Intra-Season Crop Height Variability at Commercial Farm Scales Using a Fixed-Wing UAV

Ziliani, Matteo G.; Parkes, Stephen; Hoteit, Ibrahim; McCabe, Matthew F.

doi:10.3390/rs10122007

Cited by 67 publications

(59 citation statements)

References 85 publications

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“…To reduce the cost of estimating crop PH, researchers start to use low-cost UAV RGB remote-sensing system [64][65][66][67]. For example, Ziliani et al [68] compared the PH derived from high-resolution UAV RGB imagery against PH derived from LiDAR, and showed a strong correlation between structure-from-motion derived heights and LiDAR scan data with the coefficient of determination (R 2 ) ranged from 0.77 to 0.99. Grüner et al [69] estimated the PH of grass in northern Hesse, Germany from UAV RGB imagery by using photogrammetric structure from motion processing, and showed a high correlation between PH derived from UAV RGB imagery and ground-truth data with a R 2 value of 0.56 and a root mean squared error (RMSE) value of 0.13 m. In many of these studies, crop surface model derived from point clouds was adopted to estimate PH.…”

Section: Discussionmentioning

confidence: 99%

Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

et al. 2019

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The rapid, accurate, and economical estimation of crop above-ground biomass at the farm scale is crucial for precision agricultural management. The unmanned aerial vehicle (UAV) remote-sensing system has a great application potential with the ability to obtain remote-sensing imagery with high temporal-spatial resolution. To verify the application potential of consumer-grade UAV RGB imagery in estimating maize above-ground biomass, vegetation indices and plant height derived from UAV RGB imagery were adopted. To obtain a more accurate observation, plant height was directly derived from UAV RGB point clouds. To search the optimal estimation method, the estimation performances of the models based on vegetation indices alone, based on plant height alone, and based on both vegetation indices and plant height were compared. The results showed that plant height directly derived from UAV RGB point clouds had a high correlation with ground-truth data with an R2 value of 0.90 and an RMSE value of 0.12 m. The above-ground biomass exponential regression models based on plant height alone had higher correlations for both fresh and dry above-ground biomass with R2 values of 0.77 and 0.76, respectively, compared to the linear regression model (both R2 values were 0.59). The vegetation indices derived from UAV RGB imagery had great potential to estimate maize above-ground biomass with R2 values ranging from 0.63 to 0.73. When estimating the above-ground biomass of maize by using multivariable linear regression based on vegetation indices, a higher correlation was obtained with an R2 value of 0.82. There was no significant improvement of the estimation performance when plant height derived from UAV RGB imagery was added into the multivariable linear regression model based on vegetation indices. When estimating crop above-ground biomass based on UAV RGB remote-sensing system alone, looking for optimized vegetation indices and establishing estimation models with high performance based on advanced algorithms (e.g., machine learning technology) may be a better way.

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Section: Discussionmentioning

confidence: 99%

Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

et al. 2019

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“…To construct the 3D models, photogrammetry requires at least two overlapping images of the same scene and/or object(s), captured from different points of view. These kind of techniques can be used for extracting three-dimensional digital surface or terrain models [37,40,43] and/or orthophotos [50,55]. UAV low-altitude data acquisition enables the construction of 3D models with a much higher spatial resolution compared to other remote sensing technologies (such as satellites).…”

Section: Crop Featuresmentioning

confidence: 99%

“…Adobe Photoshop [21,100] Applied to correct distortion/use of other image processing methods Agisoft Photoscan [22,36,37] Exploited for the construction of 3D models and orthomosaics. It also allows the calculation of vegetation indices QGIS [23,55] Usually exploited for the calculation of the vegetation indices from multispectral data MATLAB [35,100] Applied mainly for the calculation of vegetation indices.…”

Section: Software Tool Descriptionmentioning

confidence: 99%

A Review on UAV-Based Applications for Precision Agriculture

2019

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Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.

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“…Similar findings were identified in this research, where shape features such as plant area, border length, width and length had the highest importance in the random forest models, followed by the Green-Red Vegetation Index, and the entropy texture feature extracted from a gray level co-occurrence matrix. While crop height, which is generally measurable from UAV imagery, has been identified as an important parameter for predicting crop yield (Ziliani et al, 2018), it was found to be of little importance in the Random Forest models for the six dates assessed in this research. This was attributed to the flattening of tomato plants caused by sandstorms and the fact that once tomato fruits become larger and heavier, the weight is likely to bend branches downwards, potentially reducing plant height.…”

Section: Predicting Biomass and Yieldmentioning

confidence: 77%

Predicting Biomass and Yield at Harvest of Salt-Stressed Tomato Plants Using Uav Imagery

Johansen

Morton

Malbéteau

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Biomass and yield are important variables used for assessing agricultural production. However, these variables are difficult to estimate for individual plants at the farm scale and may be affected by abiotic stressors such as salinity. In this study, the wild tomato species, Solanum pimpinellifolium, was evaluated through field and UAV-based assessment of 600 control and 600 salt-treated plants. The aim of this research was to determine, if UAV-based imagery, collected one, two, four, six, seven and eight weeks before harvest could predict fresh shoot mass, tomato fruit numbers, and yield mass at harvest and if predictions varied for control and salttreated plants. A Random Forest approach was used to model biomass and yield. The results showed that shape features such as plant area, border length, width and length had the highest importance in the random forest models. A week prior to harvest, the explained variance of fresh shoot mass, number of fruits and yield mass were 86.60%, 59.46% and 61.09%, respectively. The explained variance was reduced as a function of time to harvest. Separate models may be required for predicting yield of salt-stressed plants, whereas the prediction of yield for control plants was less affected if the model included salt-stressed plants. This research demonstrates that it is possible to predict biomass and yield of tomato plants up to four weeks prior to harvest, and potentially earlier in the absence of severe weather events.

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Intra-Season Crop Height Variability at Commercial Farm Scales Using a Fixed-Wing UAV

Cited by 67 publications

References 85 publications

Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

A Review on UAV-Based Applications for Precision Agriculture

Predicting Biomass and Yield at Harvest of Salt-Stressed Tomato Plants Using Uav Imagery

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