfilling visual recognition tasks (eg, image classification, segmentation, object detection …
A review on 2D instance segmentation based on deep neural networks
W Gu, S Bai, L Kong - Image and Vision Computing, 2022 - Elsevier
Image instance segmentation involves labeling pixels of images with classes and instances,
which is one of the pivotal technologies in many domains, such as natural scenes …
which is one of the pivotal technologies in many domains, such as natural scenes …
Segment anything
Abstract We introduce the Segment Anything (SA) project: a new task, model, and dataset for
image segmentation. Using our efficient model in a data collection loop, we built the largest …
image segmentation. Using our efficient model in a data collection loop, we built the largest …
Sam 2: Segment anything in images and videos
We present Segment Anything Model 2 (SAM 2), a foundation model towards solving
promptable visual segmentation in images and videos. We build a data engine, which …
promptable visual segmentation in images and videos. We build a data engine, which …
Recent advances in convolutional neural networks
In the last few years, deep learning has led to very good performance on a variety of
problems, such as visual recognition, speech recognition and natural language processing …
problems, such as visual recognition, speech recognition and natural language processing …
[HTML][HTML] Convolutional neural networks for image-based high-throughput plant phenotyping: a review
Plant phenotyping has been recognized as a bottleneck for improving the efficiency of
breeding programs, understanding plant-environment interactions, and managing …
breeding programs, understanding plant-environment interactions, and managing …
Semantic instance segmentation with a discriminative loss function
Semantic instance segmentation remains a challenging task. In this work we propose to
tackle the problem with a discriminative loss function, operating at the pixel level, that …
tackle the problem with a discriminative loss function, operating at the pixel level, that …
Deep watershed transform for instance segmentation
Most contemporary approaches to instance segmentation use complex pipelines involving
conditional random fields, recurrent neural networks, object proposals, or template matching …
conditional random fields, recurrent neural networks, object proposals, or template matching …
A deep learning approach combining instance and semantic segmentation to identify diseases and pests of coffee leaves from in-field images
LM Tassis, JET de Souza, RA Krohling - Computers and Electronics in …, 2021 - Elsevier
The automated diagnosis of pests and diseases that affect coffee crops is an important issue
for coffee farmers. Conventional methods of computer vision and pattern recognition present …
for coffee farmers. Conventional methods of computer vision and pattern recognition present …
Machine learning in plant science and plant breeding
Technological developments have revolutionized measurements on plant genotypes and
phenotypes, leading to routine production of large, complex data sets. This has led to …
phenotypes, leading to routine production of large, complex data sets. This has led to …