Quantifying Citrus Tree Health Using True Color UAV Images

Garza, Blanca N.; Ancona, Veronica; Enciso, Juan; Perotto‐Baldivieso, Humberto L.; Kunta, Madhurababu; Simpson, Catherine

doi:10.3390/rs12010170

Cited by 28 publications

(19 citation statements)

References 30 publications

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“…Zhang et al (2020), Hunt Junior et al (2018 and Schirrmann et al (2016) also found weak results monitoring the N variability with RGB imagery in maize, potatoes, and winter wheat, respectively. Garza et al (2020), did not find significant correlation with the visible VI Triangular Greeness Index (TGI) and the amount of N in citrus trees. However, Zhang et al (2020) reported that the Excess Green (ExG) may have the potential for this task, and Schirrmann et al (2016) found positive relationships with other biophysical parameters of the crop, like fresh and dry biomass, and Leaf Area Index.…”

Section: Random Forest Modelsmentioning

confidence: 89%

“…On the same leaves that were analyzed, we used the chlorophyll meter SPAD 502, performing 2 readings per leaf, and registering the average for each point, that is, 16 readings per point. The SPAD values are given by the difference between the transmittance of red and infrared light that penetrated the leaf (Garza et al, 2020), and the objective of obtaining these values was to verify their relationship with the measured amounts of N.…”

Section: Study Area and Leaf Samplingmentioning

confidence: 99%

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Using unmanned aerial vehicle and machine learning algorithm to monitor leaf nitrogen in coffee

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et al. 2020

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Nitrogen is an essential element for coffee production. However, when fertilization do not consider the spatial variability of the agricultural parameters, it can generate economic losses, and environmental impacts. Thus, the monitoring of nitrogen is essential to the fertilizing management, and remote sensing based on unmanned aerial vehicles imagery has been evaluated for this task. This work aimed to analyze the potential of vegetation indices of the visible range, obtained with such vehicles, to monitor the nitrogen content of coffee plants in southern Minas Gerais, Brazil. Therefore, we performed leaf analysis using the Kjeldahl method, and we processed the images to produce the vegetation indices using Geographic Information Systems and photogrammetry software. Moreover, the images were classified using the Color Index of Vegetation and the Maximum Likelihood Classifier. As estimator tool, we created Random Forest models of classification and regression. We also evaluated the Pearson correlation coefficient between the nitrogen and the vegetation indices, and we performed the analysis of variance and the Tukey-Kramer test to assess whether there is a significant difference between the averages of these indices in relation to nitrogen levels. However, the models were not able to predict the nitrogen. The regression model obtained a R 2 = 0.01. The classification model achieved an overall accuracy of 0.33 (33%), but it did not distinguish between the different levels of nitrogen. The correlation tests revealed that the vegetation indices are not correlated with the nitrogen, since the best index was the Green Leaf Index (R = 0.21). However, the image classification achieved a Kappa coefficient of 0.92, indicating that the tested index is efficient. Therefore, visible indices were not able to monitor the nitrogen in this case, but they should continue to be explored, since they could represent a less expensive alternative.

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Section: Random Forest Modelsmentioning

confidence: 89%

Section: Study Area and Leaf Samplingmentioning

confidence: 99%

Using unmanned aerial vehicle and machine learning algorithm to monitor leaf nitrogen in coffee

Parreiras

Lense

Moreira

et al. 2020

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“…Maximum winds were recorded for each flight with a Skywatch Meteos windmeter anemometer (30 m AGL flight = 5 km/h; 40 m AGL flight = 15 km/h; 50 m AGL flight = 7 km/h). Our lowest flight altitude was 30 m AGL; this altitude was used in a similar study for agricultural crops in the same region [45] and we increased the altitude by 10 m increments up to 50 m AGL. This allowed us to assess the effect of pixel resolution on forage estimation.…”

Section: Image Acquisitionmentioning

confidence: 99%

“…For our study we set our flight settings to a double grid style flight pattern, at a speed of 3-5 km/h, 70 • camera angle, with 80% image overlap using the Pix4D capture application for Android. The use of convergent image networks to develop photogrammetric models using structure for motion can help improve the reconstruction accuracy of these models [25,45,46]. The UAV flight was controlled through auto-pilot mode in this program.…”

Section: Image Acquisitionmentioning

confidence: 99%

A Pilot Study to Estimate Forage Mass from Unmanned Aerial Vehicles in a Semi-Arid Rangeland

et al. 2020

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The application of unmanned aerial vehicles (UAVs) in the monitoring and management of rangelands has exponentially increased in recent years due to the miniaturization of sensors, ability to capture imagery with high spatial resolution, lower altitude platforms, and the ease of flying UAVs in remote environments. The aim of this research was to develop a method to estimate forage mass in rangelands using high-resolution imagery derived from the UAV using a South Texas pasture as a pilot site. The specific objectives of this research were to (1) evaluate the feasibility of quantifying forage mass in semi-arid rangelands using a double sampling technique with high-resolution imagery and (2) to compare the effect of altitude on forage mass estimation. Orthoimagery and digital surface models (DSM) with a resolution <1.5 cm were acquired with an UAV at altitudes of 30, 40, and 50 m above ground level (AGL) in Duval County, Texas. Field forage mass data were regressed on volumes obtained from a DSM. Our results show that volumes estimated with UAV data and forage mass as measured in the field have a significant relationship at all flight altitudes with best results at 30-m AGL (r2 = 0.65) and 50-m AGL (r2 = 0.63). Furthermore, the use of UAVs would allow one to collect a large number of samples using a non-destructive method to estimate available forage for grazing animals.

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“…It has been found that the high-precision recognition of citrus canker can be achieved indoors or in air. Garza et al [20] used RGB UAV remote sensing to detect citrus HLB and foot rot and explained the subtle differences in tree health caused by various diseases through the triangular green index (TGI).…”

Section: Introductionmentioning

confidence: 99%

Detection of Citrus Huanglongbing Based on Multi-Input Neural Network Model of UAV Hyperspectral Remote Sensing

et al. 2020

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Citrus is an important cash crop in the world, and huanglongbing (HLB) is a destructive disease in the citrus industry. To efficiently detect the degree of HLB stress on large-scale orchard citrus trees, an UAV (Uncrewed Aerial Vehicle) hyperspectral remote sensing tool is used for HLB rapid detection. A Cubert S185 (Airborne Hyperspectral camera) was mounted on the UAV of DJI Matrice 600 Pro to capture the hyperspectral remote sensing images; and a ASD Handheld2 (spectrometer) was used to verify the effectiveness of the remote sensing data. Correlation-proven UAV hyperspectral remote sensing data were used, and canopy spectral samples based on single pixels were extracted for processing and analysis. The feature bands extracted by the genetic algorithm (GA) of the improved selection operator were 468 nm, 504 nm, 512 nm, 516 nm, 528 nm, 536 nm, 632 nm, 680 nm, 688 nm, and 852 nm for the HLB detection. The proposed HLB detection methods (based on the multi-feature fusion of vegetation index) and canopy spectral feature parameters constructed (based on the feature band in stacked autoencoder (SAE) neural network) have a classification accuracy of 99.33% and a loss of 0.0783 for the training set, and a classification accuracy of 99.72% and a loss of 0.0585 for the validation set. This performance is higher than that based on the full-band AutoEncoder neural network. The field-testing results show that the model could effectively detect the HLB plants and output the distribution of the disease in the canopy, thus judging the plant disease level in a large area efficiently.

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Quantifying Citrus Tree Health Using True Color UAV Images

Cited by 28 publications

References 30 publications

Using unmanned aerial vehicle and machine learning algorithm to monitor leaf nitrogen in coffee

Using unmanned aerial vehicle and machine learning algorithm to monitor leaf nitrogen in coffee

A Pilot Study to Estimate Forage Mass from Unmanned Aerial Vehicles in a Semi-Arid Rangeland

Detection of Citrus Huanglongbing Based on Multi-Input Neural Network Model of UAV Hyperspectral Remote Sensing

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