Air and Spray Mixture Temperature Effects on Atomization of Agricultural Sprays

spray drift associated with agrochemical operations is highly dependent upon the physical properties of the spray solution with respect to how they influence atomization.  This study examined two spray solutions across a wide range of solution temperatures for two nozzles spraying into two high speed airstreams.  The dynamic surface tension and viscosity of the spray solutions were also measured across the range of temperatures.  Generally as the solution temperature increased, the dynamic surface tension and viscosity both decreased.  This decrease in physical properties was directly related to the decrease in spray droplet size for all nozzles and airspeeds tested.  Monitoring of spray solution temperature throughout the spray system of a typical agricultural aircraft demonstrated that while changes in the spray solutions temperature do occur, the range is much less than the ranges across which this atomization study covered.  During a typical aerial application scenario, the temperature of a spray solution and the associated physical properties and atomization characteristics would not be expected to see significant variation

Active Magnetoelectric Motion Sensing: Examining Performance Metrics with an Experimental Setup

Magnetoelectric (ME) sensors with a form factor of a few millimeters offer a comparatively low magnetic noise density of a few pT/Hz−−−√ in a narrow frequency band near the first bending mode. While a high resonance frequency (kHz range) and limited bandwidth present a challenge to biomagnetic measurements, they can potentially be exploited in indirect sensing of non-magnetic quantities, where artificial magnetic sources are applicable. In this paper, we present the novel concept of an active magnetic motion sensing system optimized for ME sensors. Based on the signal chain, we investigated and quantified key drivers of the signal-to-noise ratio (SNR), which is closely related to sensor noise and bandwidth. These considerations were demonstrated by corresponding measurements in a simplified one-dimensional motion setup. Accordingly, we introduced a customized filter structure that enables a flexible bandwidth selection as well as a frequency-based separation of multiple artificial sources. Both design goals target the prospective application of ME sensors in medical movement analysis, where a multitude of distributed sensors and sources might be applied

Current status and future directions of precision aerial application for site-specific crop management in the USA

The first variable-rate aerial application system was developed about a decade ago in the USA and since then, aerial application has benefitted from these technologies. Many areas of the United States rely on readily available agricultural airplanes or helicopters for pest management, and variable-rate aerial application provides a solution for applying field inputs such as cotton growth regulators, defoliants, and insecticides. In the context of aerial application, variable-rate control can simply mean terminating spray over field areas that do not require inputs, terminating spray near pre-defined buffer areas determined by Global Positioning, or applying multiple rates to meet the variable needs of the crop. Prescription maps for aerial application are developed using remote sensing, Global Positioning, and Geographic Information System technologies. Precision agriculture technology has the potential to benefit the agricultural aviation industry by saving operators and farmers time and money

Pesticide application technology in the 21st century

Since 1996, glyphosate has been the predominant herbicide used postemergence for weed control in corn, soybean and cotton in the United States. Because of that, glyphosate-resistant weeds have become increasingly more prevalent in glyphosate-resistant crops which have forced many growers to use other herbicides. Herbicide programs that relied primarily on glyphosate for weed control often used rates as low as 5 gallon/acre (GPA). The other herbicides being used in row crops often require a higher carrier volume according to the label when compared to glyphosate which can be burdensome to the applicator, requiring the transport of more water, more refills and more potential of mixing errors. Additionally, there is growing concern about off-target movement of pesticides and what can be done to mitigate pesticide drift. Both drift and efficacy can be affected by spray quality and application decisions such as nozzle selection, operating pressure and components of the spray solution

Development and prospect of unmanned aerial vehicle technologies for agricultural production management

Unmanned aerial vehicles have been developed and applied to support agricultural production management. Compared with piloted aircraft, an Unmanned Aerial Vehicle (UAV) can focus on small crop fields at lower flight altitudes than regular aircraft to perform site-specific farm management with higher precision. They can also “fill in the gap” in locations where fixed winged or rotary winged aircraft are not readily available. In agriculture, UAVs have primarily been developed and used for remote sensing and application of crop production and protection materials. Application of fertilizers and chemicals is frequently needed at specific times and locations for site-specific management. Routine monitoring of crop plant health is often required at very high resolution for accurate site-specific management as well. This paper presents an overview of research involving the development of UAV technology for agricultural production management. Technologies, systems and methods are examined and studied. The limitations of current UAVs for agricultural production management are discussed, as well as future needs and suggestions for development and application of the UAV technologies in agricultural production management

Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras

Consumer-grade cameras are being increasingly used for remote sensing applications in recent years. However, the performance of this type of cameras has not been systematically tested and well documented in the literature. The objective of this research was to evaluate the performance of original and resolution-reduced images taken from two consumer-grade cameras, a RGB camera and a modified near-infrared (NIR) camera, for crop identification and leaf area index (LAI) estimation. Airborne RGB and NIR images taken over a 6.5-square-km cropping area were mosaicked and aligned to create a four-band mosaic with a spatial resolution of 0.4 m. The spatial resolution of the mosaic was then reduced to 1, 2, 4, 10, 15 and 30 m for comparison. Six supervised classifiers were applied to the RGB images and the four-band images for crop identification, and 10 vegetation indices (VIs) derived from the images were related to ground-measured LAI. Accuracy assessment showed that maximum likelihood applied to the 0.4-m images achieved an overall accuracy of 83.3% for the RGB image and 90.4% for the four-band image. Regression analysis showed that the 10 VIs explained 58.7% to 83.1% of the variability in LAI. Moreover, spatial resolutions at 0.4, 1, 2 and 4 m achieved better classification results for both crop identification and LAI prediction than the coarser spatial resolutions at 10, 15 and 30 m. The results from this study indicate that imagery from consumer-grade cameras can be a useful data source for crop identification and canopy cover estimation

Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras

Consumer-grade cameras are being increasingly used for remote sensing applications in recent years. However, the performance of this type of cameras has not been systematically tested and well documented in the literature. The objective of this research was to evaluate the performance of original and resolution-reduced images taken from two consumer-grade cameras, a RGB camera and a modified near-infrared (NIR) camera, for crop identification and leaf area index (LAI) estimation. Airborne RGB and NIR images taken over a 6.5-square-km cropping area were mosaicked and aligned to create a four-band mosaic with a spatial resolution of 0.4 m. The spatial resolution of the mosaic was then reduced to 1, 2, 4, 10, 15 and 30 m for comparison. Six supervised classifiers were applied to the RGB images and the four-band images for crop identification, and 10 vegetation indices (VIs) derived from the images were related to ground-measured LAI. Accuracy assessment showed that maximum likelihood applied to the 0.4-m images achieved an overall accuracy of 83.3% for the RGB image and 90.4% for the four-band image. Regression analysis showed that the 10 VIs explained 58.7% to 83.1% of the variability in LAI. Moreover, spatial resolutions at 0.4, 1, 2 and 4 m achieved better classification results for both crop identification and LAI prediction than the coarser spatial resolutions at 10, 15 and 30 m. The results from this study indicate that imagery from consumer-grade cameras can be a useful data source for crop identification and canopy cover estimation

Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras

Consumer-grade cameras are being increasingly used for remote sensing applications in recent years. However, the performance of this type of cameras has not been systematically tested and well documented in the literature. The objective of this research was to evaluate the performance of original and resolution-reduced images taken from two consumer-grade cameras, a RGB camera and a modified near-infrared (NIR) camera, for crop identification and leaf area index (LAI) estimation. Airborne RGB and NIR images taken over a 6.5-square-km cropping area were mosaicked and aligned to create a four-band mosaic with a spatial resolution of 0.4 m. The spatial resolution of the mosaic was then reduced to 1, 2, 4, 10, 15 and 30 m for comparison. Six supervised classifiers were applied to the RGB images and the four-band images for crop identification, and 10 vegetation indices (VIs) derived from the images were related to ground-measured LAI. Accuracy assessment showed that maximum likelihood applied to the 0.4-m images achieved an overall accuracy of 83.3% for the RGB image and 90.4% for the four-band image. Regression analysis showed that the 10 VIs explained 58.7% to 83.1% of the variability in LAI. Moreover, spatial resolutions at 0.4, 1, 2 and 4 m achieved better classification results for both crop identification and LAI prediction than the coarser spatial resolutions at 10, 15 and 30 m. The results from this study indicate that imagery from consumer-grade cameras can be a useful data source for crop identification and canopy cover estimation

Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras

Consumer-grade cameras are being increasingly used for remote sensing applications in recent years. However, the performance of this type of cameras has not been systematically tested and well documented in the literature. The objective of this research was to evaluate the performance of original and resolution-reduced images taken from two consumer-grade cameras, a RGB camera and a modified near-infrared (NIR) camera, for crop identification and leaf area index (LAI) estimation. Airborne RGB and NIR images taken over a 6.5-square-km cropping area were mosaicked and aligned to create a four-band mosaic with a spatial resolution of 0.4 m. The spatial resolution of the mosaic was then reduced to 1, 2, 4, 10, 15 and 30 m for comparison. Six supervised classifiers were applied to the RGB images and the four-band images for crop identification, and 10 vegetation indices (VIs) derived from the images were related to ground-measured LAI. Accuracy assessment showed that maximum likelihood applied to the 0.4-m images achieved an overall accuracy of 83.3% for the RGB image and 90.4% for the four-band image. Regression analysis showed that the 10 VIs explained 58.7% to 83.1% of the variability in LAI. Moreover, spatial resolutions at 0.4, 1, 2 and 4 m achieved better classification results for both crop identification and LAI prediction than the coarser spatial resolutions at 10, 15 and 30 m. The results from this study indicate that imagery from consumer-grade cameras can be a useful data source for crop identification and canopy cover estimation