Artificial Intelligence in Agriculture
A multitude of technological developments has had an impact
on the agriculture industry over the years. Agricultural production is the
primary source of income in many countries around the world, and as the world's
population continues to grow, according to UN estimates, it will rise from 7.5
billion in 2015 to 9.7 billion in 2050, there will be increased pressure on
land, as only an additional 4 percent of the world's land will be under
cultivation by that time. As a result, farmers will have to do more with less. According
to the same study, food production will need to expand by 60% to feed an
additional two billion people. Traditional methods, however, are insufficient
to meet this enormous need. This is causing farmers and agribusinesses to look
for new ways to enhance output while reducing waste. Artificial Intelligence
(AI) is gradually becoming a component of the agricultural industry's technical
progress as a result of this development. The goal is to boost global food
production by 50% by 20502 to feed an additional two billion people. In
addition to improving efficiency, AI-powered solutions will assist farmers to enhance
crop quantity and quality, as well as speed up the time to market.
Agriculture and Artificial Intelligence
Using AI for intelligent chemical spraying - Saves money.
Each day, farms create hundreds of data points on temperature, soil composition, water use, and meteorological conditions. This data is used in real-time by artificial intelligence and machine learning algorithms to get critical insights such as identifying the optimal time to sow seeds, selecting crop selections, hybrid seed selection for higher yields, and so on.
AI technology is assisting in the overall quality and
accuracy of harvests – a practice dubbed precision agriculture. AI technology
aids in the detection of plant disease, pests, and agricultural nutrition
deficiencies. AI sensors can detect and target weeds before determining which
herbicide to use in the area. This results in less herbicide use and cost
savings. Many technology companies have developed weed-monitoring and
weed-spraying robots that use computer vision and artificial intelligence to
monitor and precisely spray weeds. These robots are capable of eliminating 80
percent of the volume of pesticides routinely sprayed on crops and reducing
herbicide expenditure by 90 percent. These sophisticated AI sprayers can
substantially reduce the number of pesticides needed in the fields, improving
the quality of agricultural output while also reducing costs.
Using AI-based farm harvesting robots – Overcoming the Labor Challenge
Have you ever wondered who harvests the crops from the agricultural land? In most situations, it is not the typical farm laborer who is responsible for delivering the food to your kitchen table, but robotic robots capable of undertaking mass harvesting with more accuracy and speed. These machines aid in increasing production size and reducing wastage from crops left in the field.
Many businesses are attempting to improve agricultural efficiency. There are products such as a self-picking strawberry machine1 and vacuum equipment that can harvest mature apples from trees. These gadgets employ sensor fusion, machine vision, and artificial intelligence models to determine the location of harvestable food and assist in picking the appropriate fruits.
Agriculture is the second largest industry after defense in
terms of the deployment of service robots for professional use. According to the
International Federation of Robotics, up to 25,000 agricultural robots have
been sold, equal to the amount employed for military purposes.
Using AI for predictive analytics – Allows for better
decision-making
determining the optimal time to sow
The difference between a good year and a failed harvest is
as simple as having timely knowledge of the seed sowing date. To address this,
scientists at ICRISAT used a predictive analytics technique to estimate the
perfect time to sow seeds for maximum yield. It also provides information on
soil health and fertilizer recommendations, as well as a 7-day weather
forecast.
Crop yield forecasts and price projections
The price fluctuation of the crop is the major source of concern for many farmers. Farmers are unable to anticipate a consistent output pattern due to volatile prices. This issue is especially widespread in crops with short shelf lives, such as tomatoes. Through the use of satellite imagery and weather data, businesses are surveying land and monitoring crop health in real-time. Big data, artificial intelligence, and machine learning may be used by businesses to detect pest and disease infestations, estimate tomato output and yield, and forecast prices. They can advise farmers and governments on future price patterns, demand levels, crop types to sow for best benefit, pesticide use, and so on.
AI is being used by innovative startups in agriculture. A Berlin-based agricultural technology startup3 developed a multi-lingual plant disease and pest diagnostic app that uses various images of the plant to detect diseases; a smartphone collects the image, which is matched with a server image, and then a diagnosis of that specific disease is provided and applied to the crop using an intelligent spraying technique; the app is available in multiple languages. In this way, the program employs AI and machine learning to tackle plant illnesses. This app has been downloaded by over seven million farmers and has assisted in the identification of over 385 agricultural diseases in field crops, fruits, and vegetables.
In conclusion, AI alleviates the shortage of resources and labor to a considerable extent, and it will be a powerful instrument that may assist organizations in dealing with the increasing complexity of modern agriculture. It is past time for large corporations to invest in this area.
Can artificial intelligence (AI) replace the expertise that farmers have traditionally had? For the time being, the answer is probably no, although AI will complement and challenge how decisions are made, as well as improve farming techniques, in the near future. Improved agricultural practices and yields, as well as a qualitative change in farmers' lives, are expected as a result of such technological interventions.