Knowledge and attitudes about world food production are changing. Due to the rapid changes in the size and culture of the food industry, it is difficult to assess this change concisely. However, we can point out some recent developments. Phrases such as “farm to table” and “buy local produce”, organic sections of almost every supermarket, and meat substitutes in fast food restaurants show a growing awareness of food.
These changes in food awareness are important because they facilitate conversations towards sustainability. But the challenges facing the food industry cannot be solved by consumer trends and “woke” chefs alone. In fact, the world’s food production is an expensive company, accounting for more than a quarter of all greenhouse gases and absorbing almost two-thirds of all freshwater.
These complex problems require detailed solutions, and some technologies will eventually be able to make significant contributions. In short, the accurate deployment of artificial intelligence and machine learning can have a significant impact on the sustainability of food production, transportation, sales, and consumption around the world.
It’s a Global Issue
Feeding the world is not an easy task. Unfortunately, some of the challenges that are being faced by the farmers will not improve in the coming decades. The combination of a growing population and a deteriorating natural environment puts a great deal of extra pressure on systems that are already using a lot of resources. It means that agriculture needs to do more with less effort, as everyone still has to eat.
In addition, this polarised scenario will need to be looked at from a global perspective, agricultural automation must begin to be seen as a data management issue. This is where AI can make a big contribution. Hardware advances, such as the use of drones to monitor crops, need to be combined with a new understanding of the complexities of rapidly changing agricultural landscapes. AI is a natural ally in this environment, as all forms of machine learning require robust data entry to be truly effective.
Farmers are always practical people, and there is no reason to think that they do not welcome new toolsets that can provide forecasts and analytics that can take into account weather patterns, news developments, global warming, and floods around the world. Real-time reports on water conditions, market demand, soil conditions over time, and crop conditions. As agriculture meets the need for greater precision, data scientists and machine learning programmers can be expected to work with farmers in the field.
Reaching the Consumers
Agricultural improvement is only the first step in optimizing our food system. Food, whether animals, minerals, or vegetables, must also be delivered from the farm to consumers. The traditional thinking here is primarily focused on transportation. From market-trained bots to self-driving cars, it’s true that artificial intelligence should be a major development in supply chain management, but in fact, food waste is a much bigger problem in robust economies.
Today, it is normal and acceptable to dispose of more than one-third of the food we produce. If food waste is a country, it will be the third-largest carbon producer after the United States and China. You will never achieve zero waste, but you should seriously consider adapting to these criteria. After all, if you lose more than 30% of your production, all AI solutions in agriculture will be wasted. The consumer environment plays a major role in reducing food waste, and AI can help here in amazing ways.
Taking from the top warehouse management system can make significant improvements if it is enhanced with the widest possible range of data tools. Demand forecasts are still a bit mysterious today, but it’s easy to implement new, more comprehensive solutions that can not only handle such complexity but can also thrive. Pricing is an important area where a more rigorous approach is urgently needed, and AI-powered pricing and discounted vehicles can keep more products away from the trash. AI is also used by manufacturers to develop products that better meet consumer demand.
The area of climate change solutions is unlikely to have any celebrities as ambassadors, but such efforts generate billions of revenues and are a major factor in the reduction of “greenhouse effect”. AI provides better tracking and analysis of how food reaches people from farms, provides retailers with predictive statistics, and provides consumers with incentives to save money. This speaks volumes for the potential lifespan of these developments, as it allows tax information to replace supermarket altruism.
From Tech to Plate
Even when food is ultimately placed on a plate, pantry, or refrigerator shelf, there are many actions that lead to the full utilization of the resources that contributed to food production. Data is beginning to play a major role in personal health, but food prices can rise, putting consumers under the same pressure as farmers on food. Shopping rules (excitement, brand navigation, trends, wisdom, everyday combinations) will soon be enhanced by very personal and highly detailed data entry.
AI plays an important role as people include more personal health data in their shopping lists. This will take into account the purchase history associated with the above global movements. Imagine a shopping cart that makes recipe suggestions based on recent blood sugar levels, sleep patterns, vegetable freshness, and supplier datasets. The food is not very tasty and has the added benefit of knowing how to get the most out of the food you buy.
At the End of the Tunnel
“We are the first generation to feel the effect of climate change and the last generation who can do something about it.”
Barack Obama, Former US President
This is a good statement to keep in mind as a motivational mantra. The challenges faced as we continue to find new and better ways to feed the world’s population are not the reason for our pessimistic outlook. When it comes to food, we need to be smarter.
Recent developments in machine learning and data management offer many promising solutions for optimizing food systems. The most important thing to understand when using AI effectively is that the data must be of very high quality. World food production can certainly provide an infinite amount of data to such a system, but the challenge now is to learn to ask questions. AI helps eliminate much of the waste and inefficiency of food systems, but it is our responsibility to create these tools for specific purposes.
