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

Weed control by insects is increasingly important, as chemical weed control (the use of herbicides) has an important impact on the environment and, consequently, on all organisms living there. The use of insects to control weeds thus represents an alternative to herbicides. The article presents the suppression of some widespread and persistent weeds in Europe with their natural enemies - insects. The following combinations presented below are: broad-leaved dock (Rumex obtusifolius L.) – Gastrophysa viridula (De Geer, 1775), curly dock (Rumex crispus L.) – Apion violaceum (Kirby, 1808), common ragweed (Ambrosia artemisiifolia L.) – Ophraella communa (LeSage, 1986) and Zygogramma suturalis (Fabricius, 1775), creeping thistle (Cirsium arvense (L.) Scop.) – Cassida rubiginosa (Müller, 1776), cleavers (Galium aparine L.) – Halidamia affinis (Fallen, 1807) and Sermylassa halensis (Linnaeus, 1767), common knotgrass (Polygonum aviculare L.) and black-bindweed (Fallopia convolvulus L.) – Gastrophysa polygoni (Linnaeus, 1758) and as the last one field bindweed (Convolvulus arvensis L.) – Galeruca rufa (Germar, 1824) and Tyta luctuosa (Denis in Schiffmuller, 1775).

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Use of Unmanned Aerial Vehicle for Multi-temporal Monitoring of Soybean Vegetation Fraction

Journal of Biosystems Engineering ◽

10.5307/jbe.2016.41.2.126 ◽

2016 ◽

Vol 41 (2) ◽

pp. 126-137 ◽

Cited By ~ 13

Author(s):

Hee Sup Yun ◽

Soo Hyun Park ◽

Hak-Jin Kim ◽

Wonsuk Daniel Lee ◽

Kyung Do Lee ◽

...

Keyword(s):

Unmanned Aerial Vehicle ◽

Vegetation Fraction ◽

Multi Temporal ◽

Aerial Vehicle

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SITE SPECIFIC WEED MANAGEMENT MAPPING SYSTEM USING UNMANNED AERIAL VEHICLE (UAV)

10.13031/aim.201900207 ◽

2019 ◽

Author(s):

Bruen G Smith ◽

Livia Faria Defeo ◽

Troy A Jensen

Keyword(s):

Unmanned Aerial Vehicle ◽

Weed Management ◽

Site Specific ◽

Mapping System ◽

Aerial Vehicle

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

10.13031/2013.24628 ◽

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

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Improving weed segmentation in sugar beet fields using potentials of multispectral unmanned aerial vehicle images and lightweight deep learning

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.15.034510 ◽

2021 ◽

Vol 15 (03) ◽

Author(s):

Mehdi Khoshboresh-Masouleh ◽

Mehdi Akhoondzadeh

Keyword(s):

Deep Learning ◽

Sugar Beet ◽

Unmanned Aerial Vehicle ◽

Aerial Vehicle

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Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop

Biosystems Engineering ◽

10.1016/j.biosystemseng.2015.01.008 ◽

2015 ◽

Vol 132 ◽

pp. 19-27 ◽

Cited By ~ 96

Author(s):

Francisco Agüera Vega ◽

Fernando Carvajal Ramírez ◽

Mónica Pérez Saiz ◽

Francisco Orgaz Rosúa

Keyword(s):

Unmanned Aerial Vehicle ◽

Multi Temporal ◽

Aerial Vehicle ◽

Sunflower Crop

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

PLoS ONE ◽

10.1371/journal.pone.0058210 ◽

2013 ◽

Vol 8 (3) ◽

pp. e58210 ◽

Cited By ~ 144

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

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Multi-Temporal Unmanned Aerial Vehicle Remote Sensing for Vegetable Mapping Using an Attention-Based Recurrent Convolutional Neural Network

Remote Sensing ◽

10.3390/rs12101668 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1668 ◽

Cited By ~ 4

Author(s):

Quanlong Feng ◽

Jianyu Yang ◽

Yiming Liu ◽

Cong Ou ◽

Dehai Zhu ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Convolutional Neural Network ◽

Unmanned Aerial Vehicle ◽

Feature Fusion ◽

Agricultural Landscapes ◽

Sequential Dependency ◽

Temporal Features ◽

Multi Temporal ◽

Aerial Vehicle

Vegetable mapping from remote sensing imagery is important for precision agricultural activities such as automated pesticide spraying. Multi-temporal unmanned aerial vehicle (UAV) data has the merits of both very high spatial resolution and useful phenological information, which shows great potential for accurate vegetable classification, especially under complex and fragmented agricultural landscapes. In this study, an attention-based recurrent convolutional neural network (ARCNN) has been proposed for accurate vegetable mapping from multi-temporal UAV red-green-blue (RGB) imagery. The proposed model firstly utilizes a multi-scale deformable CNN to learn and extract rich spatial features from UAV data. Afterwards, the extracted features are fed into an attention-based recurrent neural network (RNN), from which the sequential dependency between multi-temporal features could be established. Finally, the aggregated spatial-temporal features are used to predict the vegetable category. Experimental results show that the proposed ARCNN yields a high performance with an overall accuracy of 92.80%. When compared with mono-temporal classification, the incorporation of multi-temporal UAV imagery could significantly boost the accuracy by 24.49% on average, which justifies the hypothesis that the low spectral resolution of RGB imagery could be compensated by the inclusion of multi-temporal observations. In addition, the attention-based RNN in this study outperforms other feature fusion methods such as feature-stacking. The deformable convolution operation also yields higher classification accuracy than that of a standard convolution unit. Results demonstrate that the ARCNN could provide an effective way for extracting and aggregating discriminative spatial-temporal features for vegetable mapping from multi-temporal UAV RGB imagery.

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