Annotated Bibliography

Anita Simic Milas, M. R. (2018). The Importance of leaf area index in mapping chlorophyll content of corn under different agricultural treatments using UAV images. International Journal of Remote Sensing, 5415-5431.

Chlorophyll imagery in plant leaves provides a measurement of plant health when captured with multispectral and hyperspectral sensors. Specifically, visible, near-infrared, and red edge frequencies provide the greatest information about the chemical composition of the chlorophyll, and thus the health the plants surveyed. In this study, the authors focus on analyzing how crop structure, specifically leaf area index (LAI), in corn affects the capture and analysis chlorophyll using UAVs. The researchers used an eBee AG Sensefly UAV with a Sequoia camera to capture imagery and processed the imagery using Pix4Dmapper Pro to create reflectance images in the green, red, red-edge, and NIR bands. Normalized Difference Red Edge Index was then calculated from the UAV data to determine index’s the ability to determine crop condition, chlorophyll content, and LAI. The researchers found that when LAI was incorporated in as a parameter in the predictive algorithm for canopy chlorophyll content retrieval, chlorophyll mapping accuracy displayed an improvement. Of special importance for precision agriculture application is the fact the researchers show that including LAI produced a marked improvement in the capture of chlorophyll continent when using UAVs to capture data from agriculture fields displaying a high canopy cover variability.


Barry Allred, N. E. (2018). Effective and efficient agricultural drainage pipe mapping with UAS thermal infrared imagery: A case study. Agricultural Water Management, 132-137.

In the Midwest United States, a significant portion of farmland has drainage tile to remove excess water from fields. Locating this drainage  tile for repairs, additions, and water quality monitoring programs sometimes requires locating tile for which maps no longer exist. Mechanical location methods prove either destructive to fields and tile or time-intensive. In this study, the researchers show that utilizing UAVs equipped with appropriate sensors allows farmers, contractors, and environmental monitors to locate tile locations. The researchers used a senseFly SA eBee Ag fixed-wing drone combined with Pix4Dmapper Pro to process data captured by the UAS survey. Two different cameras were used, a Sequoia camera and senseFly SA thermoMap camera. In total, six bandwidths were captured: RGB, green wavelength, red wavelength, red edge wavelength, NIR wavelength, and TIR wavelength. The researchers show that under dry soil conditions, TIR is most successful in locating drainage tile. However, even this success rate was only 60%. Under moist or wet field conditions, the researchers conclude that VIS or NIR imagery would provide a higher success rate than the TIR does under dry field conditions. In conclusion, the researchers state that further research and development is needed to make UAS more accurate and successful in locating field drainage tile.


Fancisco-Javier Mesas-Carrascosa, J. T.-S.-R.-F.-M.-S.-G. (2015, October). Assessing Optimal Flight Parameters for Generating Accurate Multispectral Orthomosaicks by UAV to Support Site-Specific Crop Management. Remote Sensing, 7(10), 12793-12814.

Precision agriculture relies substantially on crop data that is increasingly collected by UAVs. Optimizing UAV flight time ensures economical operation and eliminates wasted time. In this article, the authors discuss the requirements for planning and executing successful UAV flights in precision agricultural applications. The authors used a quadcopter equipped with TetraCam mini-MCA6 multispectral camera capturing bandwidths in the visible and NIR spectrum. The authors conducted their trials to answer three main questions: determine the technical requirements for a UAV to produce the most accurate imagery based on spatial resolution and spectral discrimination; determine how these requirements affected weed detection in crops; determine if UAVs can cover the entire area of interest. Based on their trials and data manipulation, the authors conclude that, to maximize spatial resolution and spectral discrimination, a flight plan with 10% security margin with a cruising mode at 60 m AGL is optimal.


James W. Jones, J. M.-C. (2017). Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science. Agricultural Systesm, 269-288.

Agricultural systems science is becoming increasingly popular as farming evolves to provide greater yields on less land with fewer inputs. In this article, the authors discuss the many applications of agricultural systems sciences to multiple farming systems as well as the technology required to support such system sciences and analytics. The authors indicate UAVs as technology becoming integral to supporting cropping decision support systems by providing spatial and spectral soil and crop data. When discussing precision agriculture, the authors indicate that UAVs will continue to develop and change the way precision agriculture affects economic and environmental aspects of farming.


Janna Huuskonen, T. O. (2018). Soil Sampling with drones and augmented reality in precision agriculture. Computers and Electronics in Agriculture, 25-35.

Gathering soil samples is a key part in growing optimal yields; however, determining accurate sampling patterns based on soil type can be challenging. In this article, Huuskonen and Oksanen explore the novel concept of combining UAV soil imagery with augmented reality (AR) glasses. The authors used a DJI Phantom 4 Pro to capture RGB images of freshly plowed fields. These images were georeferenced and then segmented into regions based on spatial and color clustering. These regions were then used as the input data in the smartglass software to create sample regions and sample points. The authors conclude that joining drone imagery and AR is a successful method to gather soil samples for precision farming.


Jayme Garcia, A. B. (2018). Perspectives on the use of unmanned aerial systems to monitor cattle. Outlook on Agriculture, 47(3), 214-222.

Currently, UAV use in agriculture focuses primarily on farming without much application in ranching.  In this article, the authors discuss the factors hindering implementation UAVs to survey cattle populations. Aircraft and sensor type offer good options for livestock surveillance. Fixed wing UAVs and thermal sensors provide effective image gathering platform. However, processing and analyzation images to accurately and economically count livestock pose significant challenges. Manual counting is expensive, time consuming, and error-prone. Pixel recognitions software is error prone when used to analyze the highly diverse imagery. However, using artificial intelligence presents a possible solution for counting livestock in UAV gathered images. Thus, the authors conclude that, as technology continues to develop, adoption of UAVs by livestock producers is inevitable.


Jessica Y. L. Tay, A. E. (2018, October). Reaching new heights: can drones replace current methods to study plant population dynamics. Plant Ecology(10), 1139-1150.

Conventional vegetation monitoring methods provide a high degree of spatial accuracy but at a low spatial extent because of the time and resources required for field sampling. Further, extrapolation of such data to a landscape is not always accurate. In this study, the authors investigate the use of UAVs to capture vegetation imagery using a variety of sensors to record and subsequently classify vegetative cover on a landscape scale. The authors used a Falcon 8 octocopter equipped with three Sony Alpha 6000 cameras capturing images in the RGB, red-edge, and NIR spectrums. The final orthomosaic images were generated from a dense point cloud and a 3D mesh. To classify the orthomosaics, training polygons were created by grouping pixels of similar colors with polygons and then assigning them specific categories. For their study, the authors found that images captured in the RGB spectrum provided the greatest degree of accuracy. In conclusion, the authors determine that UAVs hold great potential in vegetation classification as UAVs and software become increasingly user-friendly.


  1. K. Freeman, R. S. (2014). Politics & technology: U. S. polices restricting unmanned aerial systems in agricultural. Food Policy, 302-311.

UAVs hold the potential to positively impact multiple civilian enterprises, among them agriculture. However, public concerns about privacy infringement and safety of UAVs have negatively impacted policy decisions.  In this article, Freeman and Freeland investigate UAV policy decisions as they relate to the agricultural sector, analyze the effects of UAV policy on agriculture, propose methods of UAV integration into the commercial U.S. market, and describe the opinions affecting UAV policy.  The authors looked at legislation from multiple states effecting agricultural UAV operations. The authors found that 46 states with UAV legislation, 24 of which would impact agricultural UAV usage. The authors conclude that proactive measures by the UAV community, court decisions, and economic reality will be the main factors deciding the successful acceptance of UAVs in commercial applications.


Patricia K. Freeman, R. S. (2015). Agricultural UAVs in the U. S.: potential, policy, and hype. Remote Sensing Applications: Society and Environment, 35-43.

UAV use in commercial markets is a relatively new concept, surrounded by buildup from developers and by skepticism from protentional users. In this paper, Freeman and Freeland, discuss both the reality and the hype surrounding UAV use in agriculture. In general, they found the news and articles examined provided positive reviews of UAV use in agricultural applications with some use of superlatives, indicating a moderate amount of hype. Also noted by the authors was the frequent omission of setup cost and skill required for successful UAV implementation in agriculture. Based on these finding, Freeman and Freeland conclude that media expectations for UAV usage in agriculture will not be met in the short term; however, as UAV benefits are substantiated, the long-term agricultural demand for UAVs will increase.


Rashash A*, E.-N. A. (2015). Rain Water Harvesting Using GIS and RS for Agriculture Development in Northern Wester Coast, Egypt. Journal of Geography & Natural Disasters, 5(2), 1000141.

An increasing need for irrigation water is developing as agriculture continues to expand into semi-arid regions of the world. In this study, the authors investigate the use of remote sensing combined with GIS to assess the suitability of areas for dam construction for the capture and storage of excess overland flow. The authors seek to develop a spatial GIS model that will aid in site assessment and selection for dam construction. For this study, the authors used satellite imagery, specifically imagery captured by Landsat8. However, the imagery used could have easily been captured by a drone. After imaging processing, the imagery was used to create a DEM of the study areas. The authors then used Arc Hydro software to produce the hydrological modeling for the terrains.


Redmond Ramin Shamshiri, C. W. (2018, July). Research and developoment in agricultural robotics: A perspective of digital farming. International Journal of Agricultural adn Biological Engineering, 11(4), 1-14.

As agriculture evolves to embrace technology, automation of routine farm tasks presents an opportunity for farmers to reduce labor costs while increasing their operation’s efficiency and productivity. Among multiple robotic possibilities discussed by the authors is the use of UAVs to gather imagery from which  3D models are constructed for use by field robots. The authors conclude that as the agricultural workforce declines and production costs increase, routine farm tasks will become increasingly automated.


Rohit Sharma, S. S. (2018). Big GIS analytics framework for agricultura supply chains: A literature review identifying the current trends and future perspectives. Computers and Electronics in Agriculture, 103-120.

As the global population grows, increasing pressures are exerted on current agricultural systems. Demand for increased production on less land, increased functionality of ecosystem services, and growing awareness of environmental stewardship all pose new challenges for agriculture. In this paper, the authors explore the capability of GIS to aid in solving these challenges. They identify five components, GPS, GIS, remote sensing, and geomapping sensors and electronic communication devices, that comprise precision agriculture; the technology at the forefront of bringing agriculture into the twenty-first century. The authors indicate that UAVs are playing an integral role in gathering data for precision agriculture in a variety of applications. Monitoring crop conditions, soil mapping, and yield estimation are among some of the most popular applications within precision agriculture. The authors conclude that development of platforms incorporating multiple data streams is needed to truly utilize data gathered by UAVs.


Roope Nasi, N. V. (2018, July 7). Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and  Aircraft Based Spectral and Photogrammetric 3D Features. Remote Sensing, 1-32.

Monitoring fertility levels in growing crops is a central tenant of precision agriculture. Monitoring plant health allows farmers to plan nutrient applications and potential yields. In this study, the authors discuss the use UAVs to gather information on nitrogen levels and on biomass in small grains. For the study, the authors used a hexacopter equipped with a hyperspectral camera and a Samsung NX500 RGB camera. Images were processed with Agisoft Photoscan software to orient the images and to create point clouds. The authors found biomass estimation to be most accurate when RGB images and 3D features were combined. Estimations for nitrogen content in the barley were most accurate when both data from both RGB and hyperspectral sensors was used. The authors conclude that, although hyperspectral data combined with 3D features produced the most accurate estimations, RGB data combined with 3D features provided results nearly as accurate. Moving ahead, the authors state that generalized estimation models are required to replace the need for in-situ calibration data


Stein, N. (2017). The Future of Drones in the Modern Farming Industry. Geomedia, 21(5), 31-37.

Today, agriculture faces several challenges. Farmers contend with low commodity prices, high input costs, and stringent environmental regulations. In this article, Nathan Stein, Ag Solutions Manager at senseFly, discusses the role data gathered by of UAVs can have in increasing farmers productivity while reducing the impacts felt by the agricultural industry from current market and environmental pressures. Highly detailed crop data gathered by drones allows farmers to make informed decisions about product application and crop yield, thus positively affecting production costs and marketing strategies. When used in an integrated workflow, drone-gathered data can be pushed directly to tractors and sprayers to create or adjust application prescriptions. Currently, further development of end-to-end solutions are needed to facilitate the integration drone-gathered data in farming activities. As agriculture continues to face growing sustainability and economic challenges, UAV data and solutions promise to aid farmers in their quest for sustainable livelihoods.


  1. Hovhannisyan, P. E. (2018). Creation of a digital model of fields with application of DJI Phantom 3 drone and the opportunities of its utilization in agriculture. Annals of Agrarian Science, 177-180.

As twentieth century agricultural methods become increasingly obsolete, implementation of technology in the agricultural sector is becoming not only more widespread but also necessary. In this study the authors discuss the use of UAVs to generate digital field models and their application in precision agriculture. For this study the authors used a DJI Phantom 3 drone equipped with a 12.4 MP camera to capture the imagery and used Pix40 software and ArcMap to process the imagery. Imagery of a fruit orchard, open land, and a raspberry field was captured from which a topographical, thermal, and 3D models were created and uploaded to Google Earth. The authors demonstrate the ability to gather useful, current agricultural field data with UAVs.