Smart farming and artificial intelligence

Agriculture using computer vision

Computer vision is being increasingly utilized in agriculture to automate processes and improve efficiency. By analyzing image data, computer vision enables computers to comprehend images or videos at a high level and trains models to perform tasks such as detection and tracking. Machine vision is particularly important in the implementation of robotics and automation in agriculture, as robots’ cameras capture images that are then fed to the trained model. Based on the model’s output, the robots’ actions can be directed, streamlining agricultural operations and increasing productivity.

Disease detection

AI models can effectively identify diseases from images of plants and apply appropriate treatments, such as the targeted application of pesticides.

Harvesting with computer vision

The utilization of robots and deep neural networks in agriculture is transforming the harvesting process. With the help of cameras and sophisticated algorithms, robots can identify ripe, healthy fruit and direct their arms to harvest the produce while minimizing damage. Even fruit that is unhealthy or diseased can be identified, separated from the healthy crop, and placed in a separate container.

Smart crop monitoring

Smart crop monitoring is a crucial aspect of precision agriculture that involves utilizing both satellite and drone images to assess crop health and development. While satellite images have traditionally been used for crop monitoring, drone photos are increasingly in demand due to their higher quality and greater availability.
After collecting the necessary images, sophisticated algorithms are used to process and analyze the data, extracting relevant information such as crop health, growth patterns, and yield estimates.

Watering plants

Efficient watering of plants is critical in agriculture, and artificial intelligence can play a crucial role in optimizing irrigation practices. By utilizing thermal imaging, farmers can identify which plants require more water, as warmer areas indicate higher levels of moisture stress. This data can be fed into algorithms that utilize machine learning techniques to create a precise watering schedule, minimizing water usage while ensuring that all plants receive the necessary amount of water to thrive. This approach saves water, reduces operational costs, and promotes sustainability in agriculture.

In addition to the above, other uses of computer vision in agriculture include:
● Identification and removal of weeds
● Adjusting the temperature of greenhouses
● Optimizing the lighting conditions for plant growth
● Monitoring the growth rate of crops