Unmanned Aerial Vehicle to Estimate Nitrogen Status of Turfgrasses

Caturegli, Lisa; Corniglia, Matteo; Gaetani, Monica; Grossi, Nicola; Magni, Simone; Migliazzi, Mauro; Angelini, Luciana Gabriella; Mazzoncini, Marco; Silvestri, Nicola; Fontanelli, Marco; Raffaelli, Michele; Peruzzi, Andrea; Volterrani, Marco

doi:10.1371/journal.pone.0158268

Cited by 97 publications

(80 citation statements)

References 60 publications

(69 reference statements)

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“…The use of UASs for in-season N estimation has been evaluated in crops such as maize, rice, potato or turfgrass [21][22][23][24]. The objective of this work was to assess the usefulness of a set of spectral indices acquired from very high-resolution (<10 cm) images obtained from an unmanned aerial system (UAS) for tracking spatial and temporal variability of cotton N status as well as for predicting lint yield on a commercial farm.…”

Section: Unmanned Aerial Systems For Monitoring Crop Performancementioning

confidence: 99%

Assessment of In-Season Cotton Nitrogen Status and Lint Yield Prediction from Unmanned Aerial System Imagery

et al. 2017

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Abstract:The present work assessed the usefulness of a set of spectral indices obtained from an unmanned aerial system (UAS) for tracking spatial and temporal variability of nitrogen (N) status as well as for predicting lint yield in a commercial cotton (Gossypium hirsutum L.) farm. Organic, inorganic and a combination of both types of fertilizers were used to provide a range of eight N rates from 0 to 340 kg N ha −1 . Multi-spectral images (reflectance in the blue, green, red, red edge and near infrared bands) were acquired on seven days throughout the season, from 62 to 169 days after sowing (DAS), and data were used to compute structure-and chlorophyll-sensitive vegetation indices (VIs). Above-ground plant biomass was sampled at first flower, first cracked boll and maturity and total plant N concentration (N%) and N uptake determined. Lint yield was determined at harvest and the relationships with the VIs explored. Results showed that differences in plant N% and N uptake between treatments increased as the season progressed. Early in the season, when fertilizer applications can still have an effect on lint yield, the simplified canopy chlorophyll content index (SCCCI) was the index that best explained the variation in N uptake and plant N% between treatments. Around first cracked boll and maturity, the linear regression obtained for the relationships between the VIs and both plant N% and N uptake was statistically significant, with the highest r 2 values obtained at maturity. The normalized difference red edge (NDRE) index, and SCCCI were generally the indices that best distinguished the treatments according to the N uptake and total plant N%. Treatments with the highest N rates (from 307 to 340 kg N ha −1 ) had lower normalized difference vegetation index (NDVI) than treatments with 0 and 130 kg N ha −1 at the first measurement day (62 DAS), suggesting that factors other than fertilization N rate affected plant growth at this early stage of the crop. This fact affected the earliest date at which the structure-sensitive indices NDVI and the visible atmospherically resistant index (VARI) enabled yield prediction (97 DAS). A statistically significant linear regression was obtained for the relationships between SCCCI and NDRE with lint yield at 83 DAS. Overall, this study shows the practicality of using an UAS to monitor the spatial and temporal variability of cotton N status in commercial farms. It also illustrates the challenges of using multi-spectral information for fertilization recommendation in cotton at early stages of the crop.

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Section: Unmanned Aerial Systems For Monitoring Crop Performancementioning

confidence: 99%

Assessment of In-Season Cotton Nitrogen Status and Lint Yield Prediction from Unmanned Aerial System Imagery

et al. 2017

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“…UAV imagery might be able to close the gap between the plot scale, covered by manual sensors and proximal sensors, and the regional scale, covered by traditional remote sensing, because of their high spatial resolution and almost instantaneous availability for practitioners and experts in agriculture. Most studies have investigated the relationship between biophysical parameters and UAV imagery, e.g., biomass [29][30][31][32][33], LAI [34][35][36][37][38], plant height [30] and grain yield [39], whereas fewer studies have shown the relationship between nitrogen and UAV imagery [31,35,40,41] in wheat crops, so far. We summarized existing research studies relating UAV imagery with some agronomic parameters of wheat crops in Table S1.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

et al. 2016

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Abstract:Monitoring the dynamics in wheat crops requires near-term observations with high spatial resolution due to the complex factors influencing wheat growth variability. We studied the prospects for monitoring the biophysical parameters and nitrogen status in wheat crops with low-cost imagery acquired from unmanned aerial vehicles (UAV) over an 11 ha field. Flight missions were conducted at approximately 50 m in altitude with a commercial copter and camera system-three missions were performed between booting and maturing of the wheat plants and one mission after tillage. Ultra-high resolution orthoimages of 1.2 cm·px −1 and surface models were generated for each mission from the standard red, green and blue (RGB) aerial images. The image variables were extracted from image tone and surface models, e.g., RGB ratios, crop coverage and plant height. During each mission, 20 plots within the wheat canopy with 1 × 1 m 2 sample support were selected in the field, and the leaf area index, plant height, fresh and dry biomass and nitrogen concentrations were measured. From the generated UAV imagery, we were able to follow the changes in early senescence at the individual plant level in the wheat crops. Changes in the pattern of the wheat canopy varied drastically from one mission to the next, which supported the need for instantaneous observations, as delivered by UAV imagery. The correlations between the biophysical parameters and image variables were highly significant during each mission, and the regression models calculated with the principal components of the image variables yielded R 2 values between 0.70 and 0.97. In contrast, the models of the nitrogen concentrations yielded low R 2 values with the best model obtained at flowering (R 2 = 0.65). The nitrogen nutrition index was calculated with an accuracy of 0.10 to 0.11 NNI for each mission. For all models, information about the surface models and image tone was important. We conclude that low-cost RGB UAV imagery will strongly aid farmers in observing biophysical characteristics, but it is limited for observing the nitrogen status within wheat crops.

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“…Moreover, it was verified that consumer-grade color cameras could be used to reliably acquire images to allow vegetation indices to be retrieved. A multispectral camera carried on a UAV was used by Caturegli et al [27] to obtain multispectral images of lawns. By utilizing ENVI software to process the images, information on the vegetation index of the lawns could be extracted to evaluate their nitrogen nutrition status.…”

Section: Introductionmentioning

confidence: 99%

Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

Yao

JingChao

et al. 2017

Sensors

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In view of the demand for a low-cost, high-throughput method for the continuous acquisition of crop growth information, this study describes a crop-growth monitoring system which uses an unmanned aerial vehicle (UAV) as an operating platform. The system is capable of real-time online acquisition of various major indexes, e.g., the normalized difference vegetation index (NDVI) of the crop canopy, ratio vegetation index (RVI), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW). By carrying out three-dimensional numerical simulations based on computational fluid dynamics, spatial distributions were obtained for the UAV down-wash flow fields on the surface of the crop canopy. Based on the flow-field characteristics and geometrical dimensions, a UAV-borne crop-growth sensor was designed. Our field experiments show that the monitoring system has good dynamic stability and measurement accuracy over the range of operating altitudes of the sensor. The linear fitting determination coefficients (R2) for the output RVI value with respect to LNA, LAI, and LDW are 0.63, 0.69, and 0.66, respectively, and the Root-mean-square errors (RMSEs) are 1.42, 1.02 and 3.09, respectively. The equivalent figures for the output NDVI value are 0.60, 0.65, and 0.62 (LNA, LAI, and LDW, respectively) and the RMSEs are 1.44, 1.01 and 3.01, respectively.

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Unmanned Aerial Vehicle to Estimate Nitrogen Status of Turfgrasses

Cited by 97 publications

References 60 publications

Assessment of In-Season Cotton Nitrogen Status and Lint Yield Prediction from Unmanned Aerial System Imagery

Assessment of In-Season Cotton Nitrogen Status and Lint Yield Prediction from Unmanned Aerial System Imagery

Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

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