An Over View Applications of Computer Vision in Agriculture

An Over View Applications of Computer Vision in Agriculture

Sun Nov 19 2023

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.

Read Also: Practical Application & Future of AI in Agriculture

 

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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.

Read Also: Find the Impact of AI in Self Driving Cars with our guide

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.

How Computer Vision Farming Combats Pests and Disease

Traditionally, spotting pests and diseases in agricultural fields relied on manual scouting, a time-consuming and often inaccurate process. However, computer vision farming offers a revolutionary solution. By leveraging cameras, drones, and powerful algorithms, computer vision farming can automatically monitor vast fields, identifying signs of trouble long before the naked eye can.

This early detection is crucial. By catching infestations or diseases in their nascent stages, farmers can take targeted action, minimizing damage and preserving yield. Computer vision can identify specific pests and diseases based on subtle visual cues in leaf color, texture, or growth patterns. This level of detail allows for a more precise response, compared to broad-spectrum treatments that can harm beneficial insects and disrupt ecological balance.

Furthermore, computer vision farming can track the spread of pests and diseases across fields over time. This real-time data allows farmers to predict outbreaks and implement preventative measures in unaffected areas. With its potential for comprehensive monitoring, early detection, and targeted intervention, computer vision farming is a powerful tool for sustainable pest and disease management.

Computer Vision and Its Applications Across Industries

Computer vision, a blend of computer science concepts and AI technology, endows machines with the ability to decipher and act upon visual information. This initiative isn’t just about imitating human sight but also empowering devices to extract meaning from images and videos. By developing algorithms capable of identifying relevant data from visuals, this field broadens its horizons, affecting more than just computer vision farming.

  • In the realm of healthcare, computer vision is redefining medical imaging. Its ability to facilitate the early detection of diseases is pivotal, offering healthcare experts a robust tool for interpreting diagnostic scans such as X-rays, MRI, and CT images.

  • Moving to the automotive industry, here computer vision serves as the backbone of self-driving technology and driver support systems. It equips vehicles with the perception to navigate surroundings, spot potential hazards, and determine safe outcomes on the road.

  • Retail businesses harness computer vision for streamlining inventory control, gaining customer insights, and elevating the shopping journey. Innovations like automated checkouts, shelf surveillance, and facial recognition for tailored consumer experiences are creations of this technology.

  • In manufacturing, consistent quality and efficiency are achieved through computer vision. It is instrumental in detecting product defects, which contributes to a more uniform production line and automated inspection processes.

  • The security and surveillance sector benefits immensely from computer vision. It powers technologies such as facial identification, object tracking, and motion analysis, bolstering the efficacy of security systems to act swiftly against threats.

  • The entertainment industry employs computer vision for recognizing gestures, and for enhancing virtual and augmented reality applications, resulting in more engaging gaming and interactive experiences.

  • In the financial world, computer vision strengthens fraud detection and security. Analyzing transactional patterns and pinpointing irregularities are part of its forte, thereby enhancing the safeguarding of financial activities.

  • Educational institutions integrate computer vision for automated grading, attendance monitoring, and developing engaging and interactive learning resources. This fosters the creation of cutting-edge educational tools and customized learning scenarios.

With continual advancements, computer vision’s imprint across industries is only growing besides computer vision farming. It presents creative resolutions to intricate problems and is reshaping our interaction with technology in life’s multiple facets.

 

Computer Vision and Its Applications Across Industries

Ethical Considerations and Regulatory Frameworks for Computer Vision in Farming

As the adoption of computer vision in farming continues to grow, it is essential to address the ethical implications and regulatory considerations surrounding these technologies. Some key areas of focus for computer vision in farming include:

Data Privacy and Security

  • Agricultural data, including images, video, and sensor data, often contains sensitive information about farm operations, crop yields, and individual farmers.

  • Ensuring the proper collection, storage, and use of this data, while respecting the privacy and data rights of farmers and agricultural workers, is a critical ethical concern.

  • Developing robust data governance frameworks and security measures to protect against data breaches and unauthorized access is essential.

Algorithmic Bias and Fairness

Computer vision in farming systems, like other AI-powered technologies, can perpetuate or amplify biases present in the data used to train them. Careful consideration must be given to the potential for these biases to lead to unfair or discriminatory outcomes, particularly in areas such as resource allocation, access to technology, and decision-making.

Implementing strategies to identify and mitigate algorithmic bias, and ensuring fairness and inclusivity in the development and deployment of computer vision in agriculture, is crucial.

Transparency and Explainability in Computer Vision in Farming

The "black box" nature of some computer vision in farming algorithms can make it challenging to understand the reasoning behind their decisions and outputs.

Promoting transparency and explainability in the development and deployment of computer vision in farming systems is essential to build trust, enable accountability, and ensure informed decision-making by farmers and agricultural stakeholders.

Conclusion

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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