AI and Big Data: Using Crop Data to Benefit the Food Industry

The food industry has grown significantly in recent decades. Previously, citizens went to their local grocery store to buy fresh fruits and vegetables; Now I can choose from several supermarkets in every city, not to mention online shopping.

  • The food market has exploded and, as a result, consumers are finding more variety and better quality products at lower prices.

Crop data with technological support

To meet these demands, the food industry has resorted to use of AI and Big Data. It is therefore possible to analyze and optimize every stage of your production process. Even at the beginning of the supply chain, where the raw materials are grown in the field.

  • The food supply value chain is very broad, ranging from sewn seeds to shops and supermarkets.
  • For its part, Big Data is often used from the first stage of food production. This is considered the growth phase, where it helps in fertilization, irrigation and crop disease management processes.

It is worth mentioning that the use and benefit of this data extends beyond these elements and goes even deeper among farmers.

Looking beyond the production line to the food production process, crop quality becomes critical.

Food producers generally pay the same price for a truckload of product, regardless of the quality of the cargo it contains. Most of the time, quality problems are only discovered during the production process.

  • For example, in the case of pomegranate, the nutritional input during the fruit growth phase determines the level of acidity of the harvest. In turn, this affects whether the pomegranate is made into juice or sold as fresh fruit.

For a juice producer, who wants to keep their product standardized and consistent for consumers, it is essential to receive unaltered fruit.

Similar results have been observed with almonds, where it has been proven that the quality of oil from properly fertilized trees produces almonds with better health benefits and a longer shelf life. This allows manufacturers to have a healthier and longer lasting product.

If a truckload of product received does not meet the food manufacturer’s criteria, it may have to be rejected. This results in large amounts of waste and unknown production per truckload of product.

This ultimately affects the food producer’s bottom line and filters additional costs into the supply chain.

Using AI and Big Data helps improve every stage of your food production process.

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Profitable processes and system utilization

In order to maintain reliable production and low costs, it is essential that food manufacturers improve the quality of their input ingredients and minimize waste.

Fortunately, there is a solution to the unpredictable quality of these input ingredients: It all starts with the health and nutrition of the crop.

By relying on Big Data and AI, crop nutrient requirements can be calculated accurately and efficiently. They can even be adapted to any type of crop and growing conditions.

Data such as rainfall, temperature and soil type are added to the fertilization and yield data of each specific crop variety to create a holistic view of the nutritional and management needs of each crop.

All of this data is analyzed using advanced artificial intelligence and can be used by food manufacturers to ensure that farming practices are:

  • Efficient
  • Profitable
  • Productive
  • Sustainable

By defining live, customized crop nutrition plans for optimal performance, feed producers can ensure they receive the highest quality ingredients and maximum, predictable truckloads from their suppliers.

In turn, this also brings key environmental benefits by reducing waste and minimizing disruption to your production processes.

Naturally, AgTech innovations like these offer the potential to improve production of many different food products throughout the industry, except for pomegranates and almonds.

These technologies are critical to making crop nutrition plans more advanced and affordable than ever, providing key decision support systems for growers and food producers and enabling measurable long-term benefits across the board.

For producers, implementing these digital solutions at the field level essentially means that they are empowered to play a vertically integrated role in the food supply value chain and, in turn, increase their profitability.

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Greater use of resources

From a broader perspective, the digital nature of these solutions also offers opportunities for greater collaboration, enabling agronomic research to be conducted on a global scale.

By allowing researchers to combine their data with a wealth of global knowledge about specific species:

  • Crops
  • Disease management
  • Nutritional needs and more

These types of technological developments are key to raising quality standards, enabling food manufacturers to keep up with consumer demands for quality food at a competitive price.

With the rapid development of the use of artificial intelligence and digital solutions becoming more advanced and common throughout the food supply value chain, the positive effect on the food industry as a whole is evident.

Simply put, the benefits of data collection are not just for the harvesters, but extend to the entire industry and the sectors that feed on it.

Digitization allows us to quantify progress from the field to the selection and distribution of food products.

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