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Cultivation and grazing since the mid-nineteenth century in Texas has caused dramatic changes in grassland vegetation. Among these changes is the encroachment of native and introduced brush species. The distribution and quantity of brush can affect livestock production and water holding capacity of soil. Still, at the same time, brush can improve carbon sequestration and enhance agritourism and real estate value. The accurate identification of brush species and their distribution over large land tracts are important in developing brush management plans which may include herbicide application decisions. Near-real-time imaging and analyses of brush using an Unoccupied Aerial System (UAS) is a powerful tool to achieve such tasks. The use of multispectral imagery collected by a UAS to estimate the efficacy of herbicide treatment on noxious brush has not been evaluated previously. There has been no previous comparison of band combinations and pixel- and object-based methods to determine the best methodology for discrimination and classification of noxious brush species with Random Forest (RF) classification. In this study, two rangelands in southern Texas with encroachment of huisache (Vachellia farnesianna [L.] Wight & Arn.) and honey mesquite (Prosopis glandulosa Torr. var. glandulosa) were studied. Two study sites were flown with an eBee X fixed-wing to collect UAS images with four bands (Green, Red, Red-Edge, and Near-infrared) and ground truth data points pre- and post-herbicide application to study the herbicide effect on brush. Post-herbicide data were collected one year after herbicide application. Pixel-based and object-based RF classifications were used to identify brush in orthomosaic images generated from UAS images. The classification had an overall accuracy in the range 83–96%, and object-based classification had better results than pixel-based classification since object-based classification had the highest overall accuracy in both sites at 96%. The UAS image was useful for assessing herbicide efficacy by calculating canopy change after herbicide treatment. Different effects of herbicides and application rates on brush defoliation were measured by comparing canopy change in herbicide treatment zones. UAS-derived multispectral imagery can be used to identify brush species in rangelands and aid in objectively assessing the herbicide effect on brush encroachment.

In this research a machine learning framework was developed for cotton yield estimation using multi-temporal remote sensing data collected from unmanned aircraft system (UAS). The proposed machine learning model was based on an artificial neural network (ANN) and used three types of crop features derived from UAS data to predict the yield, namely; multi-temporal features including canopy cover, canopy height, canopy volume, normalized difference vegetation index (NDVI), excessive greenness index (ExG); non-temporal features including cotton boll count, boll size and boll volume, and irrigation status as a qualitative feature. The model provided low residual values with predicted yield values close to the observed yield values (R2 ~ 0.9). ANN model performance was compared with support vector regression (SVR) and random forest regression (RFR). Comparison results revealed that ANN model outperforms SVR and RFR. Additionally, redundant features were removed using correlation analysis, and an optimal subset of features was obtained that included canopy volume, ExG, boll count, boll volume and irrigation status. Moreover, the relative significance of each feature in the optimal input feature subset was determined using sensitivity analysis. It was found that canopy volume and ExG contributed around 50% towards the corresponding yield. Finally, further analysis was performed to investigate how early in the growing season the model can accurately predict yield. It was observed that even at 70 days after planting the model predicted yield with reasonable accuracy (R2 of 0.72 over test set). This study revealed that UAS derived multi-temporal data along with non-temporal and qualitative data can be combined within a machine learning framework to provide a reliable estimation of crop yield and provide effective understanding for crop management.

Recent years have witnessed enormous interest in the application of Unmanned Aerial Systems (UAS) for precision agriculture. This study presents a novel approach to use multi-temporal UAS data for comparison of two management practices in cotton, conventional tillage (CT) and no-tillage (NT). The plant parameters considered for the comparison are: canopy height (CH), canopy cover (CC), canopy volume (CV) and Normalized Difference Vegetation Index (NDVI). Initially, the whole study area was divided into approximately one square meter size grids. Measurements were extracted grid wise using high resolution UAS data captured ten times over whole crop growing season of the cotton. One tailed Z-test hypothesis reveals that there is a significant difference between cotton growth under CT and NT for almost all the epochs. With 95% confidence interval, the crop grown under NT found to have taller canopy, higher canopy cover, bigger biomass and higher NDVI, as compared to those under CT cropping system.

Image coregistration is a key preprocessing step to ensure the effective application of very-high-resolution (VHR) orthophotos generated from multisensor images acquired from unmanned aerial vehicle (UAV) platforms. The most accurate method to align an orthophoto is the installation of air-photo targets at a test site prior to flight image acquisition, and these targets were used as ground control points (GCPs) for georeferencing and georectification. However, there are time and cost limitations related to installing the targets and conducting field surveys on the targets during every flight. To address this problem, this paper presents an automated coregistration approach for orthophotos generated from VHR images acquired from multisensors mounted on UAV platforms. Spatial information from the orthophotos, provided by the global navigation satellite system (GNSS) at each image’s acquisition time, is used as ancillary information for phase correlation-based coregistration. A transformation function between the multisensor orthophotos is then estimated based on conjugate points (CPs), which are locally extracted over orthophotos using the phase correlation approach. Two multisensor datasets are constructed to evaluate the proposed approach. These visual and quantitative evaluations confirm the superiority of the proposed method.

Unmanned aerial vehicles (UAV) have been recognized as excellent tools to provide real time feedback of temporal and spatial conditions found in agricultural fields throughout the growing season. UAVs have also allowed accelerating breeding programs by screening varieties or by selecting agronomic traits that confer resistance to biotic and abiotic stresses and selecting the best management practices that optimize the management of soil and water resources. The main objectives of this study were to assess the potential use of UAVs to determine crop height, canopy cover, and NDVI during the tomato growing season for three tomato varieties; to validate tomato height obtained with a UAV; and evaluate the correlation between leaf area index and canopy cover determined with the UAV. The UAV was flown over a tomato trial planted with 90 plots that contained eight different tomato varieties; 3 roma and 5 round replicated three times per row and planted in three rows. The plots of the tomato varieties TAM-HOT, Shourouq, and Mykonos were selected for validation with the UAV. Commitment field measurements of plant height, leaf area index, and NDVI were collected weekly (from April 27 to June 22, 2017). All the tomato varieties were healthy without diseases and the NDVI values estimated with the UAV peaked between 90 and 110 days after transplanting (DAP). A coefficient of determination of 0.72 was observed between canopy cover estimated with the UAV and leaf area index measured with the ceptometer. The coefficient of correlation between the estimated and measured crop heights were 0.9845, 0.9766 and 0.9949 for the TAM-HOT, Shourouq and Mykonos, respectively. In addition, the calculated paired t test statistic showed no significant difference (P ≥ 0.05) between the estimated, the UAV and manually measured crop heights. In the future, UAV crop growth and NDVI monitoring could be improved through temporally dense data acquisition, increasing the number of ground samples and their geometric coincidence with the grids in UAV images, removal of weather effects, and other systematic errors caused from image quality and grid size.

Geo-referenced mapping in an aquatic environment is challenging because it is hard to measure the location of objects and images underwater. In this paper, we propose the method and share the results: cost-effective framework to generate 2D and 3D underwater maps with digital imagery and a Structure from Motion (SfM) algorithm. The proposed method consists of data acquisition, image processing, and color correction. 292 and 437 images were acquired from each study site located in Spring Lake in San Marcos, Texas, U.S.A. Agisoft Photoscan Pro software was used to generate 3D point cloud data and orthomosaic images after feature matching and image alignment from geo-tagged imagery. The mosaic images with high resolution (< 0.2 cm per pixel) were generated with 2D underwater images. After color correction, the red reduction effect was recovered, and the bluer color was removed. The 3D underwater map was generated directly from 3D dense point clouds including geo-coordinates and RGB color information. As a result, the Very High Resolution (VHR) 2D and 3D maps were generated and the topographic surface of underwater structures was obtained in great detail. Although the RMSE were about 1 m, the proposed method provided more detailed surface of underwater features.

Yield prediction and variety selection are critical components for assessing production and performance in breeding programs and precision agriculture. Since plants integrate their genetics, surrounding environments, and management conditions, crop phenotypes have been measured over cropping seasons to represent the traits of varieties. These days, UAS (unmanned aircraft system) provides a new opportunity to collect high-quality images and generate reliable phenotypic data efficiently. Here, we propose high-throughput phenotyping (HTP) from multitemporal UAS images for tomato yield estimation. UAS-based RGB and multispectral images were collected weekly and biweekly, respectively. The shape of the features of tomatoes such as canopy cover, canopy, volume, and vegetation indices derived from UAS imagery was estimated throughout the entire season. To extract time-series features from UAS-based phenotypic data, crop growth and growth rate curves were fitted using mathematical curves and first derivative equations. Time-series features such as the maximum growth rate, day at a specific event, and duration were extracted from the fitted curves of different phenotypes. The linear regression model produced high R 2 values even with different variable selection methods: all variables (0.79), forward selection (0.7), and backward selection (0.77). With factor analysis, we figured out two significant factors, growth speed and timing, related to high-yield varieties. Then, five time-series phenotypes were selected for yield prediction models explaining 65 percent of the variance in the actual harvest. The phenotypic features derived from RGB images played more important roles in prediction yield. This research also demonstrates that it is possible to select lower-performing tomato varieties successfully. The results from this work may be useful in breeding programs and research farms for selecting high-yielding and disease-/pest-resistant varieties.

Assessing plant population of cotton is important to make replanting decisions in low plant density areas, prone to yielding penalties. Since the measurement of plant population in the field is labor intensive and subject to error, in this study, a new approach of image-based plant counting is proposed, using unmanned aircraft systems (UAS; DJI Mavic 2 Pro, Shenzhen, China) data. The previously developed image-based techniques required a priori information of geometry or statistical characteristics of plant canopy features, while also limiting the versatility of the methods in variable field conditions. In this regard, a deep learning-based plant counting algorithm was proposed to reduce the number of input variables, and to remove requirements for acquiring geometric or statistical information. The object detection model named You Only Look Once version 3 (YOLOv3) and photogrammetry were utilized to separate, locate, and count cotton plants in the seedling stage. The proposed algorithm was tested with four different UAS datasets, containing variability in plant size, overall illumination, and background brightness. Root mean square error (RMSE) and R2 values of the optimal plant count results ranged from 0.50 to 0.60 plants per linear meter of row (number of plants within 1 m distance along the planting row direction) and 0.96 to 0.97, respectively. The object detection algorithm, trained with variable plant size, ground wetness, and lighting conditions generally resulted in a lower detection error, unless an observable difference of developmental stages of cotton existed. The proposed plant counting algorithm performed well with 0–14 plants per linear meter of row, when cotton plants are generally separable in the seedling stage. This study is expected to provide an automated methodology for in situ evaluation of plant emergence using UAS data.

Modern agriculture and food production systems are facing increasing pressures from climate change, land and water availability, and, more recently, a pandemic. These factors are threatening the environmental and economic sustainability of current and future food supply systems. Scientific and technological innovations are needed more than ever to secure enough food for a fast-growing global population. Scientific advances have led to a better understanding of how various components of the agricultural system interact, from the cell to the field level. Despite incredible advances in genetic tools over the past few decades, our ability to accurately assess crop status in the field, at scale, has been severely lacking until recently. Thanks to recent advances in remote sensing and Artificial Intelligence (AI), we can now quantify field scale phenotypic information accurately and integrate the big data into predictive and prescriptive management tools. This review focuses on the use of recent technological advances in remote sensing and AI to improve the resilience of agricultural systems, and we will present a unique opportunity for the development of prescriptive tools needed to address the next decade's agricultural and human nutrition challenges.

Unoccupied aerial systems (UAS) were used to phenotype growth trajectories of inbred maize populations under field conditions. Three recombinant inbred line populations were surveyed on a weekly basis collecting RGB images across two irrigation regimens (irrigated and non‐irrigated/rain fed). Plant height, estimated by the 95th percentile (P95) height from UAS generated 3D point clouds, exceeded 70% correlation (r) to manual ground truth measurements and 51% of experimental variance was explained by genetics. The Weibull sigmoidal function accurately modeled plant growth (R 2: >99%; RMSE

Canopy temperature is an important variable directly linked to a plant’s water status. Recent advances in Unmanned Aerial Vehicle (UAV) and sensor technology provides a great opportunity to obtain high-quality imagery for crop monitoring and high-throughput phenotyping (HTP) applications. In this study, a UAV-based thermal system was developed to directly measure canopy temperature, skipping the traditional radiometric calibration process which is time-consuming and complicates data processing. Raw thermal imagery collected over a cotton field was converted to surface temperature using the Software Development Kit (SDK) provided by the sensor company. Canopy temperature map was generated using Structure from Motion (SfM), and Thermal Stress Index (TSI) was calculated for the test site. UAV temperature measurements were compared to ground measurements acquired by net radiometers and thermocouples. Temperature differences between UAV and ground measurements were less than 5%, and UAV measurements proved to be more stable. The proposed UAV system was successful in showing temperature differences between the cotton genotype. In conclusion, the system described in this study could possibly be used to monitor crop water status in a field setting, which should prove helpful for precision agriculture and crop research.

Diseases cause enormous losses of yield and quality for crop producers worldwide. To meet future food demands, crops are bred for resistance to as many of these maladies as possible. One su...

Recent advances in molecular breeding and bioinformatics have greatly accelerated the screening of large sets of genotypes. Development of field-level phenotyping, however, still lags behind and is considered by many as the main bottleneck to improved efficiency in breeding programs. Unmanned Aerial System (UAS) and sensor technology available today enables collection of data at high spatial and temporal scales, previously unobtainable using traditional airborne remote sensing technologies. Here, we propose an UAS-assisted high throughput phenotyping framework for cotton (Gossypium hirsutum L.) genotype selection. UAS data collected on July 24, 2015 were used to calculate canopy cover, and UAS data collected on August 5, 2015 were used to extract open boll related phenotypic features including number of open bolls, average area of open bolls, average diameter of open bolls, perimeter of open bolls, perimeter to area ratio. Using the extracted features, a sequential selection procedure was performed on a population of 144 entries. Entries selected from the proposed framework were compared to the highest yielding entries determined by mechanical harvest results. Experimental results indicated that the selection process increased minimum and average lint yield of the remaining population by 7.4 and 10%, respectively, and UAS-selected entries and genotypes matched 80 and 73%, respectively, the same lists ranked by actual field harvest measurements.

Crop height is a very important attribute to assess overall crop condition, irrigation, and estimation of terminal yield. In this study, a novel method to monitor crop height of Sorghum ( Sorghum bicolor ) using an Unmanned Aerial System (UAS) is proposed. UAS data were acquired seven times over the growing season and each aerial acquisition included over 200 images with significant image overlap at an altitude of 50 m above ground. Ortho-mosaic image and 3D point cloud were generated by applying the Structure from Motion (SfM) algorithm to the images. Ground control points (GCPs) were installed around the study area and they were surveyed using a real time kinematic (RTK) GPS unit for accurate geo-referencing of the geospatial data products. A Digital Terrain Model (DTM) and Digital Surface Model (DSM) were generated from the 3D point cloud data, and a Crop Height Model (CHM) was then created by subtracting DTM from DSM. Uniform crop grids along the center line of each variety were defined for further processing. The maximum CHM value within each individual grid was taken to represent crop height of the grid, and average of all grid heights over the whole area of each variety was calculated as crop height of individual variety. These measurements were compared with manual crop height measurements. Root Mean Square Error (RMSE) between field measurements and the proposed approach was 0.33 m. In addition, the height estimates from both field measurement and the proposed approach could be used to derive a growth curve by fitting a sigmoidal curve. The residual RMSEs between the observed and predicted value of the curve established from UAS and field measurements were calculated as 0.05 m and 0.1 m, respectively. The growth curve results showed that the proposed approach indicated less RMSE and generated more reliable growth curves for monitoring sorghum height.

The objective of this effort was to evaluate current commercially-available sensor technology (three sonic ranging and two NDVI sensors) for use in a ground-based platform for plant phenotyping and crop management decisions. The Global Positioning System (GPS) receiver from Trimble provided a high level of accuracy during our tests. Normalized Difference Vegetation Index (NDVI) data collected using the GreenSeeker sensors were more consistent and presented less variability when compared to the Decagon SRS sensor. The consistency could be due to the GreenSeeker system averaging readings of more rows. The tests also indicated that although sonic ranging sensor technology may be employed to obtain average plant height estimates, the technology is still a limiting factor for high-accuracy measurements at the plant level.

This study presents a comparative study of multispectral and RGB (red, green, and blue) sensor-based cotton canopy cover modelling using multi-temporal unmanned aircraft systems (UAS) imagery. Additionally, a canopy cover model using an RGB sensor is proposed that combines an RGB-based vegetation index with morphological closing. The field experiment was established in 2017 and 2018, where the whole study area was divided into approximately 1 x 1 m size grids. Grid-wise percentage canopy cover was computed using both RGB and multispectral sensors over multiple flights during the growing season of the cotton crop. Initially, the normalized difference vegetation index (NDVI)-based canopy cover was estimated, and this was used as a reference for the comparison with RGB-based canopy cover estimations. To test the maximum achievable performance of RGB-based canopy cover estimation, a pixel-wise classification method was implemented. Later, four RGB-based canopy cover estimation methods were implemented using RGB images, namely Canopeo, the excessive greenness index, the modified red green vegetation index and the red green blue vegetation index. The performance of RGB-based canopy cover estimation was evaluated using NDVI-based canopy cover estimation. The multispectral sensor-based canopy cover model was considered to be a more stable and accurately estimating canopy cover model, whereas the RGB-based canopy cover model was very unstable and failed to identify canopy when cotton leaves changed color after canopy maturation. The application of a morphological closing operation after the thresholding significantly improved the RGB-based canopy cover modeling. The red green blue vegetation index turned out to be the most efficient vegetation index to extract canopy cover with very low average root mean square error (2.94% for the 2017 dataset and 2.82% for the 2018 dataset), with respect to multispectral sensor-based canopy cover estimation. The proposed canopy cover model provides an affordable alternate of the multispectral sensors which are more sensitive and expensive.

Unoccupied aerial systems (UAS) were used to phenotype growth trajectories of inbred maize populations under field conditions. Three recombinant inbred line populations were surveyed on a weekly basis collecting RGB images across two irrigation regimens (irrigated and non-irrigated/rain fed). Plant height, estimated by the 95th percentile (P95) height from UAS generated 3D point clouds, exceeded 70% correlation to manual ground truth measurements and 51% of experimental variance was explained by genetics. The Weibull sigmoidal function accurately modeled plant growth (R2: >99%; RMSE: < 4 cm) from P95 genetic means. The mean asymptote was strongly correlated (r2=0.66-0.77) with terminal plant height. Maximum absolute growth rates (mm d-1) were weakly correlated to height and flowering time. The average inflection point ranged from 57 to 60 days after sowing (DAS) and was correlated with flowering time (r2=0.45-0.68). Functional growth parameters (asymptote, inflection point, growth rate) alone identified 34 genetic loci, each explaining 3 to 15% of total genetic variation. Plant height was estimated at one-day intervals to 85 DAS, identifying 58 unique temporal quantitative trait loci (QTL) locations. Genomic hotspots on chromosome 1 and 3 indicated chromosomal regions associated with functional growth trajectories influencing flowering time, growth rate, and terminal growth. Temporal QTL demonstrated unique dynamic expression patterns not observable previously, no QTL were significantly expressed throughout the entire growing season. UAS technologies improved phenotypic selection accuracy and permitted monitoring traits on a temporal scale previously infeasible using manual measurements, furthering understanding of crop development and biological trajectories.Author summaryUnoccupied aerial systems (UAS) now can provide high throughput phenotyping to functionally model plant growth and explore genetic loci underlying temporal expression of dynamic phenotypes, specifically plant height. Efficient integration of temporal phenotyping via UAS, will improve the scientific understanding of dynamic, quantitative traits and developmental trajectories of important agronomic crops, leading to new understanding of plant biology. Here we present, for the first time, the dynamic nature of quantitative trait loci (QTL) over time under field conditions. To our knowledge, this is first empirical study to expand beyond selective developmental time points, evaluating functional and temporal QTL expression in maize (Zea mays L.) throughout a growing season within a field-based environment.

Commercial off-the shelf systems of UAVs and sensors are touted as being able to collect remote-sensing data on crops that include spectral reflectance and plant height. Historically a great deal of effort has gone into quantifying and reducing the error levels in the geometry of UAV-based orthomosaics, but little effort has gone into quantifying and reducing the error of the reflectance and plant-height. We have been developing systems and protocols involving multifunctional ground-control points (GCPs) in order to produce crop phenotypic data that are as repeatable as possible. These multifunctional GCPs aid not only geometric correction, but also image calibration of reflectance and plantheight. The GCPs have known spectral-reflectance characteristics that are used to enable reference-based digital numberto-reflectance calibration of multispectral images. They also have known platform heights that are used to enable reference-based digital surface model-to-height maps. Results show that using these GCPs for reflectance and plantheight calibrations significantly reduces the error levels in reflectance (ca. 50% reduction) and plant-height (ca. 20% reduction) measurements.

Unmanned aerial vehicle (UAV) platforms with sensors covering the red-edge and near-infrared (NIR) bands to measure vegetation indices (VIs) have been recently introduced in agriculture research. Consequently, VIs originally developed for traditional airborne and spaceborne sensors have become applicable to UAV systems. In this study, we investigated the difference in tillage treatments for cotton and sorghum using various RGB and NIR VIs. Minimized tillage has been known to increase farm sustainability and potentially optimize productivity over time; however, repeated tillage is the most commonly-adopted management practice in agriculture. To this day, quantitative comparisons of plant growth patterns between conventional tillage (CT) and no tillage (NT) fields are often inconsistent. In this study, high-resolution and multi-temporal UAV data were used for the analysis of tillage effects on plant health and the performance of various vegetation indices investigated. Time series data over ten dates were acquired on a weekly basis by RGB and multispectral (MS) UAV platforms: a DJI Phantom 4 Pro and a DJI Matrice 100 with the SlantRange 3p sensor. Ground reflectance panels and an ambient illumination sensor were used for the radiometric calibration of RGB and MS orthomosaic images, respectively. Various RGB and NIR-based vegetation indices were then calculated for the comparison between CT and NT treatments. In addition, a one-tailed Z-test was conducted to check the significance of VIs’ difference between CT and NT treatments. The results showed distinct differences in VIs between tillage treatments during the whole growing season. NIR-based VIs showed better discrimination performance than RGB-based VIs. Out of 13 VIs, the modified soil adjusted vegetation index (MSAVI) and optimized soil adjusted vegetation index (OSAVI) showed better performance in terms of quantitative difference measurements and the Z-test between tillage treatments. The modified green red vegetation index (MGRVI) and excess green (ExG) showed reliable separability and can be an alternative for economic RGB UAV application.

Unmanned Aerial System (UAS) is becoming a popular choice when acquiring fine spatial resolution images for precision agriculture applications. Compared to other remote sensing data collection platforms, UAS can acquire image data at relatively lower cost with finer spatial resolution with more flexible schedule. In recent years, multispectral sensors that can capture near infrared (NIR) and red edge spectral reflectance have been successfully integrated with UAS, and it is offering more versatility in soil and field analysis, crop monitoring, and plant health assessment. In this study, we aim to investigate the capability of UAS-based crop monitoring system to determine the best management practices for 3 different tomato varieties comparing different planting dates, plant density, use of plastic mulch and fertilization rate. The field and UAS data were acquired during Spring 2016, 2017, and 2018 located in Weslaco, TX. To compare the effect of various treatments in cropping systems, physiological parameters and vegetation indexes (Canopy Cover, Canopy Height, Canopy Volume and Excess Greenness) were extracted from red, green, blue (RGB) data and correlated with final yield data to evaluate practice/treatment to maximize tomato yield. During Spring 2016, we observed highest yield from the early March planting date using white plastic mulch. The results also indicated that the variety yielded higher presented a slow canopy decay towards the end of the season. In Spring 2017, there were differences in yield among the three tomato varieties depending on the fertilization rate, DRP-8551 performed better at low nitrogen level, Mykonos performed better on the two higher nitrogen rates and TAM-Hot-Ty had no significant difference among treatments. Finally, during Spring 2018, it was observed that early March produced the best yields and varieties that were able to slow canopy decay towards the end of season performed better. No significant difference was observed between plant density. It is expected that proposed system can be used to collect reliable data and develop variety and environment specific management practices to increase marketable yield and reduce production cost.

Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.

Sorghum is one of the most important crops worldwide. An accurate and efficient high-throughput phenotyping method for individual sorghum panicles is needed for assessing genetic diversity, variety selection, and yield estimation. High-resolution imagery acquired using an unmanned aerial vehicle (UAV) provides a high-density 3D point cloud with color information. In this study, we developed a detecting and characterizing method for individual sorghum panicles using a 3D point cloud derived from UAV images. The RGB color ratio was used to filter non-panicle points out and select potential panicle points. Individual sorghum panicles were detected using the concept of tree identification. Panicle length and width were determined from potential panicle points. We proposed cylinder fitting and disk stacking to estimate individual panicle volumes, which are directly related to yield. The results showed that the correlation coefficient of the average panicle length and width between the UAV-based and ground measurements were 0.61 and 0.83, respectively. The UAV-derived panicle length and diameter were more highly correlated with the panicle weight than ground measurements. The cylinder fitting and disk stacking yielded R2 values of 0.77 and 0.67 with the actual panicle weight, respectively. The experimental results showed that the 3D point cloud derived from UAV imagery can provide reliable and consistent individual sorghum panicle parameters, which were highly correlated with ground measurements of panicle weight.