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Computer Vision in Agriculture

An Over View Applications of Computer Vision in Agriculture

Agriculture is one of the most important industries in the world because it is our food source. As digital technologies are transforming different industries, agriculture is also being transformed by these technologies. Like other industries, agriculture faces various challenges, including climate change, labor shortages, disruptions caused by epidemics, and others. Digital technologies, including computer vision, can greatly help the agricultural sector overcome these challenges and achieve greater efficiency, flexibility, and sustainability.

In this article, we will discuss the applications of computer vision in agriculture. We will also explain the benefits and challenges of each.

An overview of computer vision

An overview of computer vision

Computer vision is a subset of artificial intelligence that gives machines the ability to think and interpret visual data like humans. Computer vision systems analyze a collection of images, snapshots, and other objects and can track, label, describe, predict, and evaluate specific objects in those images. Its purpose is to train artificial intelligence to go beyond simple automation processes, especially by integrating deep learning. Computer vision technology can include all cases by training artificial intelligence based on presence and pattern recognition. For example, when we see a scene, our eyes record the scene, where the objects are and whether they are moving; based on this, we can make predictions.

As everything is expanding, experts expect computer vision to develop and expand in the modern world and make changes.

How computer vision is helpful in agriculture

As mentioned in the previous section, computer vision is a subset of artificial intelligence that gives machines the ability to understand and interpret the visual world like humans. By combining computer vision techniques with remote cameras to capture images, non-contact and scalable sensing solutions are possible in agriculture.

Below is a list of some of the key applications of computer vision in agriculture. As technology advances, we can expect to see more cases in this area shortly.

Applications of computer vision in agriculture

Agriculture, livestock, and farming all benefit from computer vision techniques. When agriculture, as the oldest profession known to man, is combined with the new frontiers of computer vision and machine learning, it is poised for many advancements. The adaptation of deep learning and machine learning applications in agriculture will be transformative, making processes more efficient, accurate, and automated, and bringing us closer to the future.

With the help of computer vision systems and the large amount of data they provide, farmers can make the most informed decisions and provide faster solutions. There are many applications of computer vision in agriculture, and we look at some of them in this section:

Harvesting with machines

It can be said that the benefits of agriculture with the help of computer vision are many, that is, the principles and methods of agricultural automation can be different. Machines are an efficient and time-saving way to harvest crops. Specialized robots equipped with object recognition and deep learning technologies can harvest fruits and vegetables autonomously.

Currently, farmers can classify and identify their products through computer vision systems, and specialized machine learning models can check and identify the condition of the product. By using computer vision in agriculture, the work process becomes faster and the need for manual work is reduced. This technology can also be very useful during harvest seasons, as fruits and vegetables perish if left unattended.

Of course, it should be kept in mind that fruit recognition is one of the most difficult tasks in agricultural automation. These complications are due to unpredictable variables in the process that can distort the results, such as inconsistent light, poor visibility, the presence of a lot of foliage, inconsistency in the shape of the fruit or vegetable, and others.

Self-driving tractors

Self-driving tractors are becoming a huge success in agriculture. Relying on computer vision, they have a complete 360-degree view of their surroundings. Using self-driving tractors, time-consuming tasks such as harvesting and removing crops can be done faster and more sustainably. These tractors need the help of neural network algorithms to analyze the data captured by their cameras and later use it to prove their algorithms and improve performance.

With the help of computerization, tractors can tag crops, giving farmers the ability to monitor plant changes while tracking the necessary trends to increase crop yields.

Of course, you should keep in mind that self-driving tractors are a big investment, but you should be careful about the legal aspects of buying them. Since self-driving tractors operate in public areas, there is a risk of damaging or even harming the soil and plants. There is transmission to humans and animals.

Self-driving tractors

Grading and sorting

Assessing characteristics such as color, size, and defects in crops is a task that agricultural workers perform to ensure the quality of their harvest. They also evaluate the needs of a particular product. Traditional human grading and sorting is an intensive and time-consuming process. Larger farms require more workers and growers to sort and grade hundreds of thousands of crops each day.

In this situation, computer vision is introduced to innovate the process through scanners equipped with image classification technology. Using artificial intelligence and computer vision algorithms, farm workers receive real-time monitoring of crop growth and satellite images of their conditions.

Of course, while early technologies and new prototypes are showing promise, they still have some shortcomings that need to be addressed. For example, scanning the product through a 2D image can lead to inconsistencies. Also, the biological diversity between fruits and vegetables should be considered for yield.

Vertical farming

Vertical farming is transforming traditional agriculture by growing crops vertically indoors, increasing crop production in a small space instead of relying solely on land, machinery, and water. Vertical farming improves performance and space by using computer vision to monitor a plant’s life cycle through its cameras and sensors.

Vertical farming can control temperature, gases, and other things by maximizing the use of agricultural technologies. It can also reduce water consumption and labor costs, ensure more sustainable production, and use energy more efficiently.

Because vertical farming is done in indoor environments, it does not allow insects to enter and will affect the pollination process. Farmers should keep such things in mind and do pollination manually. It should also be said that vertical farming depends on technology, which means that the smallest problem can affect the entire process.

Vertical farming

Aerial imaging and scanning

In recent years, drones have been widely used for tasks such as remote sensing and fire detection. Aerial imaging and scanning using computer vision technology aims to create what is known as agricultural vision. The purpose of using drones is to provide accurate data for agriculture.

The goal of precision agriculture is to improve environmental sustainability, yield estimation, and efficiency while reducing the number of traditional inputs required to grow crops, including land, water, fertilizer, and others. This is possible through accurate imaging and mapping to evaluate each area based on the imaging results.

Drones equipped with extensively trained computer vision models are an essential element of smart agriculture for processes such as vegetation classification and segmentation, crop counting, yield monitoring and prediction, crop mapping, weed identification, and disease and nutrient deficiency diagnosis. These drones allow people in the agricultural sector to remotely control agricultural products.

Of course, it is important to note that there are still limitations to computer vision in aerial agriculture. These limitations include subtle differences in the condition of plants over time, such as different plants going through different stages throughout the year and at different times. The technology needs to be error-free in its deep learning capabilities to assess these spatial changes without compromising accuracy.


Aerial imaging and scanning in agriculture with the help of drones is known as precision agriculture. In general, the use of drones increases accuracy in performance and results because they use artificial intelligence. If you are looking to increase the accuracy of your farming process, you can use these drones!



In general, the impact of computer vision systems and deep learning methods in the agricultural sector is very high because they create many improvements. Along with the expansion of agricultural machine learning data, agricultural robotics, and automation, this industry can eliminate many of its problems such as sustainability, climate, and labor shortage. With technological advancements in agriculture, there is no longer a need to spend long hours grading and scanning fruits and vegetables by hand or harming physical health. Considering that technology and agriculture are now together, farmers, botanists, and many workers in the agricultural sector can devote their time to other processes in their field of work.

Saiwa helps companies annotate, tag, and use higher-quality data. If you are looking for a reliable tool to speed up your field, this is the right place.

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