High-Resolution Aerial Imaging Based Estimation of Crop Emergence in Potatoes

Sankaran, Sindhuja; Quirós, Juan José; Knowles, N. Richard; Knowles, Lisa O.

doi:10.1007/s12230-017-9604-2

Cited by 22 publications

(30 citation statements)

References 19 publications

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“…Previous studies have demonstrated that RGB imagery has the capability to detect and count post-emergence plants more easily at their growth stages for cotton (Chen et al, 2018 ), maize (Gnädinger and Schmidhalter, 2017 ), and potato (Sankaran et al, 2017 ). Most of these plants were represented by individual objects after identification and segmentation using spectral information, because they were bigger with larger spacing and more uniform distribution (Jin et al, 2017 ; Liu et al, 2017b ).…”

Section: Discussionmentioning

confidence: 99%

“…The accuracy may be improved with additional NIR images (Chen et al, 2018 ). High resolution multispectral images were successfully used to estimate crop emergence in potatoes (Sankaran et al, 2017 ). These results demonstrated that spectral information could be effective to identify and count the number of seedlings with no or minimal overlapping.…”

Section: Discussionmentioning

confidence: 99%

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Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

et al. 2018

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The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A digital single-lens reflex camera was installed on an UAV platform to capture ultrahigh resolution RGB images at two growth stages when most rapeseed plants had at least two leaves. Rapeseed plant objects were segmented from images of vegetation indices using typical Otsu thresholding method. After segmentation, shape features such as area, length-width ratio and elliptic fit were extracted from the segmented rapeseed plant objects to establish regression models of seedling stand count. Three row characteristics (the coefficient of variation of row spacing uniformity, the error rate of the row spacing and the coefficient of variation of seedling uniformity) were further calculated for seeding performance evaluation after crop row detection. Results demonstrated that shape features had strong correlations with ground-measured seedling stand count. The regression models achieved R-squared values of 0.845 and 0.867, respectively, for the two growth stages. The mean absolute errors of total stand count were 9.79 and 5.11% for the two respective stages. A single model over these two stages had an R-squared value of 0.846, and the total number of rapeseed plants was also accurately estimated with an average relative error of 6.83%. Moreover, the calculated row characteristics were demonstrated to be useful in recognizing areas of failed germination possibly resulted from skipped or ineffective planting. In summary, this study developed practical UAV-based remote sensing methods and demonstrated the feasibility of using the methods for rapeseed seedling stand counting and mechanical seeding performance evaluation at early growth stages.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

et al. 2018

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“…The development of unmanned aerial vehicles technology and its potential application for agriculture afforded by the spatial precision with which data can be obtained and the temporal availability of these data-together with precision agriculture-can facilitate differentiated management of crops, based on the knowledge of the variability extant in agricultural exploitation. Therefore, studies have been recently realized involving counting of cotton, rapeseed, sunflower, corn, potato and wheat plants [22][23][24][25][26].…”

Section: Discussionmentioning

confidence: 99%

“…This technology has been successfully implemented to estimate biomass, estimate the fractional plant cover, height and weed detection and classification, [16][17][18][19][20]. Recently, RGB images obtained with these platforms have been used to count plants in safflower, wheat, rapeseed and germination in cotton and potato [12,[21][22][23][24][25]. UAV platforms offer advantages to estimate corn plant counting and planting density.…”

Section: Introductionmentioning

confidence: 99%

Digital Count of Corn Plants Using Images Taken by Unmanned Aerial Vehicles and Cross Correlation of Templates

et al. 2020

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The number of plants, or planting density, is a key factor in corn crop yield. The objective of the present research work was to count corn plants using images obtained by sensors mounted on an unmanned aerial vehicle (UAV). An experiment was set up with five levels of nitrogen fertilization (140, 200, 260, 320 and 380 kg/ha) and four replicates, resulting in 20 experimental plots. The images were taken at 23, 44 and 65 days after sowing (DAS) at a flight altitude of 30 m, using two drones equipped with RGB sensors of 12, 16 and 20 megapixels (Canon PowerShot S100_5.2, Sequoia_4.9, DJI FC6310_8.8). Counting was done through normalized cross-correlation (NCC) for four, eight and twelve plant samples or templates in the a* channel of the CIELAB color space because it represented the green color that allowed plant segmentation. A mean precision of 99% was obtained for a pixel size of 0.49 cm, with a mean error of 2.2% and a determination coefficient of 0.90 at 44 DAS. Precision values above 91% were obtained at 23 and 44 DAS, with a mean error between plants counted digitally and visually of ±5.4%. Increasing the number of samples or templates in the correlation estimation improved the counting precision. Good precision was achieved in the first growth stages of the crop when the plants do not overlap and there are no weeds. Using sensors and unmanned aerial vehicles, it is possible to determine the emergence of seedlings in the field and more precisely evaluate planting density, having more accurate information for better management of corn fields.

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“…IKONOS imageries have also been applied for local vegetation index calculation and mapping (Allbed, Kumar, & Aldakheel, 2014;Anchang, Ananga, & Pu, 2016;Hui, Linhai, Liming, & Qiuming, 2016;Laidler, Treitz, & Atkinson, 2008). Some very high resolution aerial photos with 0.5 m or less GSD have also been used for mapping various local vegetation land covers (Macfarlane, McGinty, Laub, & Gifford, 2017;Mora, Vieira, Pina, Lousada, & Christiansen, 2015;Sankaran, Quirós, Knowles, & Knowles, 2017;Su et al, 2016;Xiaoxiao & Shao, 2014).…”

Section: Previous Studiesmentioning

confidence: 99%

Mapping Honeysuckle Distribution in Large Forests through Use of High Resolution Satellite Images – Case Study at Great Parks of Hamilton County, Ohio, USA

Wang¹,

Taylor²,

Henninger³

et al. 2019

eer

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A non-native invasive shrub, named Amur honeysuckle, has caused various problems to the habitats of native plants and animals. Local governments, private landowners and non-profit groups spend numerous efforts on removing these species from forests. The first step to remove the species is to know their spatial distribution. The major objective of this study is to explore how to quickly map honeysuckle distribution by using high resolution satellite images. The case study is located in Great Parks of Hamilton County (GPHC), Ohio. A World-View2 satellite image acquired on Nov. 9th 2013 was classified into several land use types, including low density honeysuckle area, medium/high density honeysuckle area, grass/crop, bare soil/construction, and water/wet land. Half of the field data that we collected in Nov. 2013 and some high resolution aerial photos (with 4 inch spatial resolution) were used to validate the classification result. The study result demonstrates that the World-View2 image delineates honeysuckle distribution fairly well, in particular, for medium/high density honeysuckle areas. As a pilot program utilizing advanced geospatial analysis, this project will provide important information for understanding the status of wildlife habitats and for implementing site-specific management in parks and nature preserves.

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High-Resolution Aerial Imaging Based Estimation of Crop Emergence in Potatoes

Cited by 22 publications

References 19 publications

Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

Digital Count of Corn Plants Using Images Taken by Unmanned Aerial Vehicles and Cross Correlation of Templates

Mapping Honeysuckle Distribution in Large Forests through Use of High Resolution Satellite Images – Case Study at Great Parks of Hamilton County, Ohio, USA

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