An automatic method for weed mapping in oat fields based on UAV imagery

Gašparović, Mateo; Zrinjski, Mladen; Barković, Đuro; Radočaj, Dorijan

doi:10.1016/j.compag.2020.105385

Cited by 92 publications

(70 citation statements)

References 31 publications

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“…Additionally, weeds [62,86,140] and disease detection [63,64,72] studies with the help of drone-based data have also been carried out. A semi-automatic object-based image analysis (OBIA) procedure was developed with random forest (RF) combined with feature selection techniques, was used to classify soil, weeds, and maize [86].…”

Section: Fertilizer Weeds Pest and Water-stress Assessmentmentioning

confidence: 99%

A Review on Drone-Based Data Solutions for Cereal Crops

Panday

Pratihast

Aryal

et al. 2020

Drones

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Food security is a longstanding global issue over the last few centuries. Eradicating hunger and all forms of malnutrition by 2030 is still a key challenge. The COVID-19 pandemic has placed additional stress on food production, demand, and supply chain systems; majorly impacting cereal crop producer and importer countries. Short food supply chain based on the production from local farms is less susceptible to travel and export bans and works as a smooth system in the face of these stresses. Local drone-based data solutions can provide an opportunity to address these challenges. This review aims to present a deeper understanding of how the drone-based data solutions can help to combat food insecurity caused due to the pandemic, zoonotic diseases, and other food shocks by enhancing cereal crop productivity of small-scale farming systems in low-income countries. More specifically, the review covers sensing capabilities, promising algorithms, and methods, and added-value of novel machine learning algorithms for local-scale monitoring, biomass and yield estimation, and mapping of them. Finally, we present the opportunities for linking information from citizen science, internet of things (IoT) based on low-cost sensors and drone-based information to satellite data for upscaling crop yield estimation to a larger geographical extent within the Earth Observation umbrella.

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Section: Fertilizer Weeds Pest and Water-stress Assessmentmentioning

confidence: 99%

A Review on Drone-Based Data Solutions for Cereal Crops

Panday

Pratihast

Aryal

et al. 2020

Drones

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“…The developed methods are based on a combination of unsupervised and supervised image classification methods. Furthermore, the methods can be technologically developed on object-based machine learning algorithms [87][88][89][90]. Such an approach allows rapid damage detection on buildings, which is important because of the large amount of high-resolution spatial data.…”

Section: Automatic Methods For Building Damage Detection and Mappingmentioning

confidence: 99%

A Review of Emerging Technologies for an Assessment of Safety and Seismic Vulnerability and Damage Detection of Existing Masonry Structures

Stepinac

Gašparović

2020

Applied Sciences

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The construction sector has proven to be one of the slowest sectors to embrace technology—a problem that must be addressed. This problem can be quickly and efficiently addressed in certain aspects of seismic engineering: from seismic risk assessment to damage detection, as well as condition assessments existing structures before or after an earthquake. In this paper, the literature review of assessment methods and damage detection technologies for existing (mainly) masonry structures is presented. Traditional methods are briefly explained, and modern are critically discussed. Special focus is given to unmanned aerial vehicles, as well as, photogrammetry and close-range remote sensing as a technology that can complement traditional ways of assessment and give us data about a structure that is often different to obtain. Graphical interpretation of one post-earthquake case study is provided. Open challenges and opportunities of emerging technologies for faster and easier assessment of seismic safety and vulnerability are presented.

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“…A generated high-resolution digital orthophoto was used to create various spectral indices that were tested for fast and accurate canopy mask layer creation. In accordance with the [48] the best spectral indices were chosen. Although many indices were tested, such as Brightness Index (BI) [49]; Redness Index (RI) [50], Colour Index (CI) [51], Green Leaf Index (GLI) [52], Normalized Green Red Difference Index (NGRDI) [53], Visual Atmospheric Resistance Index (VARI) [54], just three of them were selected: NGRDI, VARI, and GLI.…”

Section: Integration Of Sentinel 2 and Uav Datamentioning

confidence: 99%

Mapping of the Canopy Openings in Mixed Beech-Fir Forest at Sentinel-2 Subpixel level Using UAV and Machine Learning Approach

Pilaš¹,

Gašparović²,

Novkinić³

et al. 2020

Preprint

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The presented study demonstrates the bi-sensor approach suitable for rapid and precise up-to-date mapping of forest canopy gaps for the larger spatial extent. The approach makes use of UAV RGB images on smaller areas for highly precise forest canopy mask creation. Sentinel-2 was used as a scaling platform for transferring information from UAV to a wider spatial extent. The various approaches of the improvement of the predictive performance were examined: (I) the highest R2 of the single satellite index was up to 0.57, (II) the highest R2 using multiple features obtained from the single date, S-2 image was 0.624 and, (III) the highest R2 on the multi-temporal set of S-2 images, was 0.697. Satellite indices such as ARVI, IPVI, NDI45, PSSRa, MCARI, CI, RI, and NDTI were the dominant predictors in most of the ML algorithms. The more complex ML algorithms such as SVM, Random Forest, GBM, XGBoost, and Catboost that provided the best performance on the training set exhibited weaker generalization capabilities. Therefore, a simpler and more robust Elastic Net algorithm was chosen for the final map creation.

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An automatic method for weed mapping in oat fields based on UAV imagery

Cited by 92 publications

References 31 publications

A Review on Drone-Based Data Solutions for Cereal Crops

A Review on Drone-Based Data Solutions for Cereal Crops

A Review of Emerging Technologies for an Assessment of Safety and Seismic Vulnerability and Damage Detection of Existing Masonry Structures

Mapping of the Canopy Openings in Mixed Beech-Fir Forest at Sentinel-2 Subpixel level Using UAV and Machine Learning Approach

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