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A review of deep learning techniques used in agriculture
Deep learning (DL) is a robust data-analysis and image-processing technique that has
shown great promise in the agricultural sector. In this study, 129 papers that are based on …
shown great promise in the agricultural sector. In this study, 129 papers that are based on …
A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications
The globe's population is increasing day by day, which causes the severe problem of
organic food for everyone. Farmers are becoming progressively conscious of the need to …
organic food for everyone. Farmers are becoming progressively conscious of the need to …
A survey of deep learning techniques for weed detection from images
The rapid advances in Deep Learning (DL) techniques have enabled rapid detection,
localisation, and recognition of objects from images or videos. DL techniques are now being …
localisation, and recognition of objects from images or videos. DL techniques are now being …
[HTML][HTML] PROSAIL-Net: A transfer learning-based dual stream neural network to estimate leaf chlorophyll and leaf angle of crops from UAV hyperspectral images
Accurate and efficient estimation of crop biophysical traits, such as leaf chlorophyll
concentrations (LCC) and average leaf angle (ALA), is an important bridge between …
concentrations (LCC) and average leaf angle (ALA), is an important bridge between …
Weed detection using deep learning: A systematic literature review
Weeds are one of the most harmful agricultural pests that have a significant impact on crops.
Weeds are responsible for higher production costs due to crop waste and have a significant …
Weeds are responsible for higher production costs due to crop waste and have a significant …
[HTML][HTML] Image-to-image translation-based data augmentation for improving crop/weed classification models for precision agriculture applications
Applications of deep-learning models in machine visions for crop/weed identification have
remarkably upgraded the authenticity of precise weed management. However, compelling …
remarkably upgraded the authenticity of precise weed management. However, compelling …
Deep learning models for the classification of crops in aerial imagery: a review
In recent years, the use of remote sensing data obtained from satellite or unmanned aerial
vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield …
vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield …
[HTML][HTML] Advanced technology in agriculture industry by implementing image annotation technique and deep learning approach: A review
The implementation of intelligent technology in agriculture is seriously investigated as a way
to increase agriculture production while reducing the amount of human labor. In agriculture …
to increase agriculture production while reducing the amount of human labor. In agriculture …
Utilizing convolutional neural networks (CNN) and U-Net architecture for precise crop and weed segmentation in agricultural imagery: A deep learning approach
MA Bhatti, MS Syam, H Chen, Y Hu, LW Keung… - Big Data Research, 2024 - Elsevier
This study presents the implementation and evaluation of a convolutional neural network
(CNN) based image segmentation model using the U-Net architecture for forest image …
(CNN) based image segmentation model using the U-Net architecture for forest image …
Map** invasive noxious weed species in the alpine grassland ecosystems using very high spatial resolution UAV hyperspectral imagery and a novel deep learning …
The term “invasive noxious weed species”(INWS), which refers to noxious weed plants that
invade native alpine grasslands, has increasingly become an ecological and economic …
invade native alpine grasslands, has increasingly become an ecological and economic …