Contact us anytime to know more — Kuldeep K., Founder & CEO CISIN
The true power of AI and machine learning is how it can democratize experience, reducing the barriers to entry for tasks that once can only be performed with a small group of specialists.
The result, one day, will probably be that your self-driving automobile drops you off at the grocery store, where you will see higher-quality foods readily available at costs lower than they have ever been.
It will occur through the use of machine learning algorithms that consume a large volume of information, identify patterns and apply statistical probabilities to select the course of action most likely to result in a successful result.
By way of example, Google's famous self-driving car used machine learning how to catalog a number of fascinating behaviors on the street.
Whenever the automobile detectors recognized a garbage truck vehicles following behind faking to pull abruptly into the next lane to go around it -- usually without signaling. Hence the Google automobile stored this pattern of behavior and accommodated its position and rate to decrease the possibility that these "sudden" lane changes could cause a collision.
For humans, this is a common defensive driving ability, but repeating this level of consciousness in a machine would have been unthinkable only a couple of decades back.
Now, strong algorithms may conquer the turmoil of streets full of drivers of all ability levels, such as those paying additional attention to their phones than the road ahead.
Artificial Intelligence and agriculture
As amazing as this may be, the use of machine learning to the living fields of agriculture is an order of magnitude more complicated.
A street network is fixed, with a map that seldom changes and gives a solid base for the algorithm to create its conclusions.
However calm and calm a wind-swept area of wheat may seem to the casual observer, agricultural fields are genuinely chaotic places.
There's irregular weather that changes in land quality as well as the ever-present probability that disease and pests may cover a visit. Requirements in one portion of a field may be totally different from another part. Consequently, growers never truly know until the previous day if they will have a successful harvest or not.
The potential for expansion in agricultural AI systems is important.
Have a seed and plant it into a field in Iowa. Then take the exact same seed and plant it into Brazil. The outcomes will probably not be the same, or, if they're, repeat the experiment again and the odds are that the return of each will differ.
That is because tens of thousands of interrelated variables are at play, in the number of nutrients in the land, to whether it's sunny or cloudy, to rain levels, fever, the presence of pests and so on.
That's where machine learning may reap clarity from the madness. Remote detectors placed in fields perceive the environment as statistical data.
Algorithms process this data, adapting and learning to predict a range of outcomes.
Farmers may use these AI calculations to make far better field decisions that increase the chances of a successful crop.
Breeders also can use AI calculations to make the crops themselves better. The combo of these uses will ultimately drive lower prices in the supermarket.
The democratization of farming experience
That is a gigantic a change in the way things have been done in agriculture. Farmers have a proud tradition going back generations of relying on intuition growing crops.
They have got an instinct of what is best based on long-term experience. It is not that farmers did not wish to use computers, it is they have not been especially effective. Early machines, using their binary logic, were not well-suited to highly intricate and variable field surroundings.
Therefore a farm's productivity frequently depended on having the most experienced growers on hand. But what if we could change that, and make the best decisions and growing techniques available to novice farmers? This is very vital for developing nations that might not have access to highly skilled growers.
The rise of precision agriculture has opened the prospect of spreading the benefit of machine experience far and wide.
Remote sensors, satellites, and UAVs can gather information 24 hours each day within a whole field. These can monitor plant health, soil condition, humidity, temperature and so forth. The amount of information these sensors can create is overwhelming, but the algorithms of precision agriculture can process and interpret the information in a helpful way.
The next huge leap will come from deploying true artificial intelligence algorithms which learn in the information and translate never-before-seen scenarios, enabling each harvest to become increasingly certain.
This will reduce wasted effort and lower the cost of growing, with much of the savings passed on to customers.
AI builds better plants
Machine learning algorithms are also placed on the centuries-old procedure of breeding plant types better able to resist drought or pest infestation.
Breeders have long used conventional ways of picking the "best" parent crops to make varieties with a more pleasing look, longer shelf life, and a superior taste. Because of AI's program inbreeding, stronger plants are more inclined to create their approach to harvest, and yields will continue to rise.
As with farming methods, machine learning helps with all facets of the decision-making procedure of picking plants and testing new varieties.
Algorithms speed the process to ensure improvements in plant varieties create their way to the fields and the supermarkets quicker than ever before. This, again, helps lower costs while improving quality.
The possibility of growth in agricultural AI methods is significant, as well as the calculations grow smarter, the rewards will continue to be seen every single time you check out at the grocery store.
