Supporting Urban Weed Biosecurity Programs with Remote Sensing

Sheffield, Kathryn; Dugdale, Tony M.

doi:10.3390/rs12122007

Cited by 10 publications

(3 citation statements)

References 77 publications

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“…The classification was performed using the Maximum Likelihood Classification (MLC) algorithm, part of the supervised classification for grids option of the aforementioned program. The MLC was chosen for its ability to discriminate between weeds and the surrounding elements, as indicated by different authors [33][34][35]. MLC is based on two principles: that the cells in each class sample in the multidimensional space are normally distributed, and on the Bayes theorem of decision making.…”

Section: Weed Classificationmentioning

confidence: 99%

Opportunities from Unmanned Aerial Vehicles to Identify Differences in Weed Spatial Distribution between Conventional and Conservation Agriculture

et al. 2022

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Weeds are one of the major issues in agricultural production and they are present in most agricultural systems. Due to the heterogeneity of weed distribution, understanding spatial patterns is paramount for precision farming and improving sustainability in crop management. Nevertheless, limited information is currently available about the differences between conventional agricultural (CV) weed spatial patterns and weed spatial patterns in conservation agricultural systems (CA); moreover, opportunities to use unmanned aerial vehicles (UAV) and recognition algorithms to monitor these differences are still being explored and tested. In this work, the opportunity to use UAVs to detect changes in spatial distribution over time between CA and CV fields was assessed for data acquisition. Acquired data were processed using maximum likelihood classification to discriminate between weeds and surrounding elements; then, a similarity assessment was performed using the ‘equal to’ function of the raster calculator. The results show important differences in spatial distribution over time between CA and CV fields. In the CA field 56.18% of the area was infested in both years when the field margin effect was included, and 22.53% when this effect was excluded; on the other hand, in the CV field only 11.50% of the area was infested in both years. The results illustrate that there are important differences in the spatial distribution of weeds between CA and CV fields; such differences can be easily detected using UAVs and identification algorithms combined.

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Section: Weed Classificationmentioning

confidence: 99%

Opportunities from Unmanned Aerial Vehicles to Identify Differences in Weed Spatial Distribution between Conventional and Conservation Agriculture

et al. 2022

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“…Due to that fact, the understanding of weed spreading and their timely identification within a field for using plant protection products is crucial. Earth remote sensing provides great prospects for weed vegetation foci detection [9,10].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Invasive and Weed Species by Hyperspectral Imagery in Agrocenoses Ecosystem

et al. 2022

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The present study aimed to investigate the possibility of using hyperspectral imaging data to identify the invasive and weed species in agrocenoses ecosystem. The most common weeds in grain agrocenoses, i.e., Ambrosia artemisiifolia L., Euphorbia seguieriana Neck., Atriplex tatarica L., Glycyrrhiza glabra L., Setaria pumila (Poir.) Roem. and Schult, served as objects. The population of weeds, especially Ambrosia artemisiifolia is invasive for the selected region of study. Therefore, the shooting of objects was carried out with a hyperspectral camera, Cubert UHD185, and the values of 100 spectral channels were obtained from hyperspectral images. The values of 80 vegetation indices (VIs) were calculated. The material was processed using mathematical statistics (analysis of variance, t-test) and search methods of data analysis (principal component analysis, decision tree, and random forest). Using statistical methods, the simultaneous use of several VIs differentiated between species more deliberately and precisely. The combination of VIs Derivative index (D1), Chlorophyll content index (Datt3), and Pigment specific normalized difference (PSND) can be used for weeds identification. Using the decision tree method, VIs established a good division of weeds into groups; (1) perennial rhizomatous weeds (Euphorbia seguieriana, and Glycyrrhiza glabra), and (2) annual weeds (A. artemisiifolia, A. tatarica, and S. pumila); These Vis are Chlorophyll index (CI), D1, and Datt3. Using the random forest method, the VIs that have the greatest impact on Mean Decrease Accuracy and Mean Decrease Gini are D1, Datt3, PSND, and Double Peak Index (DPI). The use of spectral channel values for the identification of plant species using the principal component analysis, decision tree, and random forest methods showed worse results than when using VIs. A great similarity of the results was obtained with the help of statistical and search methods of data analysis.

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“…In this work, we tested three classifiers. Classification and Regression Trees (CART) because it requires less effort for data preparation during pre-processing [11], Random Forest (RF) used in several remote sensing works [12], and Artificial Neural Network (ANN) because of its ability to deal with noisy data [13] and non-linear problems [14].…”

Section: Introductionmentioning

confidence: 99%

Crop and Weed Identification in Sugarcane Fields Using RGB Uav Imagery

Yano¹,

Mesa²,

Teruel³

2022

Open Science Research VI

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The presence of weeds in the sugarcane crop can affect its production. Usually, farmers use herbicides to control weeds. Precision agriculture is an alternative to avoid damages to the environment, and it is a way to save economic resources. Once, herbicides will be applied only in the appropriate places and correct dosages. Precision agriculture requires georeferenced information on the location of the infestation spots. In this sense, UAVs can fly at low altitudes to play an important role. This permit takes images with the required spatial resolution for a good classification of the weeds present in the crop. The UAV also can fly on demand, solving the problem of temporal resolution. This work proposes using RGB cameras assembled in UAVs to capture images to classify weeds in sugarcane crops. The RGB camera is a solution affordable for a large number of producers. In tests realized in an experimental field with sugarcane, four weeds' species, and soil, using Artificial Neural Networks classifier, was obtained 72.33% to 77.33% of overall accuracy and Kappa coefficient of 0.668 to 0.724.

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Supporting Urban Weed Biosecurity Programs with Remote Sensing

Cited by 10 publications

References 77 publications

Opportunities from Unmanned Aerial Vehicles to Identify Differences in Weed Spatial Distribution between Conventional and Conservation Agriculture

Opportunities from Unmanned Aerial Vehicles to Identify Differences in Weed Spatial Distribution between Conventional and Conservation Agriculture

Assessment of Invasive and Weed Species by Hyperspectral Imagery in Agrocenoses Ecosystem

Crop and Weed Identification in Sugarcane Fields Using RGB Uav Imagery

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