Machine Learning and Artificial Intelligence Technologies are the future of farming. It is a time of opportunities with Artificial Intelligence Technology in Agriculture, from soil testing to crop monitoring. There are certain tasks being performed that are so effective that, some of them can even promise to ensure that the overall production increases by 30 to 35%. Artificial Intelligence Technology can be one of the major drivers of the frontier of agriculture in the upcoming time. Artificial Intelligence will have a revolutionary effect on the field of agriculture. Machine Learning is the latest technology at the disposal of farmers as it enables them to minimize losses by providing them with efficient farming avenues using the least number of individuals for high-quality production.
The world population is growing at a very fast rate which contributes to the growth of food demand. AI (Artificial Intelligence), the process by which a computerized robot or computer can continue to do things that intelligent living organisms are normally expected to do. Considering how rapidly technology has advanced, Artificial Intelligence in agriculture revolutionized all the processes. Artificial Intelligence in agriculture not only makes the mechanization of agriculture possible for farmers but is also biased towards precision agriculture in search of high yield and quality production at the cost of fewer inputs. Artificial Intelligence has the potential to help in agricultural practice and eliminate farmers’ problems such as climate uncertainty, weed infestation, and pests that cause waste in yields. This is what makes humans uniquely different from other animals that they have the ability to think, mold, and control things to their advantage. Almost all human paraphernalia ever is an extension of the field of senses. For example, consider a telescope, which is an extension of the eyes. Artificial Intelligence is also an extension of our hand to think and store useful information and data and utilize them to the best possible. Now, computers can do a lot of things, but can computers think? The idea of the thinking machine was born in the 17th century when mechanical calculators were invented.
If they were able to mimic our rational processes, then weren’t they thinking? Since we’ve moved to electronic computers, we’ve been able to simulate neural structure and create more sophisticated algorithms that can beat our best minds in strategy games like chess and create new scientific breakthroughs. But what makes a machine think? What would it be like to be a conscious machine, a computer with an inner life and emotion? Would the machine be human? The term artificial intelligence is not in the minds of modern people synonymous with computers, even though this is a more recent invention than the origin of the term. A very famous pseudonym for an artificial intelligence developer is Alan Turing, who was a British mathematician. One component of the Turing test is the solution to this question. It is the only test of true artificial intelligence that Turing provided, and which actually passes when attempting to simulate a person in a human-computer interaction. Someone sits behind a computer system typing on it and attempting to understand whether the responses that are being returned are from real human individuals or from a computer. If it is one of them are pretending to be a computer and the other not at all, then artificial intelligence has emerged victorious. We don’t know what another human being is thinking.
Why can we even be sure that we find other humans like us, in the way they behave with us and with other humans. But if the machine is simply copying the process, then as soon as we attribute that others have a mind, we will be forced to attribute a mind to the machine as well. ELIZA was the first successful simulation of the Turing test, a computer program by Joseph Weizenbaum in 1965. ELIZA was a highly advanced program that had the ability to offer conversational interaction in the vocabulary of everyday use. It was designed to simulate an interview session between a patient and a psychiatrist, and was so convincing that most individuals engaged in conversation with ELIZA were unaware that it was likely a computer program.
So far, there can be two types of artificial intelligence, namely, strong artificial intelligence and weak artificial intelligence. For this purpose, we need to take into account the experiments and explanations by the American philosopher John Searle, who revived the mind in the discussion of artificial intelligence with his “Chinese room” analogy. In this scenario, Bob, who is familiar with English alone, works in a room filled with books. Sheets of paper containing Chinese script are pushed through a slot in the door, and it is Bob’s responsibility to write down the answers. This he does by detecting written Chinese characters within one of the books. The book then tells him what to do; for example, if it rains, write down this character. Bob reflects on the appropriate characters on the paper and passes it back to the slot. The some Chinese-speaking people who threw out the questions and received the answers are being discussed, but with whom? It is clearly not Bob, who has no Chinese. And it would be absurd to imagine that the room could speak Chinese, because rooms are not clever.
Searle uses this analogy to differentiate between weak AI and strong AI. Strong artificial intelligence captures the nature of our intelligence as we experience it from the inside, that is, having thoughts, feelings, and experiences. Weak artificial intelligence is possessed by a machine that is able to do the things one would do with their own intelligence, such as solving problems, making discoveries, interacting with the environment, and being strategic.
The application of artificial intelligence in agriculture can be revolutionary. Though not much has been explored, the application of A.I. in farming has a lot of potential and is the future of farming. The status of artificial intelligence in India can be seen in two perspectives: one that already exists in operation and the other that can be used for the future. There are many areas where artificial intelligence can be applied, including soil testing IoT development, crop disease detection, crop health monitoring, supply market chain, and many other such operations. Moreover, there are already some models available based on artificial intelligence applied in the agriculture sector, including artificial intelligence sowing applications of Microsoft, E-NAM, etc. Moreover, in this article we discuss these areas in more detail.
The following are some of the active projects in India that have, to great effectiveness, applied a suite of artificial intelligence, IOTs, and other factors towards bringing into existence a productive agricultural environment. There are even those who claim to have experienced an increase of about 30 to 35 percent in total production. These projects function under either name:.
1. Microsoft’s Artificial Intelligence Sowing App
This app uses Microsoft Cortana Intelluge Suite and Power BI, both using A.I-based technology to convert the value of data into an easily actionable format. It can give out weather forecasts and predict the expected upcoming rainfall. According to the local area, it can also envisage the crops that can give better yield this year by analyzing the previous data of the area. The app can also generate automated messages that can be sent directly to the farmers on their registered devices. Talking about the data in June 2016, the performance report of the app showed the success rate with satisfactory results. The test was conducted in Andhra Pradesh in which 175 farmers volunteered. The app sent 10 messages in one sowing season to the farmers about the time and weather forecast in their native language.
2. price forecasting model based on artificial intelligence technologies
Lack of market knowledge can result in huge loss of income for farmers, as the markets are very dynamic in nature it is difficult to get every bit of detail, so to combat this problem in 2017 the tech giant Microsoft and Karnataka Agricultural Price Commission (KAPC) developed a multi-variate commodity price forecasting model using satellite-imaging and other high level coding, now the question arises that how this model predicts the price, so to answer this question Microsoft claims that this model studies the historical sowing data, weather patterns and other relevant data along with GIS and RS data provided by the Metrological Department of India, it creates a report which gives the Karnataka government an idea of setting the minimum support price in the mandis or markets, talking about the results, the 2018 crop season was the first season which this model was used for the first time.
3. Infosys Precision Crop Management Model
Infosys Pvt.ltd is a Bangalore-based Indian multinational information company that has developed a model using IoT (Internet of Things) that can keep a check on the rapidly growing demand for food in the Indian markets. With data based on the changing climate and arable land, the company developed a test bed that will improve crop productivity by studying the data collected from sensors located in commercial crop fields. Although this technology is still new, it has a some lot of potential for developing countries like India, where the agriculture sector contributes 13% of the total GDP. Now, talking about the agriculture sectors, where the future of artificial intelligence technologies seems quite bright. These are some of the most prominent areas where we can see the gargantuan impact of artificial intelligence technologies in the subsequent years.
4. Soil Testing and Monitoring/ Monitoring Soil and Crop Health
Artificial intelligence is an efficient way to identify or monitor potential deficiencies and nutrient deficiencies in the soil. With the help of these technologies, we can get accurate data on soil testing. While remote sensing requires sensors to be built into the aerial system to collect data, it can help determine soil characteristics in a very scrupulous manner.
5. Crop sowing and harvesting
Artificial Intelligence can help in analyzing the sowing and harvesting of crops based on the past data collected by IOTs and other technologies. We can predict the right time for sowing. It can also predict the monsoon and send advice to farmers on when to harvest.
6. Weed and pest control, as well as crop health
Pest management is one of the most common problems faced by farmers across the world. About 75 to 85% of the losses in agricultural practices are caused by lack of weed management and improper pest control. A.I can play a major role in pest control practices, the latest example is The Blue River Technologies (currently under John Deere & Company) and “See and Spray” in Marianna, Arkansas, USA, which claims to have reduced the expenditure on weedicides by almost 90%, reducing the need for weedicides used per acre from 20 to just 2 gallons.
7. Image-based insight generation
Drones are the new innovations that are getting popular very fast. Drones equipped with IOTs can play an indispensable role in the upcoming Indian agriculture. Technologies like thermal scanning can play a very important role in modern day agriculture. Artificial Intelligence based drones can give real-time data to farmers about their crop growth and potential upcoming threats, which result in loss of economic yield.
8. Optimal mixing of agricultural products
For a better and surplus agricultural yield, it is important to know and have the right proportion of chemicals that are to be applied in the field. One of the most common problems faced by farmers is improper proportions of mixing. Artificial Intelligence mixing technologies can help us get the most accurate proportions, which can be effectively used in the fields to get the best possible results.
9. Timely irrigation helps in avoiding over-irrigation and over-irrigation
For a good quality produce as well as better plant growth, timely irrigation is a must. But, at the same time over irrigation should be avoided at any cost. The major problem faced by farmers is when to irrigate and in what quantity. A.I
10. Gramophone (AgStack Technologies)
They use image recognition skills to help farmers get the right information, methods and materials at the right time to harvest crops. Artificial intelligence can also be used to predict pests and diseases, predict food prices and advise farmers on what to produce to maximize productivity. Some of the benefits of Artificial Intelligence and Machine Learning are – 1. A.I accelerates decision making. 2. It reduces costs. 3. It helps in gaining deeper insights. 4. It ensures increased supply chain agility. 5. It improves forecast accuracy.
Although very little explored, the use of artificial intelligence in the agriculture sector seems to have a bright future, and the upcoming results can be a pioneering one. Despite being little explored, AI agriculture has a lot of potential and is the future of agriculture. In terms of employment, AI will help farmers find better jobs and thus, improve their quality of life.
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