11 citations · 21 across the 8 of their papers we have counts for
9 papers
A Vision-Based Navigation System for Arable Fields
Rajitha de Silva, Grzegorz Cielniak, Junfeng Gao
Vision-based navigation systems in arable fields are an underexplored area in agricultural robot navigation. Vision systems deployed in arable fields face challenges such as fluctu…
Leaving the Lines Behind: Vision-Based Crop Row Exit for Agricultural Robot Navigation
Rajitha de Silva, Grzegorz Cielniak, Junfeng Gao
Usage of purely vision based solutions for row switching is not well explored in existing vision based crop row navigation frameworks. This method only uses RGB images for local fe…
Transferring learned patterns from ground-based field imagery to predict UAV-based imagery for crop and weed semantic segmentation in precision crop farming
Junfeng Gao, Wenzhi Liao, David Nuyttens +3
Weed and crop segmentation is becoming an increasingly integral part of precision farming that leverages the current computer vision and deep learning technologies. Research has be…
Vision based Crop Row Navigation under Varying Field Conditions in Arable Fields
Rajitha de Silva, Grzegorz Cielniak, Junfeng Gao
Accurate crop row detection is often challenged by the varying field conditions present in real-world arable fields. Traditional colour based segmentation is unable to cater for al…
Deep learning-based Crop Row Detection for Infield Navigation of Agri-Robots
Rajitha de Silva, Grzegorz Cielniak, Gang Wang +1
Autonomous navigation in agricultural environments is challenged by varying field conditions that arise in arable fields. State-of-the-art solutions for autonomous navigation in su…
Towards Infield Navigation: leveraging simulated data for crop row detection
Rajitha de Silva, Grzegorz Cielniak, Junfeng Gao
Agricultural datasets for crop row detection are often bound by their limited number of images. This restricts the researchers from developing deep learning based models for precis…
In-field early disease recognition of potato late blight based on deep learning and proximal hyperspectral imaging
Chao Qi, Murilo Sandroni, Jesper Cairo Westergaard +4
Effective early detection of potato late blight (PLB) is an essential aspect of potato cultivation. However, it is a challenge to detect late blight at an early stage in fields wit…
Tea Chrysanthemum Detection under Unstructured Environments Using the TC-YOLO Model
Chao Qi, Junfeng Gao, Simon Pearson +3
Tea chrysanthemum detection at its flowering stage is one of the key components for selective chrysanthemum harvesting robot development. However, it is a challenge to detect flowe…
Towards agricultural autonomy: crop row detection under varying field conditions using deep learning
Rajitha de Silva, Grzegorz Cielniak, Junfeng Gao
This paper presents a novel metric to evaluate the robustness of deep learning based semantic segmentation approaches for crop row detection under different field conditions encoun…