[HTML][HTML] Deep learning based computer vision approaches for smart agricultural applications
The agriculture industry is undergoing a rapid digital transformation and is growing powerful
by the pillars of cutting-edge approaches like artificial intelligence and allied technologies …
by the pillars of cutting-edge approaches like artificial intelligence and allied technologies …
Artificial intelligence tools and techniques to combat herbicide resistant weeds—A review
The excessive consumption of herbicides has gradually led to the herbicide resistance weed
phenomenon. Managing herbicide resistance weeds can only be explicated by applying …
phenomenon. Managing herbicide resistance weeds can only be explicated by applying …
[HTML][HTML] Analysis of Stable Diffusion-derived fake weeds performance for training Convolutional Neural Networks
Weeds challenge crops by competing for resources and spreading diseases, impacting crop
yield and quality. Effective weed detection can enhance herbicide application, thus reducing …
yield and quality. Effective weed detection can enhance herbicide application, thus reducing …
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 …
Use of synthetic images for training a deep learning model for weed detection and biomass estimation in cotton
Site-specific treatment of weeds in agricultural landscapes has been gaining importance in
recent years due to economic savings and minimal impact on the environment. Different …
recent years due to economic savings and minimal impact on the environment. Different …
Machine learning for precision agriculture using imagery from unmanned aerial vehicles (uavs): A survey
Unmanned aerial vehicles (UAVs) are increasingly being integrated into the domain of
precision agriculture, revolutionizing the agricultural landscape. Specifically, UAVs are …
precision agriculture, revolutionizing the agricultural landscape. Specifically, UAVs are …
Combining high-resolution imaging, deep learning, and dynamic modeling to separate disease and senescence in wheat canopies
Maintenance of sufficiently healthy green leaf area after anthesis is key to ensuring an
adequate assimilate supply for grain filling. Tightly regulated age-related physiological …
adequate assimilate supply for grain filling. Tightly regulated age-related physiological …
Pseudo-label generation for agricultural robotics applications
In the context of table grape cultivation there is rising interest in robotic solutions for
harvesting, pruning, precision spraying and other agronomic tasks. Perception algorithms at …
harvesting, pruning, precision spraying and other agronomic tasks. Perception algorithms at …
Cisa: Context substitution for image semantics augmentation
Large datasets catalyze the rapid expansion of deep learning and computer vision. At the
same time, in many domains, there is a lack of training data, which may become an obstacle …
same time, in many domains, there is a lack of training data, which may become an obstacle …
Unsupervised Domain Adaptation for Weed Segmentation Using Greedy Pseudo-labelling
Automatic weed identification based on RGB images with convolutional neural networks
(CNN) is a new frontier of precision agriculture. However the CNN models expect a large …
(CNN) is a new frontier of precision agriculture. However the CNN models expect a large …