MOROS Artificial Intelligence

Leaping into the future with Artificial Intelligence

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MOROS Response uses unmanned vehicles (“UMVs”) for air, land or sea, to monitor, test and control plant and tree diseases and environmental contamination. This also includes diseases that cannabis groweries are encountering. Specifically, Moros combines cantilever technology and artificial intelligence (“Cantilever AI”) to eradicate plant and tree diseases using Rhamnolipid. Cantilever AI is also very effective in analyzing soil and water health and reversing oil and metal environmental contamination.

Many tree and plant components (analytes) are analyzed by data from our cantilevers. AI learning cannot be successful without cantilever measurement in parts per trillion. Immediate feeds to our AI platform produces very specific “learning” data. Each plant and tree virus, disease and fungus can be detected very early on because each species has a particular form and structure and any slight change through exascale computing (billions of data runs) can be detected and learned. Change takes place through temperature variation, organic compound release and water movement rate (among other data) at different stages of the virus, disease and fungus infection. Particular AI “ultra high def” data mining of plant and tree vasculature “Leaf veins” produce fingerprints that are also learned. Data picture mining combined with nanotechnology measurements can predict future events.

How do we Apply our technology?

We combine Artificial Intelligence “learning machines” with aerial drones, land and sea roaming robots that analyze:

  • Data mining - discover patterns of diseases and contamination.
  • Exascale computing – use a billion calculations per second
  • Images – recognize certain characteristics of images and algorithms to analyze past, present and future data.
  • Weighing particles - weigh particles in femtograms.
  • What ratios of mono to di-rhamnolipid must be used in applications.
  • Environmental, disease and pest forecasts using AI algorithms.


AI Technology

Rhamnolipid environmental applications through machine learning

With MOROS AI machine learning, we can create specific algorithms for autonomous environmental, agricultural and cannabis applications. We then apply numerous separate data sets managed by AI to determine what is needed, where is it needed and why it is needed all the while “learning” to carry out specific tasks.

Our applications use 3 separate developed technologies. Cantilever analysis, AI learning and Biosurfactant (patents pending) applications.

Our Rhamnolipid Biosurfactant has the capacity to destabilize the plasma membrane of plant and tree bacterial diseases and prevents its proliferation. One costly and time-consuming challenge is to what degree a plant or tree is sick, how long its has been sick and what disease is affecting the plant or tree.

Our AI agricultural and cannabis applications eliminate time, cost and human error while curing many diseases.


Our AI environmental application can reverse environmental contamination. Rhamnolipid Biosurfactants have the ability to break apart hydrocarbons (petroleum), remove the bond between soil and metals, leaving only minerals after treatment. One costly and time consuming challenge is to what ppms (parts per millions) of oil and diesel are present in the contaminated soil, what other contamination is either growing or subsiding in the soil and what type of metal is present and what is the ppms.

With this technology, we can eliminate human error, cut costs, and learn “on the spot” in real time using short and long-term machine learning” to correct and restore the environment and eradicate agricultural diseases.

Contaminated Soil and Water reversal by Biosurfactants

Biosurfactant soil and water purification is based on removal and separation of high-level contaminants such as petroleum and metals.

Biosurfactants act as an aqueous metal sequestering application to separate Lead, Copper, Caesium, Cadmium, and Lanthanum from soil and water. Biosurfactants when reversing petroleum contamination act as “soap” and breakup the oil into smaller nontoxic components such as natural minerals. Many environmental contamination treatments work very well initially in a controlled testing setting. Numerous publications state the success of these results.

Unfortunately, when the same application makes its way to field testing, results don’t show the same promise as the initial results. We believe we have the solution to this challenge through our Artificial Intelligence platform.

The key to successful environmental applications in the field is to NOT leave any residual material after application that may cause toxicity. The use of too much material or too little material of the application is another challenge. We found that the biggest challenge to environmental contaminated water or soil applications are the “unforeseen” variations and type of contaminants. One end of the site may have low-level contamination whereas high-level contamination, including other inorganic material may be at the other end of the site.

Constant evaluation by use of Exascale computing (billions of runs) and Artificial Intelligence managed by drones and robots will eliminate these mistakes. The “Learning Machines”:

  • Determine the type of contaminant
  • The saturation of the contaminant
  • The exact area of the contaminant and;
  • What ratio of application is needed?
  • What dilution is required?
  • What is the specific area that needs the specific application?

After “learning” what worked by rate of time, rate of cost and rate of error, “Data Mining” AI will commence more efficient applications.