A jupyter notebook with crop analysis algorithms utilizing digital elevation models, dtm and multi-spectral imagery (R-G-B-NIR-Rededge-Thermal) from a MicaSense Altum sensor processed with DroneMapper Remote Expert.

https://github.com/dronemapper-io/CropAnalysis

Due to limitations on git file sizes, you will need to download the GeoTIFF data for this project from the following url: https://dronemapper.com/software/DroneMapper_CropAnalysis_Data.zip

These basic algorithms are intended to get you started and interested in multi-spectral processing and analysis.

The orthomosaic, digital elevation model, and dtm were clipped to an AOI using GlobalMapper. The shapefile plots were also generated using GlobalMapper grid tool. We highly recommend GlobalMapper for GIS work!

We cloned the MicaSense imageprocessing repository and created the Batch Processing DroneMapper.ipynb notebook which allows you to quickly align and stack a Altum or RedEdge dataset creating the correct TIF files with EXIF/GPS metadata preserved. These stacked TIF files are then directly loaded into DroneMapper Remote Expert for processing.

This notebook assumes the user has basic knowledge of setting up their python environment, importing libraries and working inside jupyter.

View the entire Medium article here: https://medium.com/dataseries/data-science-crop-analysis-notebook-using-6-band-micasense-altum-and-dronemapper-processed-uav-3683dbc21836

Load Digital Elevation Model and Orthomosaic

In this step, the digital elevation model and 6 band orthomosaic are loaded for processing. The data is in UTM16N WGS84 projection and has a pixel size (GSD) of 5cm for the orthomosaic and 10cm for the DEM.

Load Plot 1 AOI and Generate NDVI

Next, we use the NIR and RED channels from the orthomosaic to compute a standard NDVI. The plots of interested are also loaded and displayed. Utilizing Rasterio, GeoPandas and Earthpy makes easy!

Generate NDVI Zonal Statistics For Each Plot

Using the NDVI we generated in the previous step, we use the RasterStats library to quickly compute zonal statistics for each of the plots. That data is stored in a GeoPandas dataframe and shown below.

Load Plot 2 AOI & Compute DEM Canopy Mean Height For Each Plot

Here we load the plot 2 area of interest, using the DEM we can compute zonal statistics for each plot. This gives us a canopy height reading for each pixel inside a plot.

Compute Thermal Mean For Each Plot

The thermal band (6) in the processed orthomosaic shows stitching artifacts which could likely be improved using more accurate pre-processing alignment and de-distortion algorithms. You can find more information about these functions in the MicaSense imageprocessing github repository. See notes at the top of this notebook.

Load Plot 1 AOI & Compute Volume/Biomass For Each Plot

Using the DEM and DTM we can create a surface model with a ground reference of 0 meters. This allows us to calculate the volume for each plot and all pixels contained inside that plot. The volume is calculated from the ground (0m) to the top of the canopy for every pixel inside a plot.

Load Plant Count AOI & Count Plants

Using the surface model, we load the plant count AOI and clip our raster data to it. This process allows us to segment the plants we want to count. We then create a binary mask to allow for simple processing with OpenCV blob detector. The plant blobs are shown in white on a black background in the binary image below. The detector is run and we get our plant count, next we iterate through each of the blobs to find the center point. With the pixel center of each blob we can do a xy lookup against the original raster to determine the spatial position of each plant.

Plant Count: 310

Thanks! Keep an eye out for future notebooks and algorithms! DroneMapper.com

Hi Guys,

Recently we built one 2.5 hours long-endurance VTOL fixed-wing plane using the Pixhawk Cube flight controller. Auto flight demo: https://youtu.be/H-M0Mm12N7Y

Specification of the VTOL

Hi,

A week ago I placed an order of over $300 on rctimer.com site, PayPal account was charged but I never got order confirmation from rctimer.

Two days later, the site disappeared and emails are bouncing back.

I know they sell on AliExpress as well, be very careful.

Gal

The 17th China International Social Public Safety Expo (CPSE)will be held from October 28^th-31 ^th_,2019 in Shenzhen Convention & Exhibition Center, Shenzhen,  China. At that time, it will present the most advanced artificial intelligence, big data, and intelligent security products to the world.

The largest scale in the exhibition

This CPSE will be held at all exhibition halls of the Shenzhen Convention and Exhibition Center, The scale of this exhibition is the largest in history, with an exhibition covers an area of 30,000㎡, and hundreds of industry brand companies and more than 2,000 exhibitors participated in the exhibition. As a high-end brand event in China's security industry and one of the important international security expo with global influence, the Shenzhen security expo will focus on showing smart city solutions, security prevention solutions, police equipment, emergency rescue, Bio-recognition, artificial intelligence machines, and other industries. This exhibition has attracted great attention among manufacturers, engineers, and distributors in many industries across the country.

UAVs play an important role in security

In the construction of smart security, UAVs play an important role. In the security scenes of military reconnaissance, emergency flood control, drought prevention, border surveillance, police monitoring, forest fire monitoring, and environmental monitoring, the drone acts as a "security weapon" for the reconnaissance unit. It can perform various tasks such as air surveillance, daily patrol, and rapid air attack. It can cooperate with the background command and control system, with the ability of situation sharing integration, collaborative planning and scheduling, remote command, and diversified intelligence processing.

As one of the exhibitors of this Shenzhen security expo, MMC has established a ground-to-air integrated security system with its intelligent and advanced complete set of UAV products and big data dispatching backstage, which has played a huge advantage in the security field. MMC will exhibit the latest automatic drone inspection system security solution at this expo, including the full range of core intelligent application products such as “Sky eye” dispatching platform, high-performance drones, high-definition zoom tracking gimbal, and automatic police equipment. Today, MMC drones have evolved from a single “moving eye” to a versatile “smart cloud brain”.

The world's first industrial UAV industry chain

As the leading industrial UAS manufacturer, MMC is the first UAV company in the world to get through the whole industrial UAV industry chain and achieves win-win cooperation with upstream and downstream industries with a positive and open mind. MMC has achieved independent research and development, design and production of core components including full carbon fiber integrated molding case, power system, flight control, image transmission, ground station, etc. All of the gimbal adopt standardized fast-release interface, which has strong compatibility.

In addition to opening up the industrial drone industrial chain, MMC has strong product customization capabilities in the UAV industry's application solutions. It not only mastering the systematic management of cost and quality, but also continuously develops towards the direction of intelligent solutions. In the global drone industry chain, MMC has been at the forefront.

"Technology security in the Sky". At this security expo, MMC will build a technology security and smart city through the big data integrated management platform based on the automated inspection system and diverse equipment.

Shenzhen CPSE 2019 is about to begin, MMC sincerely invites you to visit. We are here waiting for you.

Address: Shenzhen Convention and Exhibition Center, China

Booth number: Hall 8-845.

My hacked Altair Aerial Blackhawk continues to serve as a great test platform for experiments in DIY flight controllers. So, when I heard the buzz surrounding the ESP32 line of Arduino-compatible microcontrollers (Dual 240 MHz cores, WiFi/Bluetooth on-chip), I knew I had to try one of these boards on the "Hackhawk".  

If you've been following the developments in the ESP32 community, you know that the smallest ESP32 board is the recently-released TinyPICO.  With its petite form factor, this board seemed to me like an obvious choice for indoor MAVs, and I wasn't disappointed.  This wiki shows how I got the Hackhawk flying with the TinyPICO, using my favorite IMU solution and some small additions to my platform-independent C++ flight-control firmware toolkit. 

Future plans for this project include:

1. Finding (or designing) an IMU that will mount on the TinyPICO without sacrificing two of the GPIO pins to serve as power and ground.
2. Switching from standard / old-school ESCs to DSHOT600, using C++ code I've already tested on ESP32 boards. (The ESP32's RMT signal module and FreeRTOS kernel make this especially easy.)
3. Using the TinyPICO's on-board Bluetooth (or wifi) for real-time sensor telemetry, and possibly even control from a mobile device.

BioCarbon Engineering technician prepares the UAV for the seeding demonstration at Ellerslie site in Edmonton.

Drones might be seen as pests by Gatwick Airport traffic control, but increasingly farmers are beginning to see them as pest-killers.

This is one of many uses drones have in agriculture. One obvious use is for aerial images, which has led to its take-off in the consumer market. But this same use is making it popular in the agriculture sector.

Drones take the hard work out of planting

European start ups such as HummingBird Technologies and Delair offer aerial imaging for precision agriculture.

One of the most radical ones is British start up BioCarbon Engineering which stated its goal is to plant 500 billion trees by 2060. Its drones fly 3m above the ground and drop two seeds per second.

It is targeting areas affected by logging and natural disasters such as bushfires. Beyond this it can also monitor the growth of seeds. Greening Australia is one of the organisations in partnership with BioCarbon.

Drones can also seek out weeds that humans either cannot or take lengthy periods of time to discover.

Beyond just being reactive, drones can also monitor crop conditions on an ongoing basis. In July, Iowa-based Rantizo won the right to conduct drone-based agricultural spraying and provides solutions to the entire Midwest.

Founder Michael Ott said this could also solve labour shortages that come at the same time food needs are booming.

“Soon there will be 9 billion people in the world but fewer and fewer are working in agriculture,” he said. “We need ways to create more food with fewer workers, so we have to automate and use new technologies like drones.”

He noted his company was popular among a wide variety of agriculture players. “We’ve had interest from wildflower seed producers, hemp growers, commodity growers, berry farmers, vineyards and others,” he told Commercial UAV News last month.

Bug-fighting

Another thing drones can do is fight pests and one pest in particular is causing big problems right now.

The fall army worm has invaded more than 80 crop varieties in over 100 countries since 2016. In 2018 alone it caused $US4.6 billion in losses. Developing countries, including Zambia and Vietnam, have been terribly hit with farmers lacking expertise to deal with sudden infestation.

These creatures can destroy a crop field in hours, fly 1,000km in one night and lay 1,000 eggs in its life. Chemical spraying has been the traditional way to resolve this but it is time consuming and exposes farmers to the chemicals themselves.

One Chinese company, XAG, has developed a drone that does the job en-masse. It does so using 30 per cent less peptides and 90 per cent less agricultural water. It has been tested in the Guangzi province and this growing season killed up to 98 per cent of larvae.

Hello UAV enthusiasts,

I recently created a slide deck on the topic of sensors for attitude estimation (i.e. gyroscopes, accelerometers and magnetometers). In these slides, I review

1. Installation considerations,
2. Stochastic models,
3. Bias characterization,
4. Sensor calibration, and
5. Conceptual models.

The methods and concepts contained in these slides have been "battle tested''. That is, these are the methods and concepts I used while developing a lightweight autopilot system for UAVs. I hope you find some of this material useful. These slides were part of a graduate course titled "Control of Marine and Aerial Vehicles".

All the best,

Matthew

silic_uav_sensors_v2.pdf

silic_uav_sensors_v3.pdf

We're seeking a cargo drone partner for a Gates Foundation Project with the Center for Disease Control and the World Health Organization in Cameroon. Grateful if you could help us share this opportunity: http://bit.ly/2OUh6Av

By using Intels OpenVINO and neural computing stick (NCS1) the inference speed of my neural network is increased from 5 frames per second (fps) to 20 using a RPI3. This enabled a far better obstacle avoidance performance of my drone. More info at https://github.com/avncalst/drone_cnn.

Over 150 cargo drone flights have delivered over 1,000 patient samples from 8 remote health facilities to 2 regional hospitals for rapid testing. The best part? The deliveries are being run entirely by Nepali drone pilots & Nepali health experts. Full details and videos here.

YANGDA Mapird is a professional VTOL(vertical take-off and landing) fixed-wing drone for mapping, aerial survey, and inspection mission. It comes with two tilting motors and two lift motors, which will allow the mapping drone to ascend like a helicopter. For the airplane survey mission, the front two tilting rotors will transition to forward mode and make the drone fly like a fixed-wing plane.

Rugged airframe
We reinforce the two wings of Mapird VTOL drone using 3K carbon fiber. Therefore, the frame structure is greatly strengthened.

Fast deploy
Due to its modular airframe design, Mapird mapping VTOL can be set up in less than 5 minutes by a single man.

Long endurance
Equipped with one 200 grams mapping camera, one unit 6S 22000mAh battery, the Mapird survey drone can fly up to 1.5 hours and complete an 8 square kilometer area mapping job in a single flight.

Big inner space for payload
Mapird mapping drone comes with a big internal space to accommodate various mapping cameras and payload, like SONY A7R/A6000/A5100 camera and oblique camera.

No piloting skills needed
Mapird can conduct fully autonomous mapping and survey missions without human interaction.

The future is coming! Now drones can be charged in-flight in 6 minutes!

See the post on Global Energy Transmission site: http://getcorp.com/get-reveals-next-generation-of-its-in-flight-wireless-charging-solution-for-drones/ - and don't forget to subscribe to our mailing list, more news will follow — stay tuned! Like & share!

From Hackaday:

Many of us have projects that end up spanning multiple years and multiple iterations, and gets revisited every time inspiration strikes and you’ve forgotten just how much work and frustration the previous round was. For [Daniel Riley] AKA [rctestflight] that project is a solar powered RC plane which to date spans 4 years, 4 versions and 13 videos. It is a treasure trove of information collected through hard experience, covering carbon fibre construction techniques, solar power management and the challenges of testing in the real world, among others.

Solar Plane V1 had a 9.5 ft / 2.9 m carbon fibre skeleton wing, covered with transparent film, with the fragile monocrystaline solar cells mounted inside the wing. V1 experienced multiple crashes which shattered all the solar cells, until [Daniel] discovered that the wing flexed under aileron input. It also did not have any form of solar charge control. V2 added a second wing spar to a slightly longer 9.83 ft / 3 m wing, which allowed for more solar cells.

Solar Plane V3 was upgraded to use a single hexagonal spar to save weight while still keeping stiff, and the solar cells were more durable and efficient. [Daniel] did a lot of testing to find an optimal solar charging set-up and found that using the solar array to charge the batteries directly in a well-balanced system actually works equally well or better than an MPPT charge controller.

V4 is a departure from the complicated carbon fibre design, and uses a simple foam board flying wing with a stepped KF airfoil instead. The craft is much smaller with only a 6 ft / 1.83 m wingspan. It performed exceptionally well, keeping the battery fully charged during the entire flight, which unfortunately ended in a crash after adjusting the autopilot. [Daniel] suspects the main reasons for the improved performance are higher quality solar panels and the fact that there is no longer film covering the cells.

Designing electric multicopters for a specific flight time is a complex multidisciplinary optimization problem. This short blog post is some of my thoughts on how to think about this problem. Obviously, there so many different ways of doing this and welcome suggestions and other constructive discussions.

From an energy conversion standpoint, multirotors is a pretty simple systems. Energy is available from the batteries which is routed via ESCs to the motor and the propeller. 

The challenging part is that batteries do not always provide the same efficiency(energy). They are dependent on the power draw (watts,current). For most batteries, the higher the current you draw from the battery, the lower the total-energy available from them. This is mainly due to the internal resistance of the batteries which is pretty low (milliohms or less), but not zero. A typical characteristics of few of the different battery cells are given below. The y-axis shows the energy density (Wh/Kg) of the battery and x-axis shows the power that was drawn from one Kg of battery.

Notice how the increased drain causes the energy capacity to drop sharply for all the cells. 

If we ignore the ESCs power loss characteristics, the other significant power conversion is from the electrical energy received to the motor and the thrust provided by the propeller. One intuitive way to think about this conversion is W/Kg. This is the watts input to the system for every Kg of thrust generated by the propeller. 

If we ignore the motor and consider just the propeller, we can define this as

“Mechanical power (watts) supplied to rotate the propeller / Kg of thrust”.

If we consider the motor, this unit becomes “Electrical power input to the motor (watts) / Kg of thrust”.

Shown below is an “electrical watts/Kg of thrust” of various sizes of propellers tested with different motors. The x-axis shows the “electrical watts/Kg of thrust” and y-axis is the total thrust generated by the propeller. Each color shows a propeller of a particular size.

The three things to note are 

• How the same propeller can have widely varying efficiency with different motors. 
• How a given propeller drops its efficiency for higher thrust
• The higher efficiency of a larger propeller 

Designing an optimal multirotor requires matching the characteristics of the battery to those of the motor and the propeller. Would love to discuss more about this is in a later blog