That is clearly a Brain Computer Interface (BCI) with asymmetric cruciform dipole antennae (ACDA) for thought control transferance (TCT) using JAST from Mega Chips Inc.
Correction: it is not a BCI. It is a BDI (Brain Deity Interface)
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
Correction: it is not a BCI. It is a BDI (Brain Deity Interface)
Dozzaman1977
Regular
Happy new year to everybody gracing this forum 
Link below is a good article about some of the exhibitors at CES including Socionext and brainchip. Cheers
www.autoconnectedcar.com
Link below is a good article about some of the exhibitors at CES including Socionext and brainchip. Cheers
CES Round-Up Automakers, Tech Suppliers & Other Mobility | auto connected car news
CES expanded the mobility space for CES. Here is a round-up of exhibitors and speakers who have announced participation in CES. Stellantis will be the major automaker. Suppliers include lidar, chip and software. Bosch and Continental will participate in CES. The names are listed in alphabetical...
www.autoconnectedcar.com
Fact Finder
Top 20
Only if they or someone else wants to monetise it.So what you are saying is any of those other projects Simon and his team are working on and need to apply the JAST rule they must first pick up the phone to Brainchip?
Oh that secret sauce is so tasty and can be used in a variety of recipes
The more great ideas they come up with that require the JAST rules to be implemented the greater the risk becomes that an entrepreneurial type will pick it up and want to build and sell it. At which time they will need a license.
The research community like the 55,000 engineers developing product applications via the Edge Impulse AKIDA platform create 55,000 opportunities for the same thing to happen.
Same goes for the SiFive, ARM and Intel developer communities. This grouping alone numbers in the tens of thousands.
Same goes for every student passing through the growing AKIDA University program.
Customers, engineers and developers linked to Socionext, Renesas, MegaChips, VVDN, Prophesee, Nviso and MOSCHIP all offer the same opportunities.
Not to forget ISL, Numen, NASA and all the other Space and Defence researchers.
Finally we have our friends at Tata Consulting Services publishing paper after paper promoting innovative SNN solutions.
As I mentioned the other day I am convinced Brainchip has passed the commercialisation tipping point.
Nothing can now stop commercial success being revealed.
Pantene is now the answer.
My opinion only DYOR
FF
AKIDA BALLISTA
Fact Finder
Top 20
I agree positive information needs to be emphasised. I was actually wondering if things were all good at your end which obviously they are.No lol, I think I’ve been just overwhelmed by the great contributions of the 1000 eyes that I failed to register it accordingly, at least it is better to have too much information to register than not enough
And whilst I am posting let me slip in a very Happy New Year to you FF and all the BrainChip fam here, I’m very excited about the year ahead and sharing it here with you all
So Happy New Year
and may good fortune accompany everyone on their journey throughout 2023. 
Regards
FF
AKIDA BALLISTA
Bravo
If ARM was an arm, BRN would be its biceps💪!
Check out this video from Nov 2022 at 2.45 minute mark. Amazon and BMW discussing the new enhancements on the Alexa Custom Voice Assistant.
"There's the ability to stay connected when you're out of range and new features that can make your car more intelligent and more proactive".
"There's the ability to stay connected when you're out of range and new features that can make your car more intelligent and more proactive".
Schnitzel lover
Regular
Happy New Year Chapman..2023 is shaping up to be Brainchips best year, and although our share price isn’t where we all believe it deserves to be, we must remember that not all shareholders have the same level of information that we have here on Tsex and that the market will catch up, it’s only a matter of time.
It is of my opinion that the second half of 2023 we will really shine due to many factors but most notably renesas wold have released market ready applications containing akida IP, and the WANCA’s still won’t believe in Brainchip but that’s ok, we are truly on a once in a lifetime Stock that will not only change our lives as shareholders, but everybody’s life on this planet, as Sean has said on his LinkedIn Brainchip job description-
“BRAINCHIP NEUROMORPHIC AI IS TRULY TRANSFORMATIVE WILL BE THE COMPUTATIONAL FOUNDATION OF SMART SENSING IN ALL EDGE DEVICES. THIS COMPUTING REVOLUTION HAS BEGUN”
Now if we think of who Sean Hehir is and his background and connections, he is a board member still at the Silicon Valley Executive Network, he was Vice President at HP looking after Microsoft, ARM, Intel, AMD, Adobe, Cisco to name a few, and to have a public statement as he has about Brainchip and that it WILL BE the computational foundation of smart sensing in all edge devices, is a man full of confidence, and he has a reputation to uphold, with the connections to Silicon Valley he has, he will either look like the biggest loser with such a statement, or he just knows what he has seen and knows we really are going to be the de facto standard.
As @Violin1 has rightly said regarding the next couple of 4C’s, I caution those who have set their own expectations regarding the 4C and please take into account current macroeconomics, war, budgets!
It’s pretty obvious Brainchip is going to be successful, it’s a matter of when, not if.
Happy new Year to all, have a safe and positive holiday season and shall we all make a toast to Brainchip
Whilst I’m all for a great BRN 2023, the reality is the market doesn’t take kindly to SPACS that show no or little revenue. They are the worst performing stocks in 2022. So we simply cannot be flippant about what BRNs 4Cs show in revenue.
The latest trading day is a good start, but that was largely short cover and the start of another bear flag until proven otherwise..
Good luck to all in 2023
Rise from the ashes
Regular
We need a poo reaction.
Fact Finder
Top 20
Are you saying you can completely discount SynSense as being the provider and if so based on what evidence given the following release:
SynSense and BMW exploring intelligent cockpits
SynSense is integrating its innovative sensors in smart vehicle cockpits for the first time. The neuromorphic chip provider partners with BMW.
My opinion only DYOR
FF
AKIDA BALLISTA
PS: AKIDA is better is not a sufficient answer.
HopalongPetrovski
I'm Spartacus!
Bravo
If ARM was an arm, BRN would be its biceps💪!
Are you saying you can completely discount SynSense as being the provider and if so based on what evidence given the following release:
SynSense and BMW exploring intelligent cockpits
SynSense is integrating its innovative sensors in smart vehicle cockpits for the first time. The neuromorphic chip provider partners with BMW.www.greaterzuricharea.com
My opinion only DYOR
FF
AKIDA BALLISTA
PS: AKIDA is better is not a sufficient answer.
TechGirl
Founding Member
I agree positive information needs to be emphasised. I was actually wondering if things were all good at your end which obviously they are.
So Happy New Year
Regards
FF
AKIDA BALLISTA
Thanks for your concern for my girl Terroni FF, but no need worry, I talk to her everyday, she is all good
We need a poo reaction.
Fact Finder
Top 20
Your use of the term SPACS in connection with Brainchip in 2023 it having listed via a reverse takeover in 2015 to undertake fundamental research into neuromorphic computing underpinned by a world wide patent portfolio protection dating from 2008 seems a very interesting approach.
“special purpose acquisition company (SPAC) is a “blank check” shell corporation designed to take companies public without going through the traditional IPO process.”
It becomes even more interesting when Brainchip officially declared it had moved from research phase to a company commercialising the IP which had arisen from this fundamental research and has in fact accounted for sales of the IP to two major semiconductor world players in Renesas and MegaChips and has product coming to market in 2023 as a result from them as well as from Socionext making your statement regarding income entirely misleading.
I have also read @chapman89 ’s posts and it is also interesting that you have styled his narrative as making light (flippant) of revenue in up coming 4Cs as having done so I obtained the exact opposite view of his intent.
I perhaps should be more charitable but your decision to post charts on this thread and extol opinions based on same when there is a dedicated space for chartists seems to conflate with the above matters and give rise to the need for me to ask what is your intent here?
In the absence of an explanation your failure to address another posters reasonable question regarding what you claimed about your charts showing in 2021 prior to the Mercedes Benz reveal does suggest dishonest manipulation may be your stock in trade.
I do hope this is not the case because you have come to the wrong place.
My opinion only DYOR
FF
AKIDA BALLISTA
Last edited:
Typo? 'SPEC' perhaps?
Fact Finder
Top 20
Not likely when it is a short post, capitals are used and this poster likes to claim extensive market knowledge and experience and well knows the difference between a Spec and a Spac.
My opinion only DYOR
FF
AKIDA BALLISTA
thelittleshort
Top Bloke
Don’t think anyone has gone down this rabbit hole yet? I looked into the DSTG Women in STEM Award - specifically what her paper was about
I couldn’t actually find the paper that the won the award - An energy-efficient AkidaNet for morphologically similar weeds and crops recognition at the Edge' (co-authors Kevin Tsiknos, Kristofor Carlson, Selam Ahder, but another one that lead to the outputs below
My search lead me to the Australian company Photonic Group
Vi Nguyen Thanh Le has journal article cited on their website
ieeexplore.ieee.org
View attachment 25816
The purpose behind the formation of the Photonic Group was to determine if it was possible to create an automated detection system that used light to distinguish one plant from another.
Since that time, the Group has made several key discoveries leading to the lodgement of various patent families in various countries, including Australia, Canada, USA, and Europe.
In 2017, the Group realised that real-time identification using only one discrimination mechanism (spectral reflectance) did not, of it itself, allow for the requisite discrimination in all instances encountered, so a decision was made to identify a suitable complimentary detection technology that could be combined or hybridised with spectral reflectance to generate superior discrimination rates.
Imaging technology was found to be the best complementary technology and the system now developed uses a combination of image data and spectral reflectance data, collected simultaneously, with both data streams being blended and ultimately analysed via the application of artificial intelligence in our proprietary neural net.
Having distinguished one plant from another, the system can then be programmed to make a range of decisions – within an agricultural environment, these decisions are typically Spray Plant A, ignore all other plants, or ignore Plant A, spray everything else, however, once the identification is made, the decisions and actions following from that identification are totally contained with the system programming.
As the US Marines have observed – “If you can see a target, you can hit it, and if you can hit it, you can kill it.”
The recently developed discrimination sensor has been termed the ‘Missing Link in Precision Agriculture’ and as such represents the future of real time weed / crop discrimination.
At its most basic it is a system that provides a farmer with real time discrimination between differing types of vegetation, typically discriminating between crop and weed.
The demand for such a system within the precision agricultural arena has been high, predominantly because of the costly outlay arising from the current practice of blanket spraying of pre and post emergent weeds; a practice that is now universally recognised as being highly inefficient, expensive and hazardous to both human and environmental health.
Another potential usage within agriculture is dealing with those weeds that are starting to show resistance to any herbicide applied in a blanket pre-emergent spray.
Because the herbicide applied using the Group’s technology is used precisely and sparingly, a second spray run can be done in the weeks following a blanket spray, and where viable plants (i.e. those starting to develop resistance to the herbicide used as a blanket spray) are detected, those plants can be re-sprayed using a more expensive, but more effective herbicide, thus eliminating from the farm’s seed bank any weeds developing resistance to the blanket spray herbicide
Furthermore, because the decision to turn on a spray nozzle is made in real-time, the precise location of where that nozzle is activated can be recorded by the technology, leading to the generation of ‘paddock maps’ showing the precise location and numbers of activation in a given area.
Should this information be passed back to a central location, then, at the farm level, analysis of the paddock maps over time, will allow farmers to see the impact their spraying program, identify the direction of and speed of spread of any invading weed, etc.
Analysis of multiple farms in the same region, will inevitably lead in better regional agronomic information regarding the control of weeds across multiple farming properties.
Each detection unit currently contains three lasers projecting light at three discrete and highly optimised wavelengths.
These lasers are sequentially switched on with each pulse of light passing through an optical cavity that generates multiple beams from each laser source. A linear photo detector imager records the intensities of the laser light reflected off any plants within view.
Simultaneously, a camera takes a series of images and both the spectral reflectance data and the image data are combined with an on-board controller circuit then using both data streams, calculates the signature and compares that signature to signatures stored in a database.
Should the signature match the profile of a pre-recorded weed in the database, the system generates a ‘positive strike’ signal that then results in a positive action occurring, such as a spray nozzle being activated and the weed being sprayed, or the position of the weed being logged using a d GPS system.
Each detection unit covers a detection field of 500mm. Multiple detection units are mounted on a vehicle side by side to achieve the desired detection swathe – i.e. 4 units provide 2 metres coverage.
The vehicle is then driven forward at a relatively constant speed such that the units traverse and interrogate the terrain. Sensors detect the reflected laser intensities from the ground and vegetation, whilst images are being generated. The electronic system then processes the recorded data.
Once a target weed is detected a ‘positive strike’ signal is generated to activate a nozzle and spray the weed, or to log its precise position, or both.
If spraying is the selected outcome arising from a positive strike; because the distance between any individual detection unit and its associated spray nozzle is known absolutely and because the speed of the vehicle at that particular instant of time is also known, the system allows for an appropriate delay before activating the spray nozzle so that the spray nozzle is only activated immediately in front of the weed.
These factors then allow the spray nozzle to remain open only whilst it is positioned above the weed and once the detection unit determines that it has transited the weed, it turns off the spray nozzle.
In this way, that which is sprayed out of the spray nozzle is only sprayed immediately before the detected weed, across the detected weed and turned off immediately after the weed ceases to be detected – in this way, herbicide is precisely applied, minimising the volume of herbicide used per square metre and minimising the deleterious effects of excess herbicide coverage on the crop, on the soil and generally on the environment.
Because the herbicide is being applied so precisely, it is possible to envisage the Group’s sensor platform being used in row cropping scenarios, such as cotton, sugar-cane and similar, where the technology actually detects and sprays undesirable plants within the actual row, and not just in the area between rows.
Several technologies are now converging and one possible future within the agricultural sector would be the creation of a multiplicity of small, ground based, autonomous (or semi-autonomous) weeding devices, bearing a considerable likeness to domestic semi-autonomous vacuum cleaners.
These devices would, over time, map a particular area and / or have specified coordinates within which they operate on a 24 / 7 basis. Each device would be equipped with a Photonic Group sensor and would ‘patrol’ a broad-acre paddock or similar, constantly looking for plants that are designated as undesirable at that location (canola plants might not be regarded as undesirable, unless they were found in a field of barley, for example).
Once detected, the device could deploy one of many possible mechanisms to eradicate the undesirable plant that do NOT involve herbicides at all.
For example, the plant could be mechanically removed, the device could be fitted with solar cells and generate boiling water, position itself over the plant and generate a high voltage discharge, generate a flame, etc. All these measures do NOT involve herbicide as a killing mechanism, and all are far more environmentally friendly than continually applying increasingly more expensive herbicides.
As an interim measure to that future, the device could carry one or more herbicides, selecting which one to use based on prior logged activity, and could be programmed to return to a base station as its energy supply, or any of its payload herbicides ran low.
All instances of any intervention could be reported to a central information repository, allowing for real time analysis of activities undertaken to eradicate undesirable plants, in terms of location, frequency, and the like.
This environmentally friendly scenario ultimately rests on the ability of a machine to recognize ‘friend’ or ‘foe’ in real time.
The Photonic Group sensor platform is designed to allow machines to make this decision and once the decision is made, the consequences and down-stream outcomes of that decision can be readily programmed into the system.
I couldn’t actually find the paper that the won the award - An energy-efficient AkidaNet for morphologically similar weeds and crops recognition at the Edge' (co-authors Kevin Tsiknos, Kristofor Carlson, Selam Ahder, but another one that lead to the outputs below
My search lead me to the Australian company Photonic Group
Vi Nguyen Thanh Le has journal article cited on their website
PATENTED TECHNOLOGY TO DISTINGUISH ONE OBJECT FROM ANOTHER.
Our patented technology seeks to mimic the human eye as a mechanism for distinguishing one object from another in real time by using spectral reflectance data (colour) as well as images (shape) as a combined differentiator.AGRICULTURAL SPRAYING – DIFFERENTIATE BETWEEN PLANTS AND WEEDS IN REAL TIME.
Commercially, the Group is currently focused on deploying the technology within the agricultural sector where the accurate real time differentiation of one green plant from another has substantial commercial implications in terms of the reduction in herbicide application following the ability to distinguish one plant as desirable crop and not spray it, and another as an undesirable weed and to spray that plant in isolation.
Detecting weeds from crops under complex field environments based on Faster RCNN
The power of deep learning in object detection has widely been investigated, demonstrating promising results in recent years. In precision agricultural applications, weed detection plays an indispensable part in site-specific weed management. The published resources of crop and weed datasets...
View attachment 25816
WHAT WE DO.
PATENTED TECHNOLOGY TO DISTINGUISH ONE OBJECT FROM ANOTHER.
Our patented technology seeks to mimic the human eye as a mechanism for distinguishing one object from another in real time by using spectral reflectance data (colour) as well as images (shape) as a combined differentiator.AGRICULTURAL SPRAYING – DIFFERENTIATE BETWEEN PLANTS AND WEEDS IN REAL TIME.
Commercially, the Group is currently focused on deploying the technology within the agricultural sector where the accurate real time differentiation of one green plant from another has substantial commercial implications in terms of the reduction in herbicide application following the ability to distinguish one plant as desirable crop and not spray it, and another as an undesirable weed and to spray that plant in isolation.OTHER APPLICATIONS.
Our patent families encompass object differentiation using size, shape and colour, and accordingly we are of the opinion that this technology now truly does mimic the human eye and as such ,the technology has broad application in a multitude of commercial scenarios, some of which are described in accompanying pages. However, we acknowledges that the number of potential applications for this new technology are vast and should anyone believe that our technology has particular application in some specific field or endeavour or would like to explore how our technology could be used or deployed in the future, either in isolation or teamed with some other technology, we would encourage that person to contact is to further discuss and evaluate the concept.WHO WE ARE
The purpose behind the formation of the Photonic Group was to determine if it was possible to create an automated detection system that used light to distinguish one plant from another.
Since that time, the Group has made several key discoveries leading to the lodgement of various patent families in various countries, including Australia, Canada, USA, and Europe.
In 2017, the Group realised that real-time identification using only one discrimination mechanism (spectral reflectance) did not, of it itself, allow for the requisite discrimination in all instances encountered, so a decision was made to identify a suitable complimentary detection technology that could be combined or hybridised with spectral reflectance to generate superior discrimination rates.
Imaging technology was found to be the best complementary technology and the system now developed uses a combination of image data and spectral reflectance data, collected simultaneously, with both data streams being blended and ultimately analysed via the application of artificial intelligence in our proprietary neural net.
Selective spraying using Photonic Group detection
As a result of the work done, the Group has determined that the generation of spectral reflectance data by illuminating a target with a selection of specific laser wavelengths and the collection and analysis of that spectral reflectance data in real time, combined with image data collected at the same time does indeed enable the detection unit to distinguish one plant from another.Having distinguished one plant from another, the system can then be programmed to make a range of decisions – within an agricultural environment, these decisions are typically Spray Plant A, ignore all other plants, or ignore Plant A, spray everything else, however, once the identification is made, the decisions and actions following from that identification are totally contained with the system programming.
As the US Marines have observed – “If you can see a target, you can hit it, and if you can hit it, you can kill it.”
Real time identification & spraying
The initial step is the most difficult – the seeing of the target – what our detection unit does is provide a substitute for the human eye (but is not limited to the human eye limitations in terms of only using the visible light portion of the entire electromagnetic spectrum) to identify a target in real time. Once that identification is made, decisions and actions will follow, subject only to the pre-programmed instructions of the system.A TECHNOLOGICAL BREAKTHROUGH
The recently developed discrimination sensor has been termed the ‘Missing Link in Precision Agriculture’ and as such represents the future of real time weed / crop discrimination.
At its most basic it is a system that provides a farmer with real time discrimination between differing types of vegetation, typically discriminating between crop and weed.
The demand for such a system within the precision agricultural arena has been high, predominantly because of the costly outlay arising from the current practice of blanket spraying of pre and post emergent weeds; a practice that is now universally recognised as being highly inefficient, expensive and hazardous to both human and environmental health.
Another potential usage within agriculture is dealing with those weeds that are starting to show resistance to any herbicide applied in a blanket pre-emergent spray.
Because the herbicide applied using the Group’s technology is used precisely and sparingly, a second spray run can be done in the weeks following a blanket spray, and where viable plants (i.e. those starting to develop resistance to the herbicide used as a blanket spray) are detected, those plants can be re-sprayed using a more expensive, but more effective herbicide, thus eliminating from the farm’s seed bank any weeds developing resistance to the blanket spray herbicide
Furthermore, because the decision to turn on a spray nozzle is made in real-time, the precise location of where that nozzle is activated can be recorded by the technology, leading to the generation of ‘paddock maps’ showing the precise location and numbers of activation in a given area.
Should this information be passed back to a central location, then, at the farm level, analysis of the paddock maps over time, will allow farmers to see the impact their spraying program, identify the direction of and speed of spread of any invading weed, etc.
Analysis of multiple farms in the same region, will inevitably lead in better regional agronomic information regarding the control of weeds across multiple farming properties.
HOW DOES IT WORK? (UNIT LEVEL)
Each detection unit currently contains three lasers projecting light at three discrete and highly optimised wavelengths.
These lasers are sequentially switched on with each pulse of light passing through an optical cavity that generates multiple beams from each laser source. A linear photo detector imager records the intensities of the laser light reflected off any plants within view.
Simultaneously, a camera takes a series of images and both the spectral reflectance data and the image data are combined with an on-board controller circuit then using both data streams, calculates the signature and compares that signature to signatures stored in a database.
Should the signature match the profile of a pre-recorded weed in the database, the system generates a ‘positive strike’ signal that then results in a positive action occurring, such as a spray nozzle being activated and the weed being sprayed, or the position of the weed being logged using a d GPS system.
HOW DOES IT WORK? (SYSTEM LEVEL)
Each detection unit covers a detection field of 500mm. Multiple detection units are mounted on a vehicle side by side to achieve the desired detection swathe – i.e. 4 units provide 2 metres coverage.
The vehicle is then driven forward at a relatively constant speed such that the units traverse and interrogate the terrain. Sensors detect the reflected laser intensities from the ground and vegetation, whilst images are being generated. The electronic system then processes the recorded data.
Once a target weed is detected a ‘positive strike’ signal is generated to activate a nozzle and spray the weed, or to log its precise position, or both.
If spraying is the selected outcome arising from a positive strike; because the distance between any individual detection unit and its associated spray nozzle is known absolutely and because the speed of the vehicle at that particular instant of time is also known, the system allows for an appropriate delay before activating the spray nozzle so that the spray nozzle is only activated immediately in front of the weed.
These factors then allow the spray nozzle to remain open only whilst it is positioned above the weed and once the detection unit determines that it has transited the weed, it turns off the spray nozzle.
In this way, that which is sprayed out of the spray nozzle is only sprayed immediately before the detected weed, across the detected weed and turned off immediately after the weed ceases to be detected – in this way, herbicide is precisely applied, minimising the volume of herbicide used per square metre and minimising the deleterious effects of excess herbicide coverage on the crop, on the soil and generally on the environment.
Because the herbicide is being applied so precisely, it is possible to envisage the Group’s sensor platform being used in row cropping scenarios, such as cotton, sugar-cane and similar, where the technology actually detects and sprays undesirable plants within the actual row, and not just in the area between rows.
FUTURISTIC
Several technologies are now converging and one possible future within the agricultural sector would be the creation of a multiplicity of small, ground based, autonomous (or semi-autonomous) weeding devices, bearing a considerable likeness to domestic semi-autonomous vacuum cleaners.
These devices would, over time, map a particular area and / or have specified coordinates within which they operate on a 24 / 7 basis. Each device would be equipped with a Photonic Group sensor and would ‘patrol’ a broad-acre paddock or similar, constantly looking for plants that are designated as undesirable at that location (canola plants might not be regarded as undesirable, unless they were found in a field of barley, for example).
Once detected, the device could deploy one of many possible mechanisms to eradicate the undesirable plant that do NOT involve herbicides at all.
For example, the plant could be mechanically removed, the device could be fitted with solar cells and generate boiling water, position itself over the plant and generate a high voltage discharge, generate a flame, etc. All these measures do NOT involve herbicide as a killing mechanism, and all are far more environmentally friendly than continually applying increasingly more expensive herbicides.
As an interim measure to that future, the device could carry one or more herbicides, selecting which one to use based on prior logged activity, and could be programmed to return to a base station as its energy supply, or any of its payload herbicides ran low.
All instances of any intervention could be reported to a central information repository, allowing for real time analysis of activities undertaken to eradicate undesirable plants, in terms of location, frequency, and the like.
This environmentally friendly scenario ultimately rests on the ability of a machine to recognize ‘friend’ or ‘foe’ in real time.
The Photonic Group sensor platform is designed to allow machines to make this decision and once the decision is made, the consequences and down-stream outcomes of that decision can be readily programmed into the system.
Attachments
Last edited:
thelittleshort
Top Bloke
Fact Finder
Top 20
Sensational work. Like you I tried to find her paper but gave up. Some very large dots exposed by you which the 1,000 Eyes can now monitor.Don’t think anyone has gone down this rabbit hole yet? I looked into the DSTG Women in STEM Award - specifically what her paper was about
I couldn’t actually find the paper that the won the award - An energy-efficient AkidaNet for morphologically similar weeds and crops recognition at the Edge' (co-authors Kevin Tsiknos, Kristofor Carlson, Selam Ahder, but another one that lead to the outputs below
My search lead me to the Australian company Photonic Group
PATENTED TECHNOLOGY TO DISTINGUISH ONE OBJECT FROM ANOTHER.
Our patented technology seeks to mimic the human eye as a mechanism for distinguishing one object from another in real time by using spectral reflectance data (colour) as well as images (shape) as a combined differentiator.
AGRICULTURAL SPRAYING – DIFFERENTIATE BETWEEN PLANTS AND WEEDS IN REAL TIME.
Commercially, the Group is currently focused on deploying the technology within the agricultural sector where the accurate real time differentiation of one green plant from another has substantial commercial implications in terms of the reduction in herbicide application following the ability to distinguish one plant as desirable crop and not spray it, and another as an undesirable weed and to spray that plant in isolation.
View attachment 25827
View attachment 25826
Detecting weeds from crops under complex field environments based on Faster RCNN
The power of deep learning in object detection has widely been investigated, demonstrating promising results in recent years. In precision agricultural applications, weed detection plays an indispensable part in site-specific weed management. The published resources of crop and weed datasets...
View attachment 25816
View attachment 25829
View attachment 25819
View attachment 25823
View attachment 25821
WHAT WE DO.
PATENTED TECHNOLOGY TO DISTINGUISH ONE OBJECT FROM ANOTHER.
Our patented technology seeks to mimic the human eye as a mechanism for distinguishing one object from another in real time by using spectral reflectance data (colour) as well as images (shape) as a combined differentiator.
AGRICULTURAL SPRAYING – DIFFERENTIATE BETWEEN PLANTS AND WEEDS IN REAL TIME.
Commercially, the Group is currently focused on deploying the technology within the agricultural sector where the accurate real time differentiation of one green plant from another has substantial commercial implications in terms of the reduction in herbicide application following the ability to distinguish one plant as desirable crop and not spray it, and another as an undesirable weed and to spray that plant in isolation.
OTHER APPLICATIONS.
Our patent families encompass object differentiation using size, shape and colour, and accordingly we are of the opinion that this technology now truly does mimic the human eye and as such ,the technology has broad application in a multitude of commercial scenarios, some of which are described in accompanying pages. However, we acknowledges that the number of potential applications for this new technology are vast and should anyone believe that our technology has particular application in some specific field or endeavour or would like to explore how our technology could be used or deployed in the future, either in isolation or teamed with some other technology, we would encourage that person to contact is to further discuss and evaluate the concept.
WHO WE ARE
The purpose behind the formation of the Photonic Group was to determine if it was possible to create an automated detection system that used light to distinguish one plant from another.
Since that time, the Group has made several key discoveries leading to the lodgement of various patent families in various countries, including Australia, Canada, USA, and Europe.
In 2017, the Group realised that real-time identification using only one discrimination mechanism (spectral reflectance) did not, of it itself, allow for the requisite discrimination in all instances encountered, so a decision was made to identify a suitable complimentary detection technology that could be combined or hybridised with spectral reflectance to generate superior discrimination rates.
Imaging technology was found to be the best complementary technology and the system now developed uses a combination of image data and spectral reflectance data, collected simultaneously, with both data streams being blended and ultimately analysed via the application of artificial intelligence in our proprietary neural net.
Selective spraying using Photonic Group detection
As a result of the work done, the Group has determined that the generation of spectral reflectance data by illuminating a target with a selection of specific laser wavelengths and the collection and analysis of that spectral reflectance data in real time, combined with image data collected at the same time does indeed enable the detection unit to distinguish one plant from another.
Having distinguished one plant from another, the system can then be programmed to make a range of decisions – within an agricultural environment, these decisions are typically Spray Plant A, ignore all other plants, or ignore Plant A, spray everything else, however, once the identification is made, the decisions and actions following from that identification are totally contained with the system programming.
As the US Marines have observed – “If you can see a target, you can hit it, and if you can hit it, you can kill it.”
Real time identification & spraying
The initial step is the most difficult – the seeing of the target – what our detection unit does is provide a substitute for the human eye (but is not limited to the human eye limitations in terms of only using the visible light portion of the entire electromagnetic spectrum) to identify a target in real time. Once that identification is made, decisions and actions will follow, subject only to the pre-programmed instructions of the system.
View attachment 25820
A TECHNOLOGICAL BREAKTHROUGH
The recently developed discrimination sensor has been termed the ‘Missing Link in Precision Agriculture’ and as such represents the future of real time weed / crop discrimination.
At its most basic it is a system that provides a farmer with real time discrimination between differing types of vegetation, typically discriminating between crop and weed.
The demand for such a system within the precision agricultural arena has been high, predominantly because of the costly outlay arising from the current practice of blanket spraying of pre and post emergent weeds; a practice that is now universally recognised as being highly inefficient, expensive and hazardous to both human and environmental health.
Another potential usage within agriculture is dealing with those weeds that are starting to show resistance to any herbicide applied in a blanket pre-emergent spray.
Because the herbicide applied using the Group’s technology is used precisely and sparingly, a second spray run can be done in the weeks following a blanket spray, and where viable plants (i.e. those starting to develop resistance to the herbicide used as a blanket spray) are detected, those plants can be re-sprayed using a more expensive, but more effective herbicide, thus eliminating from the farm’s seed bank any weeds developing resistance to the blanket spray herbicide
Furthermore, because the decision to turn on a spray nozzle is made in real-time, the precise location of where that nozzle is activated can be recorded by the technology, leading to the generation of ‘paddock maps’ showing the precise location and numbers of activation in a given area.
Should this information be passed back to a central location, then, at the farm level, analysis of the paddock maps over time, will allow farmers to see the impact their spraying program, identify the direction of and speed of spread of any invading weed, etc.
Analysis of multiple farms in the same region, will inevitably lead in better regional agronomic information regarding the control of weeds across multiple farming properties.
HOW DOES IT WORK? (UNIT LEVEL)
Each detection unit currently contains three lasers projecting light at three discrete and highly optimised wavelengths.
These lasers are sequentially switched on with each pulse of light passing through an optical cavity that generates multiple beams from each laser source. A linear photo detector imager records the intensities of the laser light reflected off any plants within view.
Simultaneously, a camera takes a series of images and both the spectral reflectance data and the image data are combined with an on-board controller circuit then using both data streams, calculates the signature and compares that signature to signatures stored in a database.
Should the signature match the profile of a pre-recorded weed in the database, the system generates a ‘positive strike’ signal that then results in a positive action occurring, such as a spray nozzle being activated and the weed being sprayed, or the position of the weed being logged using a d GPS system.
HOW DOES IT WORK? (SYSTEM LEVEL)
Each detection unit covers a detection field of 500mm. Multiple detection units are mounted on a vehicle side by side to achieve the desired detection swathe – i.e. 4 units provide 2 metres coverage.
The vehicle is then driven forward at a relatively constant speed such that the units traverse and interrogate the terrain. Sensors detect the reflected laser intensities from the ground and vegetation, whilst images are being generated. The electronic system then processes the recorded data.
Once a target weed is detected a ‘positive strike’ signal is generated to activate a nozzle and spray the weed, or to log its precise position, or both.
If spraying is the selected outcome arising from a positive strike; because the distance between any individual detection unit and its associated spray nozzle is known absolutely and because the speed of the vehicle at that particular instant of time is also known, the system allows for an appropriate delay before activating the spray nozzle so that the spray nozzle is only activated immediately in front of the weed.
These factors then allow the spray nozzle to remain open only whilst it is positioned above the weed and once the detection unit determines that it has transited the weed, it turns off the spray nozzle.
In this way, that which is sprayed out of the spray nozzle is only sprayed immediately before the detected weed, across the detected weed and turned off immediately after the weed ceases to be detected – in this way, herbicide is precisely applied, minimising the volume of herbicide used per square metre and minimising the deleterious effects of excess herbicide coverage on the crop, on the soil and generally on the environment.
Because the herbicide is being applied so precisely, it is possible to envisage the Group’s sensor platform being used in row cropping scenarios, such as cotton, sugar-cane and similar, where the technology actually detects and sprays undesirable plants within the actual row, and not just in the area between rows.
FUTURISTIC
Several technologies are now converging and one possible future within the agricultural sector would be the creation of a multiplicity of small, ground based, autonomous (or semi-autonomous) weeding devices, bearing a considerable likeness to domestic semi-autonomous vacuum cleaners.
These devices would, over time, map a particular area and / or have specified coordinates within which they operate on a 24 / 7 basis. Each device would be equipped with a Photonic Group sensor and would ‘patrol’ a broad-acre paddock or similar, constantly looking for plants that are designated as undesirable at that location (canola plants might not be regarded as undesirable, unless they were found in a field of barley, for example).
Once detected, the device could deploy one of many possible mechanisms to eradicate the undesirable plant that do NOT involve herbicides at all.
For example, the plant could be mechanically removed, the device could be fitted with solar cells and generate boiling water, position itself over the plant and generate a high voltage discharge, generate a flame, etc. All these measures do NOT involve herbicide as a killing mechanism, and all are far more environmentally friendly than continually applying increasingly more expensive herbicides.
As an interim measure to that future, the device could carry one or more herbicides, selecting which one to use based on prior logged activity, and could be programmed to return to a base station as its energy supply, or any of its payload herbicides ran low.
All instances of any intervention could be reported to a central information repository, allowing for real time analysis of activities undertaken to eradicate undesirable plants, in terms of location, frequency, and the like.
This environmentally friendly scenario ultimately rests on the ability of a machine to recognize ‘friend’ or ‘foe’ in real time.
The Photonic Group sensor platform is designed to allow machines to make this decision and once the decision is made, the consequences and down-stream outcomes of that decision can be readily programmed into the system.
Very generously shared. Thank you and Happy New Year @thelittleshort
I might yet get my lean mean autonomous weed killing machine. Look out Biddie Bush we’re coming to get you.
https://weeds.dpi.nsw.gov.au/Weeds/Siftonbush - (locally known as Biddie Bush.)
My opinion only DYOR
FF
AKIDA BALLISTA
thelittleshort
Top Bloke
Sensational work. Like you I tried to find her paper but gave up. Some very large dots exposed by you which the 1,000 Eyes can now monitor.
Very generously shared. Thank you and Happy New Year @thelittleshort
I might yet get my lean mean autonomous weed killing machine. Look out Biddie Bush we’re coming to get you.
https://weeds.dpi.nsw.gov.au/Weeds/Siftonbush - (locally known as Biddie Bush.)
My opinion only DYOR
FF
AKIDA BALLISTA
Exciting other applications for the technology here too
OTHER COMMERCIAL APPLICATIONS
Because the Group’s technology is based on the analysis of reflectance spectra (colour) combined with images providing information about shape, several agricultural applications other than the discrimination of weeds from crop, in real time, become apparent.
Precise fertiliser application
Plants, and more specifically plant leaves, frequently develop colour-based characteristics because of nutrient deficiencies in the soil they are growing in. Our technology could be immediately adapted to analyse and identify nutrient deficiencies at a plant by plant level within an agricultural environment with immediate consequences to the application of fertilisers – that is, our technology could be the lead technology that allows for precise fertiliser application tailored to optimise soil in which individual plants are growing.Grain sorting
Spectral reflectance allows for the analysis of parts of the spectrum that are outside the range of light visible to humans. Both water levels and protein levels in cereal crops are important indicators as to the value of a grain crop. Our technology has the ability to identify and measure these variables, so a potential use for the technology would be for a grain-handler to deploy the technology to interrogate and classify varying grain grades based on objective criteria, or at a farmer level, allow for the sorting of grain, based on quality, as it is harvested.Reduce herbicide resistance
Another immediate use for Group’s technology is the spraying of all vegetative matter near railway lines. It is generally accepted that the constant and continued spraying of plants growing with a few metres of a railway lines (for safety reasons because left unchecked the plants can destabilise the ballast material that holds the lines) is a major cause of herbicide resistance in weeds. This spraying is currently undertaken manually, but with our technology it can be automated. Further, when linked to a GPS system, a precise record of plant location and herbicide application can be maintained, such that in a subsequent spray pass, if a plant is detected at an identical location as mapped previously, a different herbicide can be sprayed to the one used previously. In this way, future herbicide resistance can be minimised.The maintenance of open grassland in suburban parks and the elimination of weeds from pavements to airport runways are all potential areas of operation for our technology.
The perfect lawn
Ultimately, our technology could be seen mounted on the front of a suburban lawn mower with a small spray kit mounted on the rear of the mower, such that as the lawn is mown, individual weeds within the overall lawn are detected and killed as the lawn is mown.
HopalongPetrovski
I'm Spartacus!
Similar threads
