AgriSegNet: Deep aerial semantic segmentation framework for IoT-assisted precision agriculture
Aerial inspection of agricultural regions can provide crucial information to safeguard from
numerous obstacles to efficient farming. Farmland anomalies such as standing water, weed …
numerous obstacles to efficient farming. Farmland anomalies such as standing water, weed …
Semantic scene segmentation in unstructured environment with modified DeepLabV3+
Semantic scene segmentation has become a key application in computer vision and is an
essential part of intelligent transportation systems for complete scene understanding of the …
essential part of intelligent transportation systems for complete scene understanding of the …
[HTML][HTML] Disruptive technologies in smart farming: an expanded view with sentiment analysis
Smart Farming (SF) is an emerging technology in the current agricultural landscape. The
aim of Smart Farming is to provide tools for various agricultural and farming operations to …
aim of Smart Farming is to provide tools for various agricultural and farming operations to …
Genco: An auxiliary generator from contrastive learning for enhanced few-shot learning in remote sensing
J Wu, N Hovakimyan, J Hobbs - ECAI 2023, 2023 - ebooks.iospress.nl
Classifying and segmenting patterns from a limited number of examples is a significant
challenge in remote sensing and earth observation due to the difficulty in acquiring …
challenge in remote sensing and earth observation due to the difficulty in acquiring …
Towards computationally efficient and realtime distracted driver detection with mobilevgg network
According to the World Health Organization (WHO) report, the number of road traffic deaths
have been continuously increasing since last few years though the rate of deaths relative to …
have been continuously increasing since last few years though the rate of deaths relative to …
[HTML][HTML] Outdoor plant segmentation with deep learning for high-throughput field phenotyping on a diverse wheat dataset
Robust and automated segmentation of leaves and other backgrounds is a core prerequisite
of most approaches in high-throughput field phenotyping. So far, the possibilities of deep …
of most approaches in high-throughput field phenotyping. So far, the possibilities of deep …
Extended agriculture-vision: An extension of a large aerial image dataset for agricultural pattern analysis
A key challenge for much of the machine learning work on remote sensing and earth
observation data is the difficulty in acquiring large amounts of accurately labeled data. This …
observation data is the difficulty in acquiring large amounts of accurately labeled data. This …
AAFormer: a multi-modal transformer network for aerial agricultural images
Y Shen, L Wang, Y Jin - … of the IEEE/CVF Conference on …, 2022 - openaccess.thecvf.com
The semantic segmentation of agricultural aerial images is very important for the recognition
and analysis of farmland anomaly patterns, such as drydown, endrow, nutrient deficiency …
and analysis of farmland anomaly patterns, such as drydown, endrow, nutrient deficiency …
[HTML][HTML] Multiple-attention mechanism network for semantic segmentation
D Wang, S Xiang, Y Zhou, J Mu, H Zhou, R Irampaye - Sensors, 2022 - mdpi.com
Contextual information and the dependencies between dimensions is vital in image
semantic segmentation. In this paper, we propose a multiple-attention mechanism network …
semantic segmentation. In this paper, we propose a multiple-attention mechanism network …
Vision on the bog: Cranberry crop risk evaluation with deep learning
Computer vision and AI for smart agriculture have exciting potential in optimizing crop yield
while reducing resource use for better environmental and commercial outcomes. The goal of …
while reducing resource use for better environmental and commercial outcomes. The goal of …