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Smart farming and artificial intelligence

Smart farming and artificial intelligence

Smart farming and artificial intelligence

Agriculture is one of the largest industries globally, and its modernization has been crucial to prevent famine as the population has grown and the demand for food has increased.

The use of herbicides, fertilizers, and genetically modified seeds has significantly transformed agriculture, and technology-assisted irrigation and spraying techniques have improved agricultural productivity. However, in recent years, artificial intelligence has garnered significant attention in the industry.

The goal of incorporating artificial intelligence in the agricultural sector is to optimize resource allocation, enhance product quality and safety, and minimize damage.
In the following section, we will discuss some computer vision applications that are being utilized in agriculture.

What is Precision Agriculture?

Precision Agriculture is a farming management approach that involves monitoring, measuring, and adjusting to agricultural variability within and between fields. Precision farming aims to increase productivity and efficiency, reduce costs, and promote environmental sustainability.

Specialized tools and software such as drones, sensors, GPS, satellite photos, deep learning technologies, and computer vision are employed in precision farming to collect and analyze data.

Using the information gathered, farmers can determine the optimal time to fertilize, irrigate, or apply pesticides to their crops, as well as when to plant and harvest them.

The implementation of precision farming techniques offers numerous benefits, including:

  • reducing costs and the use of resources such as water, seeds, fertilizers, and pesticides
  • Promoting soil health by reducing the reliance on pesticides
  • Enhancing agriculture’s resilience to weather conditions
  • Increasing production

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.

Identifying ill plants
Figure1. Identifying ill plants and spraying them. Source

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.

Fruit-picking robots
Figure2. Fruit-picking robots. Source

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.

drones monitoring
Figure3. Using drones to monitor crops. Source

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.

thermal cameras
Figure4. Utilizing thermal cameras and irrigation automation to find low water regions. Source

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


In light of the limited availability of resources like water, fertile soil, and labor, it is crucial to reduce resource waste and product spoilage. Fortunately, the usage of artificial intelligence provides one of the greatest solutions available today.

By employing computer vision techniques and readily available hardware, we can quickly and continuously assess products and farms, with technology even surpassing human ability in identifying abnormalities.

At AIEX, we’ve developed an artificial intelligence platform that incorporates numerous segmentation, object detection, and calcification methods. These algorithms allow you to customize your own personal models and apply them to any IOT devices or hardware of your choice.

Get in touch with us for further details.

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