scholarly journals Integration of remote‐weed mapping and an autonomous spraying unmanned aerial vehicle for site‐specific weed management

2019 ◽  
Vol 76 (4) ◽  
pp. 1386-1392 ◽  
Author(s):  
Joseph E Hunter ◽  
Travis W Gannon ◽  
Robert J Richardson ◽  
Fred H Yelverton ◽  
Ramon G Leon
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Weed Management ◽  
Site Specific ◽  
Weed Mapping ◽  
Aerial Vehicle

scholarly journals Configuration and Specifications of an Unmanned Aerial Vehicle (UAV) for Early Site Specific Weed Management

PLoS ONE ◽  
2013 ◽  
Vol 8 (3) ◽  
pp. e58210 ◽  
Author(s):  
Jorge Torres-Sánchez ◽  
Francisca López-Granados ◽  
Ana Isabel De Castro ◽  
José Manuel Peña-Barragán
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Weed Management ◽  
Site Specific ◽  
Aerial Vehicle

Mapping Cynodon dactylon in vineyards using UAV images for site-specific weed control

2017 ◽  
Vol 8 (2) ◽  
pp. 267-271 ◽  
Author(s):  
A. I. de Castro ◽  
J. M. Peña ◽  
J. Torres-Sánchez ◽  
F. Jiménez-Brenes ◽  
F. López-Granados
Keyword(s):  
Cover Crops ◽  
Weed Management ◽  
Cynodon Dactylon ◽  
Management Practice ◽  
Bare Soil ◽  
Economic Losses ◽  
Site Specific ◽  
Object Based ◽  
Aerial Vehicle ◽  
Uav Images

In Spain, the use of annual cover crops is a crop management practice for irrigated vineyards that allows controlling vineyard vigor and yield, which also leads to improve the crop quality. Recently, Cynodon dactylon (bermudagrass) has been reported to infest those cover crops and colonize the grapevine rows, resulting in significant yield and economic losses due to the competition for water and nutrients. From timely unmanned aerial vehicle (UAV) imagery, the objective of this research was to map C. dactylon patches in order to provide an optimized site-specific weed management. A quadrocopter UAV equipped with a point-and-shoot camera was used to collect a set of aerial red-green-blue (RGB) images over a commercial vineyard plot, coinciding with the dormant period of C. dactylon (February 2016). Object-based image analysis (OBIA) techniques were used to develop an innovative algorithm for early discrimination and mapping of C. dactylon, which had the ability to solve the limitation of spectral similarity of this weed with cover crops or bare soil. As a general result, the classified maps of the studied vineyard showed four main classes, i.e. vine, cover crop, C. dactylon and bare soil, with 85% overall accuracy. These weed maps allow developing new strategies for site-specific control of C. dactylon populations in the context of precision viticulture.

Development of an Unmanned Aerial Vehicle-Based Spray System for Highly Accurate Site-Specific Application

2008 ◽  
Author(s):  
Yanbo Huang ◽  
Wesley Clint Hoffmann ◽  
K Fritz ASABE Member ◽  
Yubin Lan
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Site Specific ◽  
Spray System ◽  
Aerial Vehicle

scholarly journals An open source workflow for weed mapping in native grassland using unmanned aerial vehicle: using Rumex obtusifolius as a case study

2020 ◽  
pp. 1-18
Author(s):  
Olee Hoi Ying Lam ◽  
Marcel Dogotari ◽  
Moritz Prüm ◽  
Hemang Narendra Vithlani ◽  
Corinna Roers ◽  
...  
Keyword(s):  
Open Source ◽  
Unmanned Aerial Vehicle ◽  
Rumex Obtusifolius ◽  
Weed Mapping ◽  
Native Grassland ◽  
Aerial Vehicle

scholarly journals A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery

PLoS ONE ◽  
2018 ◽  
Vol 13 (4) ◽  
pp. e0196302 ◽  
Author(s):  
Huasheng Huang ◽  
Jizhong Deng ◽  
Yubin Lan ◽  
Aqing Yang ◽  
Xiaoling Deng ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Weed Mapping ◽  
Aerial Vehicle

scholarly journals Multi-Temporal Site-Specific Weed Control of Cirsium arvense (L.) Scop. and Rumex crispus L. in Maize and Sugar Beet Using Unmanned Aerial Vehicle Based Mapping

Agriculture ◽  
2018 ◽  
Vol 8 (5) ◽  
pp. 65 ◽  
Author(s):  
Robin Mink ◽  
Avishek Dutta ◽  
Gerassimos Peteinatos ◽  
Markus Sökefeld ◽  
Johannes Engels ◽  
...  
Keyword(s):  
Sugar Beet ◽  
Weed Control ◽  
Unmanned Aerial Vehicle ◽  
Cirsium Arvense ◽  
Rumex Crispus ◽  
Site Specific ◽  
Multi Temporal ◽  
Aerial Vehicle

scholarly journals Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping

Sensors ◽  
2015 ◽  
Vol 15 (8) ◽  
pp. 19688-19708 ◽  
Author(s):  
Irene Borra-Serrano ◽  
José Peña ◽  
Jorge Torres-Sánchez ◽  
Francisco Mesas-Carrascosa ◽  
Francisca López-Granados
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Quality Evaluation ◽  
Weed Mapping ◽  
Aerial Vehicle ◽  
Spatial Quality

scholarly journals Accurate Weed Mapping and Prescription Map Generation Based on Fully Convolutional Networks Using UAV Imagery

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3299 ◽  
Author(s):  
Huasheng Huang ◽  
Jizhong Deng ◽  
Yubin Lan ◽  
Aqing Yang ◽  
Xiaoling Deng ◽  
...  
Keyword(s):  
Weed Management ◽  
Experimental Results ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Weed Mapping ◽  
Weed Cover ◽  
Map Generation ◽  
Aerial Vehicle ◽  
Weed Infestation ◽  
And Performance

Chemical control is necessary in order to control weed infestation and to ensure a rice yield. However, excessive use of herbicides has caused serious agronomic and environmental problems. Site specific weed management (SSWM) recommends an appropriate dose of herbicides according to the weed coverage, which may reduce the use of herbicides while enhancing their chemical effects. In the context of SSWM, the weed cover map and prescription map must be generated in order to carry out the accurate spraying. In this paper, high resolution unmanned aerial vehicle (UAV) imagery were captured over a rice field. Different workflows were evaluated to generate the weed cover map for the whole field. Fully convolutional networks (FCN) was applied for a pixel-level classification. Theoretical analysis and practical evaluation were carried out to seek for an architecture improvement and performance boost. A chessboard segmentation process was used to build the grid framework of the prescription map. The experimental results showed that the overall accuracy and mean intersection over union (mean IU) for weed mapping using FCN-4s were 0.9196 and 0.8473, and the total time (including the data collection and data processing) required to generate the weed cover map for the entire field (50 × 60 m) was less than half an hour. Different weed thresholds (0.00–0.25, with an interval of 0.05) were used for the prescription map generation. High accuracies (above 0.94) were observed for all of the threshold values, and the relevant herbicide saving ranged from 58.3% to 70.8%. All of the experimental results demonstrated that the method used in this work has the potential to produce an accurate weed cover map and prescription map in SSWM applications.

Fusion of pixel and object-based features for weed mapping using unmanned aerial vehicle imagery

2018 ◽  
Vol 67 ◽  
pp. 43-53 ◽  
Author(s):  
Junfeng Gao ◽  
Wenzhi Liao ◽  
David Nuyttens ◽  
Peter Lootens ◽  
Jürgen Vangeyte ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Weed Mapping ◽  
Object Based ◽  
Aerial Vehicle
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