Digital Farming' Future with AI and Computer Vision opportunities
The robotics industry is projected to reach a whopping USD 74 billion by 2026, with a significant share of this expansion devoted to agricultural robots.
Agricultural robots may be utilized for many functions to reduce farmers' workloads. Their major responsibility is to do physically difficult, labor-intensive, and repetitive jobs. In recent years, however, robots have been utilized for various specialized tasks formerly performed by seasoned farmers.
Forecasting the weather, nursery planting, crop yields, crop monitoring and analysis, picking and harvesting, weeding and spraying. Increasingly, farmers entrust these responsibilities to artificial intelligence and robotics.
By 2026, the increase in the usage of artificial intelligence in the agriculture industry is projected to reach USD 4 billion, up from USD 1 billion in 2020. The market expansion is fueled by the increasing deployment of data generation via sensors and aerial photographs for crops, the rising crop productivity enabled by deep-learning technology, and government backing for the use of contemporary agricultural practices.
Robots for Weed Control
With #ComputerVision, robots detect weeds in fields and eliminate them using lasers. By employing robots, fields are no longer necessary to be sprayed with pesticides or manually weeded.
There is enormous potential for two million farms in the United States.
Drones may offer producers a bird's-eye perspective of fields and crops and valuable insights. Combining drone video with computer vision-based AI algorithms can give farmers even more information.
New computer vision models and robotic systems are being developed to automate harvesting. Even as the labor market evolves, harvesting robots can guarantee the continued availability of food.
The future of digital farming
Computer vision models may be trained to recognize items, such as fruit, and differentiate them from other objects, such as leaves and branches. Moreover, AI systems may be taught to recognize the ripeness of fruit. Images with annotations depict the various colors of fruit at various stages of maturity. This data teaches artificial intelligence algorithms to identify perfectly ripe fruit in the real world.
Models of computer vision can be combined with robotics. The AI instructs the robot to choose delicate fruits, and artificial arms can now hold them gently. Once this technology is developed and made more affordable, it may revolutionize agriculture.
Cambridge University is one of the most prominent institutions active in this field of agricultural robots. They have constructed a unique robot called "Vegebot," a prototype that harvests crops with astounding precision using computer vision.
Cambridge University built this robot to be able to harvest lettuce, a task that was formerly thought to be nearly impossible for machines.
The "Vegebot's" camera allows it to scan the lettuce and determine whether or not a specific plant is ready to be harvested. Once it determines that the plant is ready to be harvested, a second camera positioned near the robot's blade is used to precisely guide its activities.
Additionally, a machine-learning algorithm has been included in the software of "Vegebot" to recognize ripe and ready-to-harvest lettuce.
Although "Vegebot" cannot yet match the pace of human hands, the fact that this technology has advanced so quickly bodes well for the agricultural sector. If the robot can assist in harvesting a fragile crop such as lettuce, it would be doubly valuable for hardier food that is less susceptible to bruising or tearing.
Another robot, Berry 5, is composed of many components, unlike the single arms often found on agricultural robots. These components can assist Berry 5 in complicated activities like grasping the leaf of the strawberry shrub, carefully harvesting the fruit, and packaging it securely.
Like the robot created at Cambridge University, Berry 5 uses computer vision to distinguish between ripe and unripe fruit. Unlike the "Vegebot," however, Berry 5 executes exceedingly quickly. It can harvest a strawberry plant in eight seconds and move on to the next plant in 1.5 seconds.
This computer vision technology still faces obstacles, though. Annotating data precisely can assist AI firms in realizing the promise of harvesting robots. Innovators may benefit from data annotation services by receiving high-quality datasets that can enhance the performance of their models.
Data annotation perception is vital
To enable autonomous harvesting, computer vision models must learn from the annotated picture and video data. Essential to the creation of datasets for this technology are the following annotation methods:
Segmentation allows for the classification of images. Segmentation classifies every pixel in a digital picture. This additional contextual information enables harvesting AIs to more readily identify ripe fruit.
Variable data: harvests occur on every continent. This necessitates that training data reflect the diverse situations in which harvesting robots may work.
This program automatically produces outlines for forms in digital photos. As a result, annotations can categorize fruit much faster for AI training data collection.
Robots and artificial intelligence are a potent mix that might improve agriculture. Keymakr assists developers in this industry by providing them with access to unique annotation technologies and a staff of annotation experts.