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Practical Implications of How Computer Vision is Transforming Agriculture

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A significant part of the world economy relies on agriculture. One of the major issues farmers and governments face is their inability to monitor vast tracts of land. It’s essential to regularly study forests and farmland to check for diseases, water supply issues, pest infestations, and weather-related problems.  

The problem of getting visual information on crops and fields spread out on large areas can be solved with the help of a combination of drone technology and computer vision.

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Applying AI to agriculture makes it possible to address major world issues like health, food supply, poverty, and malnutrition.

Building an AI-powered computer vision to support farming and agriculture is one of the most important areas where AI can be applied today. Already, businesses in different industries use computer vision to sort and find images in blogs

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Sites like Amazon and Alibaba host millions of images. And it becomes important to automatically sort and tag inappropriate, harmful, or irrelevant images. 

Computer vision technology can make sure that the millions of images processed on a website are suitable for use. 

This post will look at the practical implications of computer vision in agriculture. 

Computer vision technology and how it can be used in agriculture

Drones can take pictures of land and crops from different angles. And once the image is processed, computer vision technology can be trained to identify things like pests, weeds, and also assess the land quality. 

computer vision in agriculture
Computer vision tech in agriculture | Image credit: coffeekai/Freepik

In this way, it’s possible to monitor remote areas and save money and time. Computer vision technology is useful to assist agriculture by performing the following image recognition tasks:

Object detection

Here, the algorithm detects objects in images and draws bounding boxes around them. Object detection locates different objects and can be used to extract information from drone images. Detecting rivers, forests, crops, houses, and other objects will help in mapping and planning for future crops. 

Image tagging

Machine learning builds on a training data set and learns to assign tags to objects detected in an image. This makes it possible to find geographical objects like rocks or wildlife in seconds or minutes. Image tagging is already applied to stock photography membership sites, retail businesses, and other industries that rely on imagery. For agriculture, image tagging can be used to tag different types of crops, weeds, soil conditions, and much more. 

Image segmentation

In this technology, objects in an image are distinguished by their shape. The image processing tool creates a mask that covers the shape of an object in relation to its surroundings. Image segmentation has powerful applications in agriculture where soil, crop quality, and other factors need to be differentiated. We’ll look at use cases in the next section to look at exactly how impactful image segmentation can be in managing natural resources.

Use cases of computer vision in agriculture

With the help of computer vision technologies, image tagging, segmentation, and object detection, it becomes possible to get data sent to governmental bodies, educational institutes, and other stakeholders rapidly. This enables quick decision-making and can save lives in the long run as food crop health and other issues are managed effectively. 

Let’s explore some of the use cases of computer vision when it’s applied to agriculture. 

  • Image segmentation and, in particular, pixel clustering can be used to detect foraging areas for bees. This is a critical application that impacts global food conditions as the protection of bees plays a major role in the long-term food supply
  • Solar-powered drones can cover hundreds of acres in a single flight. They can give farmers a view of their farmlands, use multispectral cameras to get data about soil and crop conditions, and monitor crop diseases.
  • Computer vision-based image processing tools make it possible to detect and classify chemical compositions of soil
  • It enables phenotyping to identify the best crop breeds.
  • We can expect to see the development of farming robots, autonomous tractors, and other AI-enabled farming devices. It will become possible to manage crops and carry out actions like sowing, clearing, weeding, and more
  • Clearing rocks from the soil is a time-consuming process and getting a bird’s eye view of the land ensures that farmers are not wasting time on poor quality soil
  • Computer vision algorithms can be trained to grade and sort fruits and vegetables automatically. This shortens the time to transport food and determine whether produce can survive long journeys through ships.  

These are just a few of the applications of computer vision in agriculture. The use cases are virtually limitless and can have a real impact on the welfare of humanity. 

AI-powered computer vision boosts the Agricultural Industry

AI-powered computer vision has the potential to save lives by improving agricultural yields.

It’s important to spread education and encourage more startups to create AI and computer vision-based products. Government bodies and non-profit organizations, alongside businesses, need to create websites and use other means to share how AI can help.

As we see further developments in AI and computer vision, we can expect even further improvements in agricultural yields.

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