Smart agriculture: Computer vision and robotics are being used to improve efficiency

Artificial intelligence in agriculture helps detect pests, plant diseases and malnutrition in farms. AI sensors can identify and target weeds before deciding which herbicide to use in the area. Precision agriculture, often referred to as artificial intelligence systems, is helping to improve the overall quality and accuracy of harvests.

The role of computer vision

We need a lot of land to feed billions of people. Today, manual cultivation is no longer possible. Meanwhile, crop failures are often caused by insect pests and plant diseases. Given the scale of modern agricultural operations, detecting and stopping this pest invasion is challenging.

This adds a new application to computer vision technology. Farmers can use aerial photography to identify early indicators of plant disease or pests at the macro level and to identify crop diseases at the micro level using close-up photos of leaves and plants. A commonly used computer vision method in these studies is convolutional neural networks. Today, more applications of artificial intelligence in agriculture are being developed.

It’s worth noting that we use the term “computer vision” very broadly here. Images are generally not the most reliable source of information. Many important aspects of plant life are best studied using other methods. Specialized sensors are often used to collect hyperspectral images or 3D laser scans are used to better understand plant health. In the field of agronomy, this type of technology is increasingly used due to the application of artificial intelligence in agriculture.

This data type is typically high-resolution and more similar to photos than medical imaging. AgMRI is an on-site monitoring system. Although specialized models are required to process this data, convolutional neural networks in particular can be used due to the spatial organization of the data.

Multi-million dollar investment in plant phenotyping and imaging research. The main task now is to collect sizable datasets of crop plants, usually in the form of pictures or three-dimensional images, and compare phenotypic information with plant genotypes. Research results and information can be used to advance agricultural technology worldwide. Agriculture is not the only field using smart AI systems, AI is also a hot topic in recruitment.

How Robots Are Used in Agriculture

Many autonomous agricultural robots are capable of digging holes and planting seeds in the ground while adhering to established basic patterns and taking into account the unique characteristics of the area. The robots are also able to manage the plant's growth process and interact with each plant individually. When the crops are ripe, the robots will harvest them, again treating each plant the way it should be treated.

Drones can automatically spray crops. Small, nimble drones can deliver hazardous materials with greater precision than larger aircraft. Additionally, aerial photography captured using spray drones can be used to collect data for the computer vision algorithms described at the beginning of this article.

Robots specifically designed for harvesting are increasingly being created and deployed. Combine harvesters have been used for a long time, while individual weeds can be identified and mechanically removed by robots. This is another remarkable achievement of contemporary robotics and computer vision, because before this, it was impossible to differentiate between weeds and beneficial plants, or to use robotic hands to interact with small plants.

Artificial intelligence improves efficiency in agriculture

While many agricultural robots are still prototypes or only tested on a small scale, the application of ML, AI and robotics in agriculture is already evident . It is believed that in the near future, more and more agricultural activities will adopt mechanization.

Today, more applications of artificial intelligence in agriculture are being developed. For example, a pilot project of one company applies computer vision to animal husbandry, but this area has not yet attracted widespread interest from deep learning companies.

Machine Learning and Artificial Intelligence in Agriculture

Of course, there are already some initiatives to use livestock tracking data for machine learning. For example, a Pakistani company has launched a collar that can wirelessly monitor the activity and body temperature of cows. And French researchers are working on facial recognition technology for cows.

Additionally, there are plans to apply artificial intelligence to pig farming, a hitherto underutilized industry with a market value of hundreds of billions of dollars. On modern farms, pigs are housed in relatively small groups and the most comparable animals are selected. Food is the main expense in pig production and, therefore, the main goal of contemporary pig production is to maximize the fattening process.

If farmers had a comprehensive understanding of pig weight gain, they could solve this problem. Animals are usually weighed only twice in their lives, when they are born and when they are sold. If experts knew how each piglet gained weight, they could design a unique fattening program for each pig, or even a unique combination of food additives. This will significantly increase production.

Although getting animals onto the scale is not particularly difficult, it can put a lot of stress on them, and stressed pigs will lose weight. This new artificial intelligence research aims to create a new, non-invasive method of weighing animals. Use computer vision models to infer pig weight from photo and video data. These estimates will be incorporated into existing traditional analytical machine learning models to enhance the fattening process.

What is the future of artificial intelligence in agriculture

Farming and livestock farming are sometimes viewed as outdated professions. Today, however, artificial intelligence in agriculture is becoming a common tool on many farms. The main reason for this phenomenon is the large number of jobs that are carried out simultaneously in agriculture.

They are so tedious that deep learning and contemporary artificial intelligence must be used to automate them. Although the cultivated plants and pigs are identical, they do not come from the same assembly line. Every tomato bush and every pig requires a unique approach, so human intervention is absolutely necessary.

We can leverage current developments in artificial intelligence to solve challenges while also automating technologies that interact with animals and plants and take into account their unique characteristics. Weighing a pig is easier than learning how to pass the Turing test, and operating a tractor in a wide field is easier than operating a car in heavy traffic.

Since agriculture remains one of the largest and most important industries in the world, even small improvements in efficiency can lead to significant gains. This is why many companies are prioritizing artificial intelligence in agriculture.

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