Date: Tue Aug 2, 2016
Time: 8:00 AM - 9:40 AM
The COSI-system is a novel compact hyperspectral imaging solution designed for small remotely piloted aircraft systems (RPAS). It is designed to supply accurate action and information maps related to the crop status and health for precision agricultural applications. The COSI-Cam makes use of a thin film hyperspectral filter technology which is deposited onto an image sensor chip resulting in a compact and lightweight instrument design.
This paper reports on the agricultural monitoring missions in 2015 which have been executed with the COSI-system over hundreds of experimental field plots containing 40 different varieties of winter wheat presenting a gradient of sensitivity to fungal leaf diseases. In this experiment two different treatments have been executed one with fungicides and the other one without. In total 4 missions with a RPAS have been conducted over the test area. The paper describes the preliminary results of the discrimination between the different treatments and varietal sensitivity as observed by the COSI-Cam.
Crop advisors and farmers increasingly use drone data as part of their decision making. However, the vast majority of UAS-based vegetation mapping services support only the calculation of a relative NDVI derived from compressed JPEG pixel values and do not include the possibility to include more complex aspects like soil correction. In our ICPA12 contribution, we demonstrated the effects and consequences of the above shortcomings. Here, we present the stepwise development of a solution to ensure reliable input for crop advisors as a basis for site-specific crop management based on drone data. UAS flights are executed with a Trimble UX5 (HP) over a Belgian farm comprising four different crop types during a 3 month interval. Vegetation index maps derived from compressed JPEG imagery as well as preprocessed raw sensor data from a modified conventional CIR camera are evaluated against those from a true multispectral camera, and we examine the ability to calibrate the maps. Resulting maps are compared to NDVI values from the active close-range Trimble GreenSeeker crop sensor. Based on the results, we discuss under which conditions the three different data types can be used to complement traditional measurements in addressing within-season crop variability.
The agricultural community is actively engaged in adopting new technologies such as unmanned aircraft systems (UAS) to help assess the condition of crops and develop appropriate treatment plans. In the United States, agricultural use of UAS has largely been limited to small UAS, generally weighing less than 55 lb and operating within the line of sight of a remote pilot. A variety of small UAS are being used to monitor and map crops, while only a few are being used to apply agricultural inputs based on the results of remote sensing. Larger UAS with substantial payload capacity could provide an option for site-specific application of agricultural inputs in a timely fashion, without substantive damage to the crops or soil. A recent study by the National Aeronautics and Space Administration (NASA) investigated certification requirements needed to enable the use of larger UAS to support the precision agriculture industry.
This paper provides a brief introduction to aircraft certification relevant to agricultural UAS, an overview of and results from the NASA study, and a discussion of how those results might affect the precision agriculture community. Specific topics of interest include business model considerations for unmanned aerial applicators and a comparison with current means of variable rate application. The intent of the paper is to inform the precision agriculture community of evolving technologies that will enable broader use of unmanned vehicles to reduce costs, reduce environmental impacts, and enhance yield, especially for specialty crops that are grown on small to medium size farms.
As the world population continues to grow, the need for efficient agricultural production becomes more pressing. The majority of farmers still use manual techniques (e.g. visual inspection) to assess the status of their crops, which is tedious and subjective. This paper examines an operational and analytical workflow to incorporate unmanned aerial systems (UAS) into the process of surveying and assessing crop health. The proposed system has the potential to significantly reduce time, labor and cost while also yielding more accurate results, allowing farmers to better estimate their yield and obtain quantifiable data on troubled areas. The airframe for this study was built from a combination of hobby-grade and scientific components. The aircraft incorporate avionics such as a Pixhawk autopilot system, GPS, and data telemetry links. This allows for completely autonomous flight paths to obtain coverage. The main sensor packages evaluated on the UAS for this study were a digital camera and a multi-spectral imager. Overlapping photos were taken during flight to ensure that there were no gaps in data. Post flight the pictures were geo-located in a world fixed frame (e.g. WGS-84). Data was collected across several flight tests conducted in Brisbane, Australia at the Samford Ecological Research Facility (SERF). The primary output was a georeferenced, orthomosaic of the area in the visible light spectrum. Corresponding normalized difference vegetation index (NDVI) maps to assess vegetation health and vigor as well as a digital elevation models (DEM) that represents the terrain’s surface in 3D were developed as well. The paper describes the process of obtaining and analyzing these results and compares data products generated using software such as QGIS, MicaSense Atlas, and Agisoft Photoscan Professional. Additionally, analysis accuracy, best practices, and improvements of this type of aerial surveying are discussed.
Precision agriculture is a practical approach to maximize crop yield with optimal use of rapidly depleting natural resources. Availability of specific and high resolution crop data at critical growth stages is a key for real-time data driven decision support for precision agriculture management during the production season. The goal of this study was to evaluate the feasibility of using small unmanned aerial system (UAS) integrated remote sensing tools to monitor the abiotic stress of eight irrigated pinto beans (Phaseolus vulgaris L.) with varied irrigation and tillage treatments. A small UAS integrated with a multispectral and an infrared thermal imaging camera was used to collect data of bean field plots on 54, 76 and 98 days after planting (DAP). Indicators such as green normalized vegetation index (GNDVI), canopy cover (CC, ratio of ground covered by crop canopy to the total plot area) and canopy temperature (CT, °C) of crops were extracted from imaging data of the two types of sensor. The statistical difference of the developed indictors in crops with different treatments was analyzed to show their performance in detecting crop stress. The indicators and their combinations of temporal data were also correlated with ground reference yield data to validate the effectiveness in stress monitoring. Results show that the GNDVI, CC and CT were able to differentiate crop grown under full and deficit irrigation treatments at each of the three growth stages. The developed indicators were strongly correlated with crop yield with Pearson correlation coefficients (r) of 0.71 and 0.72 for GNDVI and CC, respectively, in the early growth stage (54 DAP). Canopy temperature also showed high correlation with yield with r of 0.84 at 76 DAP and 0.77 at 98 DAP. Performance of small UAS based indicators in crop yield estimation was improved substantially when temporal data of each indicator were used for correlation. Overall, the small UAS based remote sensing tool has the potential in rapid crop stress monitoring and management.