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Top 5 Computer Vision Use Cases in Agriculture in 2024

Top 5 Computer Vision Use Cases in Agriculture in 2024Top 5 Computer Vision Use Cases in Agriculture in 2024

The agriculture sector is one of the most important industries in the world since it is the source of our food. As digital technologies revolutionize every industry, agriculture is no exception. Like every other sector, the agriculture sector also faces various challenges, including climate change, labor shortage, and the disruptions created by the pandemic.

Digital technologies such as computer vision can help the agricultural sector overcome these challenges and achieve efficiency, resiliency, and sustainability.

This article explores 5 computer vision use cases that can help agriculture tackle current challenges and excel in the future.

1. Crop monitoring with drones

Drone technology is being extensively used in the agriculture sector to overcome labor shortages and improve efficiency. The market for drones in agriculture is projected to reach $3.7 billion by 2027.

In precision agriculture crop monitoring, drones are installed with a high-definition camera which is enabled with computer vision and geothermal technology to:

  • Detect crop condition and health
  • Monitor soil condition
  • Map the farmland according to the crop area
  • Detect abnormalities

These drones can be highly efficient and can cover a large area much faster and more accurately than human monitoring.

Source: Business insider

However, investing in drones enabled with computer vision can be expensive; therefore it is important to study the business, short/long-term expectations, and ROI before purchasing such technologies.

2. Crop sorting and grading

Computer vision-enabled machines are being extensively used in sorting and grading the harvest. Since these jobs involve repetitive and time-consuming tasks, automating them can offer efficiency and speed.

Through machine vision systems, crops of different types can be identified and sorted based on order requirements. For example, some orders require large size potatoes, and some require medium-sized ones. A machine vision system can do this in a fraction of the time it would take to do it manually.

Machine vision systems can also sort products based on perishability to identify which batch to ship first and which ones to ship later.

Check out this computer vision-enabled apple grading machine.

Computer vision systems are also used in counting fruits and vegetables. Check out this example of a computer vision system counting apples directly from trees.

3. Pesticide spraying with drones

Spraying pesticides on crops is a common practice to protect the produce from pests and diseases. However, this can be a time-consuming process and if inhaled, it can be harmful to the farmer’s health. 

Automated drones can perform this task with higher precision and speed. Drones with spray guns and cameras enabled with computer vision can identify areas that need pesticide and spray accordingly in required amounts.

4. Computer vision phenotyping

Phenotyping refers to measuring and analyzing plants’ characteristics for research purposes. Information is gathered to learn how plants grow, what environment is best for specific plants, and insight into plant genetics. 

In the past, it was done manually, but now it is performed through AI and computer vision. As climate change threatens the agricultural sector, computer vision-enabled phenotyping enables breeders to learn more about plants to make them more resilient to the changing weather. It also helps farmers in finding the crop that would be most successful and sustainable. 

Watch this short video to learn how computer vision-enabled phenotyping works.

5. Livestock farming

Artificial intelligence is being widely used in the livestock farming market. The investment in AI is projected to significantly increase by 2026 and computer vision accounts for the largest chunk of that market.

Figure 1. Overview of AI livestock market increase from 2021 to 2026

Source: GMC

Computer vision technology combined with IoT can provide the following benefits for precision livestock farming:

  • Monitor the health of all livestock including cattle, livestock, sheep, pigs, and poultry 
  • Examine the health of the livestock with high definition cameras
  • Monitor food supply for the livestock
  • Detect abnormal behavior of the livestock
  • Counting livestock through drones
  • Send real-time information to the farmers for farm management planning and decision making

A recent study was conducted on a computer vision and deep learning system to monitor dairy cows with accuracy and with real-time data. The system successfully identified cows through pelt patterns, evaluated their position, understood the actions of the cows, and tracked movement.

Source: ScienceDirect

To ensure the success of your computer vision projects you can find the best annotation solutions from our sortable and filterable lists of:

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Shehmir Javaid
Shehmir Javaid is an industry analyst in AIMultiple. He has a background in logistics and supply chain technology research. He completed his MSc in logistics and operations management and Bachelor's in international business administration From Cardiff University UK.

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