*
*
Computer vision can also count fruits and vegetables in the field, estimate yields, and identify diseases. This saves time and labor, gets crops to market faster, and lets farmers spend more time on the business side of their operation. Computer vision-enabled machines can also sort and grade a harvest, such as potatoes or apples, much more quickly than humans with the ability to sort produce by size, weight, color, and ripeness — and even determine which foods should be shipped from the farm first based on perishability. Some livestock farmers already use computer vision to automatically monitor animals in real time as well. The technology can identify animals, track their locations, ensure they have access to food and water, and watch for injury, disease, or abnormal behavior. Cameras with computer vision are also used for remote, real-time security monitoring and surveillance. For example, they can detect foxes breaking into a chicken coop or livestock damaging equipment or crops and use deep neural networks to perform facial recognition on human intruders.
3
HOW THE FUTURE OF AI IN AGRICULTURE COULD LOOK
AI & AGRICULTURE’S MILESTONES
MORE ROBOTS ARE DOING OUR FARM WORK
While we may picture farmers driving tractors across fields for hours in the hot sun, that ’ s not always the case.
Autonomous tractors — many of which also use robot technology and precision positioning systems — can drive on farms without a human operator around the clock and in many weather conditions, boosting productivity and profits. On their own, they can mow, plow, plant crops, and spray for weeds and pests. The precise application of fertilizer, pesticides, and water could mean better yield, less need for manual labor, and less waste or negative environmental impact. A number of companies have developed robots that can do farm work autonomously. One such company is Earthsense, which produces beagle-sized robots capable of pulling weeds, diagnosing crop infections, and gathering other data that could help farmers be far more efficient in caring for crops with fewer laborers. While these types of systems aren ’ t yet in widespread use, Earthsense ’ s cofounder Girish Chowdhary and others hope this sort of precision agriculture leads to a different kind of sustainable farming that doesn ’ t just focus on growing more crops on the same amount of land, but instead prioritizes small farms growing a more diverse collection of high-value crops using fewer chemicals. The market-research consultancy Grand View Research projects that the global agricultural robots market — which it valued at $ 12.94 billion in 2023 — will reach $ 48 billion by 2030. Many farmers already rely on agricultural robots. For example, in 2022, milking made up 30 % of the global agricultural robots market with dairy farmers opting to increase milk production by using the technology to feed their cows night and day. There are also robots that can pollinate flowers, harvest lettuce, and pick delicate strawberries. And there are even “ robot swarms, ” or coordinated groups of autonomous robots that can collaborate on agricultural tasks on the ground or in the air like drones.
1
PRECISION AGRICULTURE AI technologies like Headwall’s Hyperspectral Imaging Systems have enabled the widespread adoption of precision agriculture techniques. This involves using sensors, drones, and AI algorithms to collect and analyze data on crop health, soil conditions, and weather patterns. Farmers can use this information to optimize water usage, reduce pesticide application, and increase overall crop yields.
DATA-DRIVEN
FARMING
AND
COMPUTER
VISION
MIGHT
REVOLUTIONIZE
AGRICULTURE
Data-driven farming involves using data to improve agricultural decision-making and crop-yield outcomes. Experts believe it ’ s one way for farmers to achieve more sustainable farming practices and better food safety. Computer vision, a branch of AI that processes an image or video from a camera, is one component of data-driven farming that could be particularly helpful in improving yields and sustainability. It uses machine learning and neural networks to see — and interpret — the world the way a person does. The data gleaned from computer vision may come from satellite images, drones, or stationary cameras, and the technology uses deep learning algorithms to analyze and understand the footage. An example is Penn State ’ s Huck Institutes of Life Sciences app PlantVillage, which uses computer vision to diagnose
2
INTERNET
OF
THINGS
SENSORS
GATHER FARMING
INFORMATION
FOR
DATA-DRIVEN
Internet of Things sensors are an integral part of data-driven farming. For instance, farmers can use IoT sensors to collect real-time data that computer vision algorithms then interpret to identify types of weeds, insects, and diseases ; analyze soil moisture and nutrients ; determine root health conditions ; monitor soil erosion ; conduct pH analyses ; and make decisions regarding weather conditions. Sensors can also optimize irrigation systems, analyze patterns to detect leaks and other damage, and notify farmers if there ’ s a problem.
AUTONOMOUS FARMING EQUIPMENT Advances in AI have led to the development of autonomous farming equipment. This includes self-driving tractors like John Deere ’ s 8R and other robotic systems capable of planting, irrigating, and harvesting crops. These technologies can operate with high precision, reducing labor costs and increasing efficiency on the farm.
crop diseases from photos uploaded by cell phone. After a farmer uploads a photo and enters the crop type, location, and date, PlantVillage sends advice via smartphone,
p l a n t i n g
SMS, or
s o c i a l
COMPUTER VISION CAN ALSO COUNT FRUITS AND VEGETABLES IN THE FIELD, ESTIMATE YIELDS, AND IDENTIFY DISEASES. THIS SAVES TIME AND LABOR, GETS CROPS TO MARKET FASTER, AND LETS FARMERS SPEND MORE TIME ON THE BUSINESS SIDE OF THEIR OPERATION.
CROP DISEASE DETECTION AI-powered systems like the ones developed by Fermata are increasingly being used to detect and diagnose crop diseases. By analyzing images of leaves or plants, AI algorithms can identify signs of disease or nutrient deficiencies early on, allowing farmers to take proactive measures to prevent crop loss and optimize plant health.
network.
The
project provide smallholder farmers worldwide with technology that offers them the knowledge to grow more food. aims to
82
83
ISSUE 01
BLADE RUNNERS
Made with FlippingBook - professional solution for displaying marketing and sales documents online