How Can AI Help Solve Hunger Insecurities Around the Globe?
Reyna Carswell
Grade 10
Presentation
No video provided
Problem
With 8 billion people on the planet how can we ensure that everyone gets the nutrition they need? In 2023, 9.16 million people in Canada experienced food insecurity. That is 20% of our population. 2.33 billion people experience hunger insecurity globally. In Canada alone we waste 46.5% of all the food on our shelves. Could AI help reduce waste and increase efficiency in the food production and distribution system not just in Canada but around the globe?
Method
1. Make my question as precise as possible
2. Do some brief research to get a general idea of my topic
3. Talk to friends or family who might be knowledgeable in the subject
4. Once I have a brief understanding of my project start real research (Look at websites, videos, magazines)
5. Write down all of my research (In logbook and on docs)
6. Review my sources to make sure there accurate + write my sources out
7. Write down my research in CYSF website
8. Edit all my writing
9. Review with my work with my teacher
10. Finalize everything for the due date
11. Write the things that will go on my board
12. Assemble my board
13. Practice my presentation (In front of family friends and teachers)
14. Make sure everything is in order and go set up at the science fair
15. Go to the science fair and present :)
Research
NEURAL NETWORKS
Neural Networks are a complex system of different interconnecting layers made up of nodes. Neural Networks were made in part to emulate the human brain, with the end goal of producing a machine that can learn and almost think just as well as a human being. These networks can take in information and have it pass through multiple different layers, to try and recognize different patterns and features to meet the end goal it was programmed to meet.
HOW DO THEY WORK?
LAYERS
A neural network has three main layers, the input layer, the hidden layers, and the output layer.
The input layer is where the neural network receives raw data, this could be an image or words.
The hidden layers are responsible for processing the information by using weighted connections. Each ‘neuron’ or node in the hidden layer performs some type of mathematical operation and passes the result of that equation to the next layer. The more layers the more complex the neural network, but you must consider how many layers will help us achieve the target accuracy? How many neurons will help us achieve the target accuracy? How many connections should be retained from the previous layer’s neuron?
The output layer is responsible for generating the final answer, this could be a prediction or a classification.
The “neurons” or nodes are the basic unit of a neural network. Each node is a simple mathematical function that takes multiple inputs and then factors in weights and biases with the end goal of producing an output. Each node has its own linear regression model composed of input data, weights, a bias and an output. The formula would look similar to this.
∑wixi + bias = w1x1 + w2x2 + w3x3 + bias
output = f(x) = 1 if ∑w1x1 + b>= 0; 0 if ∑w1x1 + b < 0
WEIGHTS, BIAS AND ACTIVATION FUNCTION
Weights are used to determine the strength of connections between each node. These weights decide how much influence a neuron’s input has on the next node. This is done by multiplying the input by the weight as it passes through the connection.
Biases are an additional constant value added to the weighted sum of inputs. It helps the model make better predictions and allows shifting the activation function left or right, meaning the bias controls when a neuron activates. Without bias the neuron activates only when the input and the weight are large enough, but with a bias the neuron can still activate even if the input and weight are quite small or even when the input and weight are zero. Bias can control when the neuron activates by either requiring it to be a bigger value so it shifts the function to the right, or it can allow the value to be smaller by shifting the function to the left.
The activation function is similar to a gatekeeper of sorts as it will decide whether or not a node’s output will pass on to the next layer. These activation functions allow neural networks to learn and identify more complex problems and allow them to behave in a non-linear fashion. This is quite useful because most data or images that come from the real world are full of nuance and variability, and are therefore quite non-linear. The activation function decides which neurons should be active or inactive. Some common activation functions are the Sigmoid which is an s-shaped curve and is used in probabilities. ReLU is used in most deep learning models like CNN and RNN. SoftMax is used for multi-class classification.
These three critical elements work together to ensure that the output is as precise as possible.
OPTIMIZATION ALGORITHM
An optimization algorithm is used to update the weights and the biases of a neural network based on the loss function. The gradient descent is a widely used optimization algorithm that can adjust the weights and biases in the direction of steepest descent.
PARAMETERS
Trainable parameters: These parameters include the biases of neurons in every layer except for the input layer, and the weights of the connections between the nodes. These parameters are considered trainable because they can be changed and updated.
Hyperparameters – Thes are fixed values, which are then fine-tuned through experimentation, to achieve the lowest possible cost value. Design-related hyperparameters include many hidden layers, the number of nodes, the types of activation function, the optimization algorithm used, and the loss function used. Some hyperparameters are adjusted after the design finalization, they are the learning rate which controls the magnitude of updates to the weights. High learning weights can lead to missing optimal weight values, and a lower learning weight could make the training process a lot longer. The regularization prevents overfitting by reducing the amount of learned information. The batch size controls the number of samples used to update the weights during training. Batch learning which uses a group of samples, which can aid in improving the performance and decrease outliers.
TRAINING
The first step in training is Initialization where the neural network will randomly assign weights and biases.
Forward propagation is when you pass input data through the network to get a desired output. You input the data and each node receives the input and multiplies it by its weights, it adds bias, and applies an activation function. The output of one layer will then go on to become the input for the next layer and so on and so forth. Then finally the output layer reaches its final prediction.
After we have the network’s prediction, we compare it to the real answer, to measure how wrong or right the neural network is using a loss function. A loss function comes up with a quantitative value between the neural networks predicted output and the true value. Some common loss functions are cross-entropy loss, this is used when dealing with probabilities. Intuition is another loss function, so if the neural networks prediction is far from the correct label, the loss is therefore high and if the prediction is closer the loss is low. These are loss functions used in classification. Another loss function used for regression, is mean squared error, penalizes large errors more than small errors. The formula for calculating the loss-function, is also known as mean squared error (MSE).
- i represents the index of the sample,
- y-hat is the predicted outcome,
- y is the actual value, and
- m is the number of samples.
πΆππ π‘ πΉπ’πππ‘πππ= πππΈ=1/2π ∑129_(π=1)^πβ(π¦ Μ^((π) )−π¦^((π) ) )^2
The next step is backpropagation. Once we know the error, we can then update the weights and the biases to reduce the error. This is done with backpropagation, which will usually involve gradient descent and partial derivatives. The gradient is how much each weight on each node has contributed to the mistake. We then take the derivative of the loss function with respect to each weight and bias; this will then tell us how to change each weight to decrease the error. The neural network will then calculate how much each node contributed to the total mistake. This error is propagated from the output layer to the input layer. The weights and the biases are then updated so when we run the program we will have a lower level of error.
We will then redo these steps over and over multiple epochs, each time the neural network will keep on getting better and better at what it is trying to learn.
Validation is then used to help confirm that the neural network is adept in learning patterns that generalize well to new data. This is a test to see if the model has actually learned instead of just memorized. Validation is done by introducing a completely new dataset that was not seen in training. This will tune hyperparameters and check performance. During training you would track the network's validation loss where we should see a decrease in if the neural network is improving. You also track validation accuracy which should increase if the model is doing well. To improve the result of validation you can use some techniques like early stopping, so you would stop when the model stops improving. You would use regularization to prevent overfitting. You can hyperparameter tuning, to adjust the network parameters for the best possible performance. A final test will be performed to gauge how well the network can perform on new unseen data.
HISTORY OF NEURAL NETWORKS
The idea of a thinking machine can be traced back to the Ancient Greeks, what we see as true thinking machines weren’t really seen till the mid 1900s. In 1943 Warren S. McCulloch and Walter Pitts published together “A logical calculus of the ideas immanent in nervous activity”. This research aimed to understand the mysteries of the human brain or more specifically how the human brain could produce complex and incredible patterns through connected neurons. One of the main ideas that would come out of this work was the comparison of human neurons with binary threshold to Boolean logic (0/1 or true/false statements). In 1958, Frank Rosenblatt developed the perceptron “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain”. He advanced Warren S. McCulloch and Walter Pitts research a step further by introducing weights to the equation. Leveraging IBM 704(), Rosenblatt got a computer to learn how to distinguish cards marked left vs. cards marked on the right. In 1974, Paul Werbos was the first person in the US to note the use of backpropagation within neural networks with his PhD thesis. In 1989, Yann LeCun published a paper illustrating how the use of constraints in backpropagation, and how its integration into neural networks can be very useful to train algorithms. This research helped recognize neural networks as hand-written zip code digits provided by the U.S. Postal Service.
CONVOLUTIONAL NEURAL NETWORK (CNN)
A convolutional neural network or CNN’s is a type of deep learning model that processes images, videos, and other spatial data. They are able to preserve spatial relationships using very special layers, instead of treating each pixel separately. CNN’s use convolution to detect patterns like, different textures, edges and shapes. There are four main layers: the convolutional layers, activation layers, pooling layers, and the fully connected layers.
The convolution layer handpicks important patterns from an image. It moves across the image, looking out for specific shapes like edges, textures and patterns. It works similarly to how you as a human will pick out certain features from an image to identify what it is. It makes processing images much more efficient as it doesn’t have to spend time looking over every single individual pixel. After scanning the entire image for certain patterns, it creates a feature map which is a new version of the image where only the specific details such as edges, shapes, and textures are highlighted. The number of filters used affects the depth of the network's output. Stride is the distance, or number of pixels. Zero padding is when the filters do not fit the image. This will then set everything outside the input to zero, which produces a larger or equally sized output. There are three types of padding, the valid padding is when there simply is no padding. Same padding ensures the input image is the same size as the output layer. Full padding increases the size of the output image.
The activation layer makes decisions in a CNN. It decides which patterns are the most relevant, and which ones are unimportant. It helps filter out any detail that is irrelevant and makes everything easier to identify the image because you can see the details much more clearly. The most common activation function in CNN’s is ReLU, it quickly filters out the weaker information and will only keep the most important information. This makes CNN’s much faster and much more efficient.
The pooling layer resizes the images making them much smaller yet they still keep the most important details. It pools all the most important information together, it is a summary tool that makes CNN’s faster and much, much more efficient.
The fully connected layer is used for decision-making, combining information, and learning complex patterns. The fully connected layer is the layer where all the nodes are connected to every node in the previous layer. It is often the very last step before a decision is made by the network. The fully connected layer, first flattens the image into a 1D vector. In the fully connected layer, each node is tasked with looking at all the features, and determining just how important each piece is to the final decision. The layer takes all the information from the previous layer and combines it with a weighted sum. This is where all the learning happens during training, the weights of connections get adjusted to make the right decision. The node then applies an activation function to its output so that it can decide what value to pass forward. This layer then combines everything together to make the final decision.
The output layer outputs class probabilities using SoftMax (for multi-class classification) or Sigmoid (for binary classification)
CNNs work well for images as they can capture spatial hierarchies, parameter sharing which makes them computationally efficient, they are translation-invariant, meaning they can detect any features regardless of their position in the image.
RECURRENT NEURAL NETWORK (RNN)
A recurrent neural network or an RNN are a type of neural network that has sequential data. They are designed to process sequential data. RNNs have a memory mechanism that allows them to keep information from previous time steps, which makes them much better equipped to solve tasks involving ordered data or time-dependent data. This can be natural language processing (Machine translation and text generation) or speech recognition.
STRUCTURE OF RNNs
The input layer is where all data enters the network. The data is entered piece by piece.
The hidden layers are the memory of the neural network. In a regular neural network, which treats each input separately, an RNN remembers what it has seen before and uses that information to help and influence the current step. Each time the RNN processes a new word it updates its memory and combines what it has just received as input and what it has previously learned.
After processing everything the RNN will produce an output. There are different outputs for different tasks, it could be a translated sentence, or it could maybe be the weather forecast.
DIFFERENT VARIATIONS OF RNNs
One-to-One: Each one input has one output which is identical to a normal neural network.
One-to-Many: One input generates a sequence of outputs.
Many-to-One: Many inputs that lead to one output.
Many-to-Many: Many inputs that lead to many outputs.
PROBLEMS
They struggle to remember important information from further back in the sequence. When gradients become too small during the process of backpropagation, earlier layers fail to learn properly. Gradients can also become too large, leading to unstable training.
LONG SHORT-TERM MEMORY NETWORKS (LSTMs)
Long short-term memory networks or LSTMs are an advanced type of recurrent neural network (RNN), designed to fix the long-term dependencies that appear in RNNs.
We need LSTMs because they solve the problems that come with RNNs, like the vanishing gradient problems. LSTMs solve this by having more advanced memory mechanisms. Unlike regular RNNs which have a single hidden state, LSTMs introduce a cell state that enables long-term memory, along with three gates that control information flow.
STRUCTURE OF LSTMs
The cell state is the memory of the LSTM, this state carries important information, and allows the network to retain or forget past data as needed. LSTMs also have the usual hidden states that come with the standard RNNs, that are used for short-term memory.
The first gate is the forget gate, which decides what past information should be “forgotten" by the cell state. It looks at the previous hidden state and the current input and outputs a value between zero (completely) and 1 (fully retained).
The input gate decides what new information should be stored in the cell state. It has a filter that will decide which values will be updated. I had a candidate update which creates new memory values to be stored. The forget gate and the input gate work hand in hand to update cell state, useless information is deleted and important information is added.
The output gate determines what the next hidden state should be, the hidden state is the passed to the next step and is used in the process of making predictions
TYPES OF LSTMs
There are certain variations of LSTMs that may aid in improving efficiency. The bidirectional LSTM – Processes data in both forward and backward directions to be able to look at things from two perspectives; the past and the future. The peephole LSTM – Allows gates to directly look at the cell state for better and more informed decisions. The gated recurrent unit is a simplified version of LSTM with fewer gates, making it faster while still effective for long-term dependencies.
HUNGER INSECURITY
In 2023 the U.S. 47 million people in the United States experienced food insecurity, 14 million of the people are children. In 2023 in Canada 22.9% of our population in the ten provinces lived in a food-insecure household. That amounts to 9.16 million Canadians which includes 2.1 million children. Yet in 2023 46.5% of all food in Canada is wasted. 41% of this is avoidable. 23% of avoidable food waste is from "best before" dates. In the U.S about 30%-40% of food is wasted every year. Food waste doesn’t just affect people but also contributes to 11% of the world’s emissions of greenhouse gases like methane, carbon dioxide and chlorofluorocarbons.
EFFECTS OF HUNGER INSECURITY
Hunger has many adverse effects, which can be extremely detrimental to a person's life. Hunger can cause fatigue, apathy, weight loss, obesity and can even inhibit a child's development. People may also suffer from the myriad conditions that come with mineral and vitamin deficiencies, such as anemia, rickets and many more. These conditions may happen because when food is scarce, people are more likely to turn to high-calorie meals that have lower nutritional values, such as many different fast-food options, because they tend to be cheaper. These adverse effects make it even more difficult to succeed, if you have no energy or are constantly tired, then that makes it even harder to do well at school, or at a job you might have. As well, if a family doesn't have enough money for food, it's unlikely that they have enough money to support education, or extracurriculars. This all could lead to generational poverty, where children who experience food insecurity become adults that experience food insecurity.
NEURAL NETWORKS IN AGRICULTURE
Canada is said to have lost the equivalent of seven small farms a day for 20 years. Each year we have less and less farmland, due to factors such as urbanization and soil quality. This forces us to convert forest land into farmland, which has so many environmental repercussions. This means that we need farms to be more efficient and more effective, to not just help our farmers but to also help feed people. If there is a surplus, then prices will be lower. Using neural networks, farmers could save billions of dollars each year, by using image recognition with convolutional neural networks, check for pests, soil composition, fertilizer management, livestock health and well-being. AI can also help make data-based decisions, by factoring and monitoring the weather conditions, the application of fertilizer and pesticides. AI can also detect leaks and irrigation systems. Using convolutional neural networks, you could detect mold, rot, insects, or other threats to crop health.
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Images from CBC News
AI IN GROCERY STORES
In Canada, we rely heavily on “best before” dates, yet these dates aren’t always a correct indicator of food safety. Best before dates simply indicate when the manufacturer determines they are at their best quality. After their best before dates, they may lose their flavour or become stale. Yet in Canada, we often associate the best before date with the safety of the food, this leads to food waste. If grocery stores were able sell foods that have passed their “best before” date at a discount price, we can help 9.16 million Canadians with food insecurity. Managing all of this can be hard for many different grocery stores and make them less willing to implement such a system. AI could be used to keep track of every product that comes in and out of the store, keeping track of their quality and safety. For example, once the food item reaches a point where it is no longer as flavourful or when the food has gone stale, AI could markdown the price. This would limit food waste and many people could get more of the food they need. The food may not be the freshest quality, but it is still safe to eat, and can help people put food on the table. This will not only help more people, but it will also help decrease food waste and in turn help decrease our greenhouse gas emissions. AI food tracking could help grocery stores around the world, helping millions even billions of people.
PROBLEMS
There are many problems that could accompany the widespread implementation of AI in agriculture and grocery stores. Firstly, there will most-likely be push-back from people who work in those areas, as there will always be push-back when new technology is introduced, and may be difficult and time-consuming to integrate AI into existing systems. There will also be financial barriers, as we need people to train and program these neural networks. The neural networks may also make mistakes, as it could misdiagnose any disease in crops or mistakenly miscalculate the soil conditions or it could make a mistake while categorizing produce in grocery stories. AI could “hallucinate” where it makes things up, it could become confused, it could have insufficient training data, or make incorrect assumptions.
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Data
βIn 2023, 47 million people in the United States experienced food insecurity, 14 million of which were children. In 2023, 22.9% of the Canadian population in the ten provinces lived in a food-insecure household. That amounts to 9.16 million Canadians, including 2.1 million children. Yet, in 2023 46.5% of all food in Canada was wasted. 41% of this is avoidable. 23% of avoidable food waste is from “best before” dates. In the U.S., about 30%-40% of food is wasted every year. Food waste doesn’t just affect people but also contributes to 11% of the world’s emissions of greenhouse gases like methane, carbon dioxide and chlorofluorocarbons.
With the rapid advancement of technology, we have tools such as neural networks, which can help us solve many issues such as healthcare, or as we are discussing today in solving hunger insecurities. Canada is estimated to have lost the equivalent of seven small farms a day for 20 years. Each year we have less and less farmland, due to factors such as urbanization and soil quality. This forces us to convert forest land into farmland, which is has so many environmental repercussions. So, while we are making up for the loss of farmland, we also need those trees. They help keep our air clean, and they are the home to millions of different species. What will happen when we run out of space. This highlights the need for farms to be more efficient and more effective, to not just help our farmers but to also help feed our people. Using neural networks, we can help farmers save billions of dollars each year, by using image recognition with convolutional neural network, check for pests, soil composition, fertilizer management, livestock health and wellbeing. AI can also help make data-based decision, by factoring and monitoring the weather conditions, the application of fertilizer and pesticides. AI can also detect leaks in irrigation systems. Using convolutional neural network, you could detect mould, rot, insects, or other threats to crop health. This can be extremely valuable when we are now living in a world were we now feel the effects of climate change and where we are losing more and more farmland everyday. If our farms become more efficient, and if the quality is better, then this food will become more accessible. The more we have of a certain item, the lower the price will likely be, and the items may be of a better quality, people can still get healthy meals everyday.
In Canada, we rely heavily on “best before” dates, yet these dates aren’t always a correct indicator of the food’s safety. Best before dates simply indicate when they are at their best quality. After the best before dates, the product may lose their flavour or may become stale. Yet in Canada, we often associate the best before date with the safety of the food, leading to food waste. If grocery stores were able to sell foods past their best before date at a discount price we could help 9.16 million Canadians with their food insecurity. AI could help manage all of this as it can be hard for many different grocery stores to implement this system. AI could keep track of every product that comes in and out of the store, keeping track of their quality and safety. For example, once the food item reaches a point where it is no longer as flavourful or when the food has gone stale, AI could mark down the price. This would limit food waste and many people could get more food. The food may not be the freshest, but it is still good to eat, and can help people put food on the table. This will not only help many people decrease food waste, it could also help decrease our greenhouse gas emissions. This could be implemented not only in Canada, but in grocery stores around the world, helping millions, even billions, of people.
AI is an invaluable tool that can be applied in a variety of sectors, which includes solving hunger insecurities. By implementing AI to help in agriculture and help keep track of produce in grocery stores, we can help millions of people. This will contribute to creating a higher quality of life, promoting the economy and helping create a better brighter future for people around the globe.
Conclusion
Across our country, we waste 46% of all our food, yet 9.16 Canadians still go hungry. The facts tell us that we have more than enough food to go around. AI can help make agriculture more efficient and effective, decrease crop loss, and increase food production. It could help decrease food waste, in grocery stores and help make life easier for millions of people. Because easy access to nutritious food should be a human right, people should be able to live without fear of hunger. We don’t have to just implement this technology into agriculture and grocery stores, we can use it to help more evenly distribute food to smaller towns, or schools to help students. We can also decrease greenhouse gas emissions and help slow the effects of climate change that we are starting to feel more and more each year. This will benefit everyone, not just the 9.16 million people in Canada that experience food insecurity. This will help foster a brighter better future for everyone.
FUTURE CONSIDERATIONS
If I continue to pursue the exploration of this topic, I would like to explore in a more in-depth manner how neural networks are programmed. I would also like to have the chance to speak to people who work in the farming and food industry. I would also like to spend more time researching how hunger insecurity affects other countries, instead of just focusing on Canada and the United States.
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Acknowledgement
I would like to acknowledge my parents who have supported and helped me with this project. I would like to thank my friend Evrett Sansom who has helped me and supported me all throughout my science fair journey. I would also like my teachers and especially Ms. Trainor for supporting our science projects at Western Canada.