Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review

Y Lu, D Chen, E Olaniyi, Y Huang - Computers and Electronics in …, 2022 - Elsevier
In agricultural image analysis, optimal model performance is keenly pursued for better
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 …

Segment anything

A Kirillov, E Mintun, N Ravi, H Mao… - Proceedings of the …, 2023 - openaccess.thecvf.com
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 …

Sam 2: Segment anything in images and videos

N Ravi, V Gabeur, YT Hu, R Hu, C Ryali, T Ma… - arXiv preprint arXiv …, 2024 - arxiv.org
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 …

Recent advances in convolutional neural networks

J Gu, Z Wang, J Kuen, L Ma, A Shahroudy, B Shuai… - Pattern recognition, 2018 - Elsevier
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 …

[HTML][HTML] Convolutional neural networks for image-based high-throughput plant phenotyping: a review

Y Jiang, C Li - Plant Phenomics, 2020 - spj.science.org
Plant phenotyping has been recognized as a bottleneck for improving the efficiency of
breeding programs, understanding plant-environment interactions, and managing …

Semantic instance segmentation with a discriminative loss function

B De Brabandere, D Neven, L Van Gool - arXiv preprint arXiv:1708.02551, 2017 - arxiv.org
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 …

Deep watershed transform for instance segmentation

M Bai, R Urtasun - Proceedings of the IEEE conference on …, 2017 - openaccess.thecvf.com
Most contemporary approaches to instance segmentation use complex pipelines involving
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 …

Machine learning in plant science and plant breeding

ADJ van Dijk, G Kootstra, W Kruijer, D de Ridder - Iscience, 2021 - cell.com
Technological developments have revolutionized measurements on plant genotypes and
phenotypes, leading to routine production of large, complex data sets. This has led to …