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Published: 23/04/2024

From Supply Chain Management to Disease Prevention: 5 Practical Applications of Computer Vision in Agriculture

Large-scale farming requires an enormous effort that can be challenging when done with conventional tools. Fortunately, advanced technologies assist us in automating specific tasks or supporting those that can’t be done entirely automatically. In this article, we’ll focus on five applications of computer vision technology that can transform agriculture and drive progress towards sustainable agricultural practices that are so important in the face of the significant threat of climate change. Let’s start with the basics and briefly discuss the general contribution of computer vision (CV) in the agricultural setting.

How Does Computer Vision Contribute to Farming and Sustainable Agricultural Practices?

Computer vision enables machines to perceive the world similarly to humans. Artificial intelligence (AI) and machine learning (ML) make it possible not only to “see” through the cameras but also to process and interpret the visual input. Thus, such smart machines can perceive and understand what they see and provide valuable information on the world’s population and the state of farms.

Therefore, smart farming technologies using CV advancements will find their place in various monitoring tasks (e.g., crops, soil sensors, livestock) and automation. This can be especially useful for large-scale farms that require a massive effort to ensure every aspect is thoroughly supervised, and human efforts could use some support. There are several benefits of utilising computer vision in agriculture.

Practical Applications of Computer Vision in Agriculture

Why Is It Worth Considering Employ Computer Vision in Smart Farming?

Before we move to detailed examples of using computer vision in farming, let’s shed more light on the benefits of applying these advancements in agriculture.

  1. More sustainable farming: Utilising CV to automate specific processes, such as plant irrigation and weed and pest control, helps to optimise resource use and, in some cases, limit chemical pesticides, reducing the negative environmental impact. Cameras and drones can, for example, monitor the water level and provide constant hydration for plants in just the required amount or point out weed-infested areas, enabling targeted application of herbicides.
  2. Continuous efficiency improvement through data analysis: Intelligent tools and machines can collect different kinds of data
  3. Yield prediction and management: By observing the plantations over the season and collecting data on crop health and conditions, the algorithms can analyse the data to provide insights on optimal harvest times, storage conditions, and the yield distribution window.
  4. More accurate plant and livestock health monitoring: By constantly monitoring plants and livestock, farmers can identify health issues earlier and react swiftly to manage the problem before it spreads, thus protecting healthy plants or animals.
  5. Precise produce quality control: Constant monitoring helps to control the quality of the produce, plants, etc., assuring that only high-quality products enter the supply chain. The algorithms can detect damage and highlight items that don’t meet the quality standards, thus allowing farmers to use them for other purposes rather than going to the market.

Moreover, governments launch programs that facilitate technological advancements and new technologies to support sustainable farming practices. For example, the European Union launched programs that help finance technological undertakings contributing to sustainable agriculture, thus allowing tech companies to secure funding for their precision agriculture-related projects.

What Are the Challenges and Concerns of Adopting Computer Vision in Farming and How to Prepare to Face Them?

As with every major technology and change, adopting computer vision in agriculture has various challenges and concerns. However, some ways can help address and overcome them. Let’s look at several common problems and the ways to solve them.

Large Initial Investment Cost Can Be Mitigated with Financing Programs and Initiatives

Adopting advanced technologies like computer vision and artificial intelligence can bring significant profit and cost reduction in the long run. However, it also involves an initial investment that can take a toll on the budget, as the entry threshold is high. Fortunately, the European Union and other government organisations in different countries offer programs and projects that can aid in financing new technological solutions for farming, thus encouraging the adoption of more sustainable agricultural practices.

Explainable AI and Education to Deal with Technological Scepticism

Some farmers have doubts about adopting advanced solutions like computer vision, as they are concerned that they might interfere with the usual operations that work for them. However, proper education in the field can mitigate them significantly, and explainable AI will shed some light on how the whole mechanism works.

Explainable AI is a branch of artificial intelligence that uses detailed visualization to show the workings of algorithms and machine learning models. It is used to clarify what data is used and why, what features the algorithm or model has to reach the output and are the individual layers that make up the model, and how they lead to the output or prediction. It allows insight into the decision-making processes computers make when boosted with CV or AI. It’s a solid method for dealing with doubt and scepticism.

Data Augmentation and Synthetic Data Aid in Building Reliable Datasets for Training

Quality, well-annotated data is essential to creating reliable computer vision algorithms and machine learning models that enhance the agricultural industry. However, gathering reliable datasets with varying environmental conditions and other unpredictable factors that govern the sector may be challenging. Fortunately, data augmentation and synthetic data generation can enhance training datasets.

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What Are the Practical Applications of Computer Vision in Agriculture?

CV has many valuable applications in farming, ranging from soil analysis, geospatial mapping, and field coverage analysis to crop rotation, management, and agricultural impact on biodiversity. We’ll focus on five of the most impactful ones close to us and our computer vision experts.

  1. Plant and livestock health and growth monitoring and enhancement
  2. Disease detection and pest management
  3. Supply chain optimisation
  4. Predictive analytics
  5. Precision farming

1. Computer Vision for Monitoring and Enhancing Plant and Livestock Health and Growth

Utilising cameras and AI-enabled drones can constantly monitor growing plants from above to supervise if the growth is even in every part of the field. Even growth means that the plants are healthy and not troubled by pests. Visual data gathered by drones allows detailed insights into plant health and the quality of crops. 

Disease Detection and Pest Control. Computer Vision for Agriculture

Computer Vision and AI can also automate plant watering to ensure even and regular water distribution and optimal soil moisture levels. It also limits human interference, as bacteria and environmental factors can influence the growth. In the case of less frequent human inspections, the plants have more consistent conditions supporting their health and ensuring quality yields.

CV capabilities will also find their use in livestock monitoring. By installing cameras in barns or deploying drones to monitor pastures, farmers can keep an eye on the well-being of their livestock. Moreover, by combining visual output with sound-sensitive cameras, they can look for any sounds that could point towards illness, thus allowing for immediate reaction. Vision systems can also contribute to identifying animals and tracking them to ensure their safety and control the various global population numbers.

Visual livestock monitoring using robotic systems also enables farmers to track behavioural patterns and their growth without constant human interventions that might be stressful for the animals. Monitoring their behaviour consistently can provide valuable insights into feeding patterns, social interactions and activity levels. And since drones and cameras work automatically, the supervision can be constant, allowing early detection of alarming occurrences.

Utilising computer vision and automation for monitoring allows for constant supervision around the clock, which would be hard to achieve with human-only work. It’s harder to see at night, and the risk of error is higher. Machines can gather data continuously with consistent accuracy, providing insights into the condition of the plants and livestock, enabling prompted reactions and more informed decisions.

Computer Vision for Monitoring and Enhancing Plant and Livestock Health and Growth

2. Disease Detection and Pest Control with Computer Vision Methods

Intelligent vision systems allow for early detection and crop management of diseases and pests. Good cameras can capture high-quality images that cover more than the human eye can see. They can then process these images to extract relevant features by adjusting the lighting or removing the background noise. After processing, the image recognition technology helps the system identify patterns, shapes and textures that indicate specific diseases or pest signatures.

Precise weed control and early pest detection help prevent them from spreading across entire fields, enabling prompt reaction and saving crops from dangerous outbreaks. Targeted treatments are also more environmentally friendly than field-wide applications.

Continuous monitoring to collect data and insights provided by collected data help track the progression of diseases and pest populations over time and develop more effective management strategies based on the history of diseases and pest infestations.

3. Computer Vision for Supply Chain Management in Agriculture

CV enables farmers to manage the supply chain on large-scale farms more effectively, from the growth through the harvesting process and delivery. It can also help monitor the quality of produce, ensuring high customer satisfaction. Here are several ways in which this technology can help improve crop management and supply chain efficiency.

Automated Harvesting with Computer Vision-Enhanced Robots

Innovative solutions enhanced with computer vision, like agricultural robots, can be used for automated harvesting of, for example, fruit such as strawberries. They could automatically determine the ripeness of each fruit and collect them while leaving those that still need to mature. It could speed up the harvesting process and reduce manual labour costs. It can be especially beneficial for large fields that require ample area coverage.

Sorting and Quality Control with Computer Vision

After the harvest, computer vision-enhanced machines can sort the produce based on size, colour and quality, ensuring that only the best is deployed to the market. Such an automated process is fast and efficient, offering reduced manual labour and consistent accuracy, as machines don’t grow tired, and the risk of human error is eliminated.

Computer Vision for Supply Chain Management in Agriculture

Computer Vision for Stock Monitoring and Retail Management in Post-Harvest Sales Processes

Real-time computer vision systems can monitor inventory levels by tracking the movement of goods within warehouses. This can significantly improve supply chain efficiency and reduce losses. Additionally, such systems can be used to monitor the condition of agricultural products during transportation. This ensures the products are stored at the correct temperatures and humidity levels, maintaining their quality until they reach their final destination.

Computer vision technology has numerous applications in the retail sector. It can be used to manage inventory, restock shelves, and optimise product placement by analysing consumer buying patterns. Even at the point of sale, computer vision systems can help ensure the quality of agricultural products, assisting retailers in removing items that do not meet the required standards.

4. Using Computer Vision for Predictive Analytics in the Farming Sector

Predictive analytics is a bountiful field for adopting advanced vision technologies. From anticipating the weather impact to crop health predictions, computer vision aids in the following prediction tasks:

  • Weather conditions and their impact: Cameras and AI-enhanced drones can constantly supervise and record the weather conditions and their effect on crops or livestock. Farmers can proactively apply safety measures to ensure crop health and livestock well-being in severe conditions.
  • Pest outbreaks: Computer vision provides data-driven insights on pest infestations and outbreaks, thus enabling early detection. It also allows farmers to predict the spread and make informed decisions on preventive measures to protect crops and reduce waste. Analysing collected data will allow farmers to find weak points and add extra protection to prevent future pest outbreaks and crop damage.
  • Crop yields: Visual data gathered by cameras and drones can provide information on yields and help predict them so that farmers can make informed decisions regarding future harvests. Computer vision systems help collect data on quality and quantity, thus allowing you to take measures that will enable higher yields.

Predictive analytics enhanced with artificial intelligence and computer vision can improve different aspects of the agricultural industry. Precision agriculture is another important use of this technology in farming.

5. Computer Vision Applications in Precision Farming

Precision agriculture (PA) is a farming management strategy that utilises technology to observe, measure, and respond to crop variability within and between fields. An essential aspect of precision farming is accessing real-time data about crop conditions, soil, ambient air, hyperlocal weather predictions, labour costs, and equipment availability.

Precision agriculture allows farmers to utilise advanced techniques for food production and cultivation. AI-powered algorithms combined with computer vision have significantly identified image patterns. For example, machine vision methods for grain assessment can increase accuracy compared to manual, as machine learning algorithms enable analysing massive volumes of data, regardless of complexity, quickly and accurately.

Computer Vision in Smart Farming. Using Computer Vision for Predictive Analytics

Another example of using computer vision for PA is weed control. Proper and accurate identification of weeds among healthy crops allows farmers to limit the use of solid pesticides in areas impacted by weeds, thus protecting crops from unnecessary damage from strong chemicals.

Precision farming involves several components, one of which is mapping. Geospatial mapping helps farmers plan and execute accurate and predictive operations that enable them to make the most of their land while conserving resources. Geographic Information Systems (GIS) and satellite imaging gather data that facilitates precise mapping and supports crop production and threat management.

Additionally, integrating edge computing and AI (Edge AI) allows for real-time processing on the farm, reducing reliance on cloud-based services and enabling scalable solutions. Therefore, AI and computer vision-enhanced precision farming can streamline and improve various operations and processes.

Start Your Agricultural Computer Vision Innovations with Us

We’re here to serve if you want to enhance your equipment with vision systems. We’ll happily implement vision components to your machines and build algorithms and machine learning models to transform and automate your existing and new processes. Be it fruit collection robots or vision systems that monitor the state of leaves to detect diseases, contact us, and let’s put your plans into motion.

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Katarzyna Świątek

Content Specialist at DAC.digital

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