Embedded special


the selective application of herbicides, but rather than working in the visible bands, the company supplies its hyperspectral imaging technology to a number of partner projects aimed at reducing the volume of herbicide used. According to the company’s CTO Jürgen

Hillmann, Ximea’s main focus for precision agriculture at the moment lies in hyperspectral imaging. Te company is currently developing a demonstration application with the University of Münster to measure agricultural indices such as MBDI – which gives an indication of plant health – in order to optimise fertiliser use. Ximea offers its xiSpec hyperspectral cameras for

Ximea’s xiSpec hyperspectral imaging camera is suited for flying on a drone thanks to its low weight, size and power consumption

2D machine vision cameras and global shutter sensors that are already widely used in industrial factories. With the technology, the machines are able to discern weeds from crops with a 95 per cent success rate. ‘Currently, all the weeds are in one class,’

explained Ben Chostner, vice president of business development at Blue River Technology. ‘We are starting to do more to distinguish the different classes of weeds. Tis information will be valuable in the long term, as we can use it to prepare different mixtures of herbicides, which can then be carried on board the machines in order to deliver more specific treatments to each type of weed.’ According to Chostner, since the company first

introduced its vision-guided spraying technology in 2013 as part of an automated lettuce-thinning system, deep learning has become more widely used. ‘We have therefore changed the entire architecture of our system, both on the soſtware side and the hardware side, to be able to take an image and process it with deep learning,’ he said. ‘We’ve changed our algorithms and also our processors from traditional desktop Intel processors to Jetson GPUs from Nvidia.’ Since 2013, the company has also moved from

using Ethernet to USB3-based cameras, which has significantly increased the speed at which it can pull images off its sensors, ultimately enabling its machines to travel faster through the fields. ‘Our machines travel between four and eight

miles per hour and are anywhere between 20 and 60 feet wide,’ Chostner commented. ‘As the machine moves through the field, we have about one hundred milliseconds to capture an image, get that image off the sensor and into the processor, run our deep learning algorithm, make a decision and spray the plant.’ In total this equates to each machine being able to treat around 40 acres of land per day.

Blue River Technology’s lettuce thinning

machines, which use similar technology to the See and Spray systems, selectively remove unwanted lettuce plants from a field to optimise growing yields. Te LettuceBot uses its vision technology to determine which plants are the most uniform in size and have optimal space to grow, and then applies herbicide to the remaining plants in order to remove them. ‘Our equipment has been

running in lettuce production in the US for about four years now,’ remarked Chostner. ‘It is actually used on about 10 per cent of lettuce produced in the US. Tis year we are expanding the application of this technology to cotton production.’ Te company has primarily

use on drones, which can give a bird’s eye view of a field of crops. Te camera is small and lightweight, and offers a low power consumption of 1.8W, making it ideal for prolonging drone battery life. Te camera is USB3-compliant and can produce 170 hyperspectral imaging data cubes, or up to 1,360 lines, per second. According to Hillmann, line scan cameras are

[Blue River

well suited to drone use. ‘Te advantage is the high spectral resolution, as in this case you have 100 or 150 bands, so it is better for detecting diseases on crops, or detecting unwanted weeds. Te spectral information of healthy plants is compared to those with diseases beforehand, allowing imaging technology to discern between the two.’ Hyperspectral imaging does not

used standard RGB cameras in its machines up until now, thanks to their cost and availability. However, it is now starting to consider using other narrow wavelength bands to obtain information from plants. ‘Tis could involve adding a near-infrared aspect to the cameras,’ Chostner said. ‘Being able to customise cameras to look at bands of light other than the visible would be extremely valuable in our situation.’ Chostner also explained that having a higher

dynamic range in the cameras would aid Blue River Technology’s See and Spray applications. ‘Te prevailing lighting conditions vary quite a bit outdoors, and we oſten run into shadows created by our own equipment. In the worst scenario, there’s both full shadow and full bright sunlight. We have yet to find a camera that, with one exposure, can appropriately expose all of the information in that perspective. Tis would be a game changer for us.’ Camera manufacturer Ximea is also involved in

34 Imaging and Machine Vision Europe • June/July 2017

Technology’s equipment] is used on about 10 per cent of lettuce produced in the US

come without its limitations, however. Enough light needs to be present for accurate readings, which means the technology will sometimes have issues in dark conditions. ‘In every case you have to measure

the light conditions,’ commented Hillmann. ‘If you have a foggy day, then the spectral pattern of the sunlight coming down to the earth will be a

little bit different, and you have to calculate the camera for the current light situation.’ An additional hyperspectral imaging system or

a spectral meter will therefore oſten be attached to the drone alongside the primary camera, so that online measurements of the lighting conditions and spectral response can be performed. Multi-camera systems such as these have become a further focal area for Ximea in recent years. ‘We have developed technologies to aggregate various pictures from different cameras, for example a regular colour camera and a hyperspectral imaging camera,’ said Hillmann, ‘Using regular colour pictures in addition to the hyperspectral imaging allows you to provide a stereoscopic, high-level view of a field. ‘Te idea behind the projects we are involved

in is to use cheaper, smaller systems to optimise agricultural processes,’ concluded Hillmann. O


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56