The year is 2070.
Flooding and unbearable heat have made many regions uninhabitable. Hurricanes and tropical storms often ravage the landscape, combining with particulate pollution to make the air outside almost unbreathable. Overpopulation, along with lack of habitable land, has forced many people to live together in cramped spaces, creating horrid living situations.
And, worst of all, there is a lack of food.
It is impossible for humans to create enough food to satisfy the needs of a population of over 9.4 billion people. Farms just aren’t efficient enough. Even in 2021, there were billions around the world who suffered from hunger. But with an extra 1.7 billion mouths to feed, farmers are presented with the impossible task of quickly increasing their crop yield to accommodate for extra individuals.
2070 is a nightmare. Climate change, overpopulation, and worst of all, no food — our world is a horrid place to live. If we had taken action earlier, 2070 might not have been so bleak and devastating.
Let’s rewind back to 2021. We need to find ways to counteract this disastrous future. One key way to fight this possible future is through optimizing our current farming methods.
Farming has come a long way, with new equipment and technology allowing for higher-quality crops and much higher crop yields. There’s no doubt that today’s technology is loads better than farming technology a hundred years ago.
But we can use technology to make today’s farms even better. To do so, let’s first look at how current farms are underperforming.
Farms today are growing to rely heavily on technology; high-end farms use robots, sensors, and drones to monitor crops and provide necessary nutrients, ensuring that all crops are as healthy as possible.
But even with all of these resources, much of current farming techniques are inefficient. Farmers have to manually diagnose when to harvest crops by looking at plant moisture and often have to estimate how much of necessary resources, like fertilizers, is needed.
Essentially, much of the farming process requires human intuition. And if there’s one thing that we know about humans, it’s that we tend to make a lot of mistakes.
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Trusting human intuition, for the most part, has allowed current farms to experience a lot of success. But there’s always downsides. Going by human decisions for problems like fertilizer estimation might lead to shortages in necessary fertilizers, leading to a poor harvest.
This problem leads to impacts on crop yield, and with a steadily-increasing population, it’s important that farms do not waste any crops whatsoever. Every crop should be put to use to feed as many people as possible.
Farmers need to find a way to use technology to help ensure that farmers buy exactly the amount of fertilizers needed and crops are not wasted. In fact, there’s a whole field devoted to the use of technology towards solving such problems called precision agriculture.
Precision agriculture revolves around the idea of making farming as efficient and precise as possible using sensors, robots, and other advanced technologies. Recently, precision agriculture has been employing the capabilities of artificial intelligence (AI) to achieve its goal of optimizing farming methods.
Artificial intelligence deals with using computers to predict or classify information given some data. Simplifying this a little, think of an AI as a person that reads a book on a subject and uses what it learns to perform a task. An AI that can classify between dogs and cats, for example, read a ‘book’ on the differences between dogs and cats, enabling it to recognize what makes them different.
The same idea can be applied towards agriculture. If an AI ‘reads a book’ on a specific topic in agriculture, it can perform the tasks that it read about at superhuman speeds and accuracies, making agriculture techniques both more convenient and more effective.
The proper technical process involves using an AI algorithm to analyze data from farms to optimize a specific task. The algorithm scans through the dataset, using information about the farm to perform necessary tasks.
Current sensor capabilities coupled with weather forecasts give us a plethora of data to create a model, from soil nutrient information to humidity levels. Using all of this information, we built an AI to optimize farm fertilizer purchases.
Fertilizer is an essential need for farms, but there’s some problems with purchasing it. If too little fertilizer is purchased, then a portion of the crops won’t get necessary nutrients, leading to a huge decrease in crop quality and yield. If too much is purchased, then farms lose money that could be devoted towards solving other problems.
We want to figure out exactly how much fertilizer a farm needs to get the best crop yield possible. This saves potential declines in crop quality while preventing overspending.
Luckily, AI is great at predicting things! We decided to use a neural network to solve this problem and predict necessary quantities of fertilizer necessary to optimize farming.
But, uhh… what is a Neural Network?
A neural network is a type of AI algorithm that’s really good at prediction and classification. Often, they’re visualized through a graphic of interconnected circles called nodes.
The nodes on the left-hand side are collectively called the input layer. Here, information is inputted into the neural network. In our problem, this information would be quantities like soil data, temperature, and other farming-related data.
The middle section, or the hidden layer, allows a neural network to develop a more in-depth understanding of the information. The larger a hidden layer (or the more nodes it has), the more complex information it can learn.
Finally, the output layer on the rightmost side returns the (you guessed it) outputs. Our problem involves outputting a certain number of pounds of fertilizer necessary.
Each of these nodes are stored in a computer as numbers called parameters. The end goal of the neural network is to learn parameters that are really good at predicting quantities of fertilizer necessary for a farm. It does this by scanning large amounts of data.
Data goes through the input, hidden, and output layers (in that order) to create outputs. The model then looks at its outputs and sees how incorrect its predictions were, changing its parameters to generate more accurate predictions.
Now, how do we use this for farms?
We created a model dataset containing features like average monthly temperature, wind speed, rainfall, humidity levels, and soil nutrient composition. The dataset also contained information about the optimal pounds of various types of fertilizers used given various weather and soil nutrient conditions.
A quick note — this dataset used completely random values. None of the data is accurate; it was used to give an idea of how real data can be substituted into the dataset and used to generate meaningful predictions.
Using this random dataset, we trained a neural network with 9 input features (corresponding to a farm’s different types of environment data), 32 nodes in the first hidden layer, 16 hidden nodes in the second hidden layer, 8 nodes in the third hidden layer, and 3 output nodes (corresponding to three different fertilizers).
Because of the randomness of our data (and our 24-hour time constraint), the model was not able to learn any meaningful information and had rather high loss values. Regardless, the model still successfully trained, showing that, with real data, a model that can predict pounds of fertilizer needed by a farm is easily trainable.
With more development time and clean, meaningful data, this model can become a powerful tool for farmers, ensuring that crops always give the highest possible yield and that no extra money is spent on acquiring necessities like fertilizers.
Creating such AI has advantages not only for large farms, but for small ones as well. Here’s some of the key benefits:
- Easily Modifiable. The neural network does not necessarily have to be used to predict pounds of fertilizer that a farm needs. With a few tweaks, the model can just as easily predict crop yield given weather conditions. It can also easily accommodate for more features about a farm or its climate.
- Scalable. The model scales really well to large datasets collected by large farms while also working well with smaller datasets.
- Ready Implementable. This neural network, after some tuning, can be shipped out to farms for usage within months, or even weeks. After training on some better data, the model is good to go!
- Feasible for Smaller Farms. The use of non-complex AI algorithms makes this a useful product for smaller farms that cannot devote cost towards managing large AI systems.
Through precision farming and AI, 2070 does not have to be a desolate landscape of horrors; with optimized farms, humans will be able to feed and sustain themselves for centuries. Whether it be aerial crop surveillance or weed detection, these AI-powered optimized farms are slowly becoming realities. And, with our neural network’s capability to predict necessary quantities of fertilizer for farms, we hope to help contribute to the future’s smart farms, making our 2070 a happier place!
This article was written as part of a hackathon at The Knowledge Society, where my team (Aashvi, Azam, Lilly and I) was challenged to create a pitch for how AI can be used to solve a tangible, real-world problem in under 24 hours. Check out our project Github here for the full solution.