The food industry has grown substantially over the past few decades. Where individuals used to visit their local grocer for fresh fruits and vegetables, they now have the choice of several supermarkets in every town, not to mention online shopping and even same day delivery. The food market has boomed and, as a result, consumers now expect more variety and higher quality products—all at lower prices. To keep up with these demands, the food industry has turned to the use of AI and Big Data to analyze and optimize each stage of the production process, including at the very start of the supply chain where raw ingredients are grown in the field.
The food supply value chain is vast, stretching from the sewn seed through to stores and supermarkets. Big Data is often utilized from the very first stage of food production—considered the growing stage—to aid in fertilization, irrigation and crop disease management processes. However, the uses and benefits of this data extend far beyond just these elements—and far beyond the farmers’ field.
Looking further down the production line to the food manufacturing process, crop quality becomes critical. Generally, food manufacturers pay the same price per truck load of product, regardless of the quality of the load. Most often, any quality issues are only uncovered during the manufacturing process. For example, in pomegranates, nutritional input during the growing stage of the fruit determines the acidity levels of the crop, which in turn determines whether the pomegranate is made into juice or sold as a fresh fruit. To a juice producer—wanting to keep the product standard and consistent to consumers—receiving highly variable fruits makes this difficult to achieve.
Similar results are seen in almonds, where oil quality from properly fertilized trees has been found to result in almonds with better health contributions and a longer shelf life, giving producers the ability to boast a healthier product which lasts longer.
If a received truckload of produce doesn’t meet food manufacturers’ criteria, it may have to be discarded. This results in large amounts of waste and an unknown output per truck load of produce, ultimately affecting the bottom line for food manufacturers and filtering additional costs down the supply chain.
To keep output reliably high and costs low, it’s critical for food manufacturers to improve the quality of their input ingredients and minimize this waste. The good news is that there’s a solution to the unpredictable quality of these input ingredients, and it all starts with crop health and nutrition.
By leveraging Big Data and AI, crop nutrient requirements can now be calculated accurately and effectively, and can even be tailored to each individual crop type and growing conditions. Data such as rainfall, temperature, and soil type are added to fertilization and yield data for each specific crop variant to create a holistic view of the individual crops’ nutritional and management needs. This data is then analyzed using advanced AI and can be leveraged by food manufacturers to ensure growing practices are as efficient, cost-effective, productive and sustainable as possible. By providing their growers with bespoke, live crop nutrition plans to achieve optimal yield, food manufacturers can guarantee they receive top quality ingredients and a maximal, predictable truck load from their suppliers. This, in turn, also unlocks key environmental benefits by reducing waste and minimizing disruptions to their production processes.
Naturally, AgTech innovations such as these offer the potential to improve the production of numerous different food products across the wider industry, beyond just pomegranates and almonds. These technologies are key in making crop nutrition plans more advanced and more accessible than ever, providing key decision support systems for both growers and food manufacturers and facilitating long-term, quantifiable benefits across the board. For manufacturers, deploying these digital solutions at field level essentially means that they are empowered to play a more vertically integrated role in the food supply value chain and, in turn, to increase their profitability.
Taking a wider view, the digital nature of these solutions also critically provides opportunities for further collaboration, making it possible to conduct agronomical research on a worldwide scale. By enabling researchers to combine their data with a wealth of global knowledge around specific crop varieties, disease management, nutritional needs and more, issues exactly like this can be addressed for stakeholders industry wide.
Ultimately, technological developments like these are key in driving quality standards upwards, allowing food manufacturers to keep up with consumer demands for quality foods at a competitive price. With the use of AI rapidly developing and digital solutions becoming more advanced and commonplace across the food supply value chain, the positive impact for the wider food industry is clear. In a nutshell, the benefits of harvesting data aren’t just for those harvesting crops.