The enhancement of automatic irrigation systems :
Crop management is autonomous thanks to AI algorithms. Algorithms can decide in real-time how
much water to deliver to crops when linked with IoT (Internet of Things) sensors that track soil
moisture levels and weather conditions. An autonomous agricultural irrigation system is made to
promote sustainable farming methods while preserving water.
Detecting disease and pests :
Computer vision can identify pests and diseases in addition to soil quality and crop growth. In order
to discover insects, rot, mold, and other dangers to crop health, AI is used to scan photos. Together
with alert systems, this enables farmers to take swift action to eradicate pests or quarantine crops to
stop the spread of disease.
Apple black rot has been successfully detected by AI with a 90% accuracy rate. It is equally accurate
in identifying other insects, such as flies, bees, moths, etc.
Monitoring of crops and soil :
The health and growth of crops can be significantly impacted by the incorrect balance of nutrients in the soil. Farmers may quickly make the necessary modifications by identifying these nutrients and evaluating how they affect crop productivity using artificial intelligence.
While the accuracy of human observation is constrained, computer vision models can monitor soil conditions to collect precise data. The health of the crop is then assessed using data from the study
of plants, and yields are predicted while specific problems are noted.
Application of pesticides using intelligence :
Farmers are already aware that there is room for improvement in pesticide use. Unfortunately, there are significant drawbacks to both human and automated application processes. While manually applying pesticides might be slow and laborious, it allows greater precision in aiming at specific locations. Although automated pesticide spraying is faster and less labor-intensive, it frequently lacks accuracy, contaminating the environment.
Drones driven by AI offer the best benefits of each strategy while avoiding their disadvantages.
Computer vision is used by drones to calculate how much insecticide should be applied to each
region. This technology is still in its infancy, but it is developing quickly.
Keeping track of livestock health :
Although it may appear simpler to identify health issues in cattle than in crops, in reality, it can be
very difficult. Thank goodness, AI can assist with this. Drones, cameras, and computer vision are all
used in a method developed by AI applications to remotely check the health of livestock. It
recognizes actions like giving birth and recognizes unusual behavior in cattle.
In order to assess the effects of nutrition and environmental factors on livestock and to offer useful
insights, AI and ML solutions are used. Farmers can use this information to better the health of their
cattle in order to increase milk production.
Automatic harvesting and weeding :
Computer vision may be used to identify weeds and invasive plant species, much like it can identify pests and illnesses. Computer vision examines the size, shape, and color of leaves in conjunction with machine learning to discriminate between weeds and crops. Robots that perform robotic process automation (RPA) activities, like autonomous weeding, can be programmed using such systems. In reality, a robot of this kind has already been employed successfully. Smart bots may eventually be able to completely weed and harvest crops as these technologies become more widely available.
While AI in agriculture has many advantages, it is unable to work without other digital technologies
like big data, sensors, and software that are already in use. Similar to how AI is required for other
technologies to function successfully. The data itself in the case of large data isn’t all that valuable.
What matters is how it is handled and put into practice.
admin
October 17, 2023
