Have you had your breakfast today? Take a moment to think about the journey that the various ingredients have taken. From farming to packaging and distribution to finally land on your table.
This entire lifecycle involves different processes and each of these processes has to be handled with efficiency, utmost precision and care to ensure that neither the nutrition nor the quality is compromised at any stage. And whenever there are processes that need efficiency and precision to go hand-in-hand that’s where technology can step in. The food production industry over the past years has leveraged various technologies to improve its operations and increase efficiencies like the Internet of Things (IoT), Robotics, Big Data Analytics and machine learning. These technologies have helped improve operations, reduce waste, enhance food safety, and improve the overall quality of their products. However, none has revolutionized the food production industry as much as Computer vision and Artificial intelligence.
According to Verified Market Research In 2028. The computer vision market is expected to grow from USD 14.82 billion in 2020 to USD 27.02 billion.
The process of computer vision includes encoding and transmitting images and statistical decision theory applied to visual data. The intelligence applied to this data help companies detect food containments, check the uniformity and grade of the products and even estimate the freshness of a product.
As demand for quality inspection and automation increases companies are also increasingly developing computer vision solutions to detect incorrectly labelled and sub-quality products. In addition, CV&AI also form an integral part of 3D imaging. In this blog, we will discuss some of the most prominent ways that CV&AI are together helping the food manufacturing industry.
Quality Control and Inspection
Food manufacturing companies must adhere to strict quality control and inspection standards to ensure the safety and quality of their products.
To do this at scale across various factories and outlets is the main task. CV&AI techniques, such as image analysis, can assist in this process by analyzing images of food products to detect any potential defects or anomalies. This could include detecting mould, insect infestation, or other contaminants, as well as checking for proper labelling and packaging. To do this process repeatedly and quickly and each time with pinpoint accuracy can only be possible with CV&AI.
Depending on the type of food, and the factory/outlet set-up, the way to meet these requirements is to customize the software and hardware for each customer along with access to proprietary data and specialized sensors.
Precision farming is a term we use to describe the use of technology to optimize and manage the use of resources in agriculture, including land, water, fertilizer, and seeds. CV&AI techniques can play a vital role in precision farming by analyzing images of crops to monitor growth, identify areas of stress, and detect disease.
For example, Corp Monitoring – where machine learning algorithms can analyze images of crops to identify signs of stress, such as discoloration, and provide information to farmers about the appropriate course of action. Or Precision irrigation which uses sensors and weather data to optimize water usage and minimize waste. This results in more efficient water usage and improved crop yields. These technologies enable farmers to make more informed decisions and optimize their use of resources, resulting in increased yields, reduced waste, and improved profitability. This also has the potential to help address food security challenges by increasing food production and reducing waste.
Food traceability refers to the ability to track food products from the farm to the consumer to ensure food safety and quality. It is a critical aspect of the food manufacturing process, and CV&AI techniques can play a key role in improving the process.
For example, CV&AI algorithms are today in use to recognize and interpret food product labels and packaging information, including ingredient lists, nutritional information, and product origin. This help to track food products through the supply chain, including during transportation, storage, and distribution. CV&AI is also in use to monitor the temperature and humidity of food products during transport. This ensures that that the food is in optimal conditions.
All of this data and information can be used to track the movement of food products from farm to table, providing transparency and accountability in the supply chain.
Recipe analysis is another area where CV&AI can play a vital role in the food manufacturing industry.
Today, CV&AI algorithms are also of use to analyze images of food recipes to understand the ingredients, cooking methods, and nutritional information. This information can then be used to improve the quality of food products and enhance the overall dining experience.
For example, CV&AI algorithms can analyze recipes to identify ingredients that are commonly used together, helping food manufacturers to create new and innovative products.
Packaging and Logistics
This is one of the most important roles that CV&AI plays in the food manufacturing industry. Every aspect of Packing and Logistics can be potentially improved and optimized using CV&AI. Even in areas such as sorting, counting and measuring where human intervention is known to cause anomalies, CV&AI can give accurate inputs.
CV&AI algorithms can generally measure the exact size of each fruit and compare it to the kind that is most likely to sell. For example – Most customers prefer apples that are between 75 and 80 mm in diameter, using this knowledge CV&AI can simplify grading fruits, vegetables, nuts, and oysters according to their shape, size, and maturity. All this in a fraction of a second.
Another example where CV&AI can be used is to analyse images of food packaging to ensure that it not meets the required standards but also ensures that the product is not faulty and that it is properly labelled. This simple thing can help prevent shortages and food recalls, which has business benefits. This information can also be used to improve the efficiency of the packaging process and reduce waste.
In addition, CV&AI algorithms can be used to optimize logistics, such as predicting demand for certain food products and optimizing the distribution of these products to meet the demand.
Food safety is of utmost importance in the food manufacturing industry, and CV&AI can play a critical role in ensuring that food products are safe for consumption.
For example, CV&AI algorithms can be used to analyze images of food products to detect any potential contaminants, such as bacteria or viruses.
This information can be generated as a daily report which could be used to improve and further enhance the safety of food products and prevent the spread of foodborne illnesses.
AI and computer vision technologies are also being used to increase personalization in the food manufacturing industry.
For example, AI-powered systems can analyze customer data to identify their preferences, allowing food manufacturers to create custom products that meet individual needs. This not only improves the customer experience but also helps to build brand loyalty and increase customer retention.
These are but a few examples of where the food production industry can benefit substantially by using CV&AI techniques. From precision farming to food traceability to packaging and logistics and recipe analysis, CV&AI can increase efficiency, reduce waste, enhance food safety, and improve the overall quality of food products. CV&AI can also be used further to acquire business insights and also detect consumer behavior.
SQUIRREL is a highly scalable, rapidly deployable SaaS platform empowered with Artificial Intelligence, Computer Vision, and Data Analytics. It allows enterprises to digitally Sense, Analyse and Control many aspects of the physical world. In the real world, it means granular control of inventory, improved demand planning, and a tighter supply chain.
SQUIRREL’s biggest strength is its ability to deliver across all of the above-mentioned use cases in real-world scenarios. One of its existing clients is a food industry major based in Germany, which has achieved a 20% reduction in waste, 90% accuracy in demand forecasting, 20% reduction due to optimization of logistics and a 10% increase in sales.
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