AI in animal and plant behavior: The reality vs the hype

Understanding AI’s role: Beyond the hype

AI has become a buzzword. In every industry presentation, it is showcased as the magic wand that will transform the future. But AI is powerful only in specific ways. It works on data, patterns, and probabilities—it does not ‘understand’ things the way humans do.

Take animal behavior, for example. A dog wagging its tail, a bird fluffing its feathers, or a cow stamping its feet—AI can record and even categorize these movements if enough data exists. But does it know the feeling behind those gestures? No. It cannot feel joy, stress, or curiosity—it only compares the movement to a past dataset.

Plants give us another example. They do not wag tails or stamp feet, but they respond to sunlight, touch, or humidity. Sensors and AI can detect these responses, perhaps even predict how a plant might react under different conditions. But again—interpretation of meaning or significance belongs to us, not the machine.

The reality of AI in poultry production

When people talk about AI in agriculture, they often imagine futuristic farms where machines manage everything. But the reality is far more modest. Most AI models in use today are not focused on animals at all, but on the business side—purchases, warehouse management, production numbers, and sales forecasting.

These models depend heavily on human-provided data. Someone still must enter numbers manually into the system. On the other hand, in hatcheries and farms, the story is different. There, data comes directly from sensors in real time: temperature, humidity, ammonia, and so on. This ‘live data’ is what AI could eventually use to support management decisions.

Still, there is a big gap between analyzing numbers and understanding animals. Predictive models built on static, manually entered data are different from those based on continuous, real-world signals from barns or fields. And that gap is exactly where human expertise still matters most.

The overhyped potential of AI

AI certainly holds enormous promise. No one can deny its usefulness in fields where huge amounts of data must be processed quickly. But in agriculture, especially when it comes to animal welfare or plant health, the hype often outruns reality.

The truth is, current AI is narrow. It excels at pattern recognition in specific situations—like identifying heat stress in poultry flocks from movement data or spotting plant diseases on leaves.

But beyond those defined tasks, it struggles. AI has no intuition, no experience, and no ability to adapt in completely new situations. A farmer or scientist, on the other hand, can quickly sense when ‘something feels wrong’, even before clear data appears. That human instinct is not something AI can replicate.

A helpful analogy: Pointillism and AI

When I look at AI, I sometimes compare it to pointillism in art. In a pointillist painting, you see thousands of small dots, and your brain connects them to form an image. AI does something similar. It sees millions of data points and builds a picture from them.

This is exactly how car radar and image processing systems work: sensing points, patterns, and signals to make decisions. But here is the missing piece—humans do not just see the dots; we feel the painting. We attach meaning, mood, and emotion. AI may sense the points, but it has no sensor for feelings or deeper context. That is where human interpretation remains irreplaceable.

And this raises a larger question: If AI cannot feel, why do we sometimes respond to it as if it can?

Language, signals, and the Ernesto example

When two humans interact, we communicate not only through words but also through body language. A nod, a smile, or crossed arms—all these carries meaning because both people share a language and understand its intention. Communication is possible because of this common code of symbols.

In agriculture, the situation is different. Animals and plants do not share a symbolic language with us. They give signals—such as a bird fluffing its feathers, a cow stamping its feet, or a plant dropping its leaves—but they do not ‘explain’ these actions in words. It is always the farmer or scientist who interprets meaning: Is the bird stressed, the cow irritated, or the plant dehydrated?

This is also where AI comes in. It can process vast amounts of signals from sensors, cameras, and microphones. It may even classify them with great accuracy. But just like us watching an animal, AI does not ‘understand’ the meaning behind the signals—it only matches them to patterns in its database.

A recent viral video illustrates this point well. On YouTube, an AI-generated character named Ernesto sang a touching song, ‘I am still waiting at the door’. The performance was entirely synthetic—the old man never existed—yet millions of people, me included, felt genuine emotion while watching it. Why? Because AI processed images, sound, and words so convincingly that it mimicked the signals of real human feeling. But just like in agriculture, the true meaning behind those signals was not in the AI itself. The meaning was created in our interpretation.

In the same way, AI in farming can detect the ‘signals’ of animals and plants, but it is ultimately the human who assigns context, judgment, and action.

Is the human brain just another processor?

Sometimes I think of my own brain as a basic microprocessor. If I can connect dots between AI, agriculture, pointillism, and human feelings, then is it possible that AI will one day surpass the human brain?

Perhaps in raw data speed, yes. But not in thoughtfulness. The human brain does not just process signals; it dreams, doubts, feels, and reflects. AI can win a chess match against us because it calculates millions of moves in advance. But real life is not a chessboard. It is full of uncertainty, context, and emotions. And this is where humans will always have an advantage.

AI and the future: Collaboration, not replacement

Looking ahead, AI should not be seen as a threat but as a partner. It can help farmers and scientists detect hidden patterns, monitor flocks, or predict disease outbreaks. But the responsibility to act wisely on those insights will always rest on human shoulders.

Think of AI as an assistant that never sleeps. It can process sensor data at 2 am, but it cannot walk into a poultry house and sense that the flock is unusually restless. It cannot reassure a farmer, nor can it invent a new approach out of sheer curiosity.

The real promise of AI in agriculture lies in collaboration: machines handling the repetitive, data heavy tasks while humans bring judgment, intuition, and creativity.

Conclusion

The debate around AI in animal and plant behavior is complex. AI can certainly enhance our understanding by processing more data than we ever could. It can notice patterns invisible to the human eye. But it cannot feel, interpret emotions, or replace the instincts of an expert who has spent years watching flocks, fields, and farms.

The viral story of Ernesto reminds us of this truth. AI can move us with signals so real they make us cry—but the feelings come from within us, not from the machine. In agriculture, it is the same: AI can detect signals from animals and plants, but it is the farmer or scientist who gives them meaning.

So instead of asking whether AI will replace us, the better question is: How can AI work alongside us?

For me, the answer is clear. AI is an ally—one that helps connect the dots. But the final picture, with all its depth and feeling, still belongs to us.