A new generation of farming is helping shape the crops of tomorrow. It uses technologically advanced equipment, hybrid seeds of a variety of a plant, and lots of energy grants in the form of fertilizers, pesticides, and irrigation water to improve the production practices employed by agriculturalists.
According to a recent announcement by BIS Research, the global modern farming market is likely to reach $23.14 billion by 2022, with an annual growth rate of 19.3% between 2017 and 2022. The growing demand for sophisticated crop yield, the rising integration of information & communication technology (ICT) into farming, and the need for climate-smart agriculture has continuously attributed to the increment in these numbers.
Today, governments and agricultural bodies have decided to invest heavily in the latest infrastructure to strengthen farming with technology. They are planning to conduct curriculums and researches on technologies like artificial intelligence, the Internet of Things (IoT), and machine learning to escalate the Agri Economy of the country and promote sustainable farming.
Some of those modern technologies that are known to change the way of farming Includes:
Also known as satellite farming or site-specific crop management utilizes information technology to ensure the good health and productivity of crops. The precision agriculture companies primarily depend on dedicated equipment such as antennas, access points, sensing devices, automation, and control system. Additionally, it includes a broad range of technologies such as robotics, bio-engineering, big data, and more.
Drones make precision farming more effective by observing the crops, their growth, texture, condition of the pests, etc. This observed data is processed using programs built around precision agriculture to optimize crop growth. In countries like America, drones have become the farming equipment. They use these types of machinery to save water, fertilizer and conserve resources.
Precision farming techniques have found a vast scope to grow. As per Grand View Research, this market is predicted to reach $43.4 billion by 2025.
The data collection and analysis tools can be applied to agriculture to reap the excellent yield. The robot, by name TerraSentia, with a high-resolution camera on each side and all-terrain wheels, generates the most detailed portrait possible of a field, so that agronomists can breed better crops in the future. The details observed by this machine include the size and the health of the plants, and also the number and quality of ears each corn plant is likely to produce in the given season.
Lately, the French company Naïo launched ten models of a robot named Oz to collect phenotypes of vegetable crops and gobble up weeds on the go. Similarly, EcoRobotix, based in Switzerland, developed a solar-powered robot to differentiate the crops and weeds.
Machine Learning-Based Crop-Spraying Equipment
One good example for a company producing machine learning-enabled farming equipment includes Tractor giant John Deere who is bringing in more robots to the field. The company has procured robotics start-up firm Blue River Technology for $305 million that makes “see-and-spray” robots affixed to tractors. This machine-learning assisted robot uses computer vision to ascertain plants in the field that need fertilizer, pesticides, and other inputs to manage crops. These robots are typically used on cotton, lettuce, and other vegetables.
Deere helps farmers to be more productive in fertilizing and also frees up money for investing in tractors.
Tech-Enhanced Livestock Farming
Modern farming technology also includes the livestock industry, which is widely overlooked by people. Today, livestock technology can enhance the productivity and management of animals and livestock. Computers attached to milking machines, for example, can monitor the quantity and quality of milk each animal generates, and notify the farmer if anything is unusual.
Similarly, placing intelligent sensors on cattle can help you track their everyday actions and health-related concerns while delivering data-driven insights for the herd. This data can then be used to make quick management decisions, such as breeding assessment and animal selection.
The farmers, meanwhile, can utilize one or more of these tools and techniques to achieve their harvesting goal by making better decisions in the field.