The population growth of the world is an ongoing process. According to current estimates by the UN, the global population will reach 9.7 billion by 2050 and over 10 billion by the 2080s. Of course, it makes sense because millions of people are having children every year but not as many are dying thanks to the advancement in medical technology.
What a time to live, right?
Well, there is just one problem. The human race needs sustainable food production to survive. Come to think of it, the total land mass of our planet is limited. A big portion of the available land is drowning every year due to global warming. Also, the growing population needs places to live, taking up empty lands for housing.
Do you see where I’m going with this? The day may not be far when we pivot the balance of nature, having more people than we can feed. It’ll inevitably lead to world hunger and famine, the kind humans have never seen before.
The only option we have is to improve the agriculture systems we have in place. The goal must be to increase food production without the need for more land or resources. And this is where artificial intelligence technologies can come into play.
Unless you’ve buried your head under the sand, you know about the AI Renaissance. From climate change to world politics, artificial intelligence has a role to play in every layer of society. AI in agriculture is perhaps our biggest hope in saving the planet.
How, you ask? Well, that’s what I plan on indulging in this post.
Lifecycle of the Agricultural Sector
To succeed in understanding the impact of artificial intelligence in agriculture, we need a little background on how the industry works as a whole. Of course, the details will vary drastically between horticulture/agronomy/forestry and animal husbandry.
My focus of this post is mostly on the plant section of agriculture. Here is a typical cycle that resets at the end.
- Preparation of Soil
- Sowing of seeds
- Adding fertilizers
- Weed protection
Preparation of Soil
This is the phase where the parties involved need to prepare the soil for the upcoming steps. At the end of the day, everything we eat somehow comes from the soil. Even livestock is heavily dependent on the status of soil health.
According to the cycle, this phase is supposed to prepare the land for the seeds. The process will depend on the type of crop the farmers are planning to grow. A common practice during this phase is breaking up large chunks of soil and adding fertilizers to prime it.
Sowing of Seeds
This is the phase where the appropriate type of seeds are sown into the soil. Farmers must adhere to the distance between two seeds, the depth it should be sown, and other conditions like sunlight duration, humidity, and temperature.
Once the seeds are sown, it’s time to nurture the agricultural production. One of the primary steps in this phase is adding necessary fertilizers depending on the crop health.
Fertilizer not only ensures the crop yields well but also influences the nutrition of the crop and fertility of the land. In simple words, fertilizers are nothing but macro and micronutrients for the soil, applied externally.
The soil moisture content at farmlands plays a crucial role in crop growth. Acres of healthy crops and months of human effort can go to waste if irrigation systems don’t do their job. Irrigation is simply the process of supplying water to farmlands in moderation.
No, it’s not the kind of weed you’re giggling about. These are unwanted plants that suck the nutrition out of the soil and hampers plant growth. Farmers either weed them out manually or use chemicals to kill them off. If not done in time, excess weed can destroy the yield.
Once the crops reach maturity, they need to be harvested in due time. Not harvesting mature crops for too long will ruin them and ruin the soil in the process. This is perhaps the most labor-intensive phase of the agricultural sector as most crops only allow a short window for harvesting.
Last but not least, the harvested crops must be stored properly. It may or may not need cold storage access. This is the phase that guarantees food security, something many developing countries are still struggling with.
Challenges in Traditional Agriculture
Why would you even consider smart farming if the traditional methods are working well, right?
In my experience, everything that seems well-functioning may not always be. The need for artificial intelligence in agriculture comes from the existing challenges. Let me shed some light.
Weather is a major player in the agriculture business as anything out of the ordinary can disrupt production. For example, too much rain in a season can cause the soil to become over-moist. Similarly, not enough rain is also not good. Then there is snowfall, drought, wildfire, and even pollution!
Greenhouse Gas Emission
When you think about agriculture and farming, greenhouse gas is not one of the things to pop into your mind. But greenhouse gases are one of the biggest drivers of climate change and a huge portion of these gases are released by crops.
One of the main focuses of agricultural researchers and scientists is changing the genome sequencing of crops to reduce emissions. Predictive analytics by AI can help regulate it big time.
Inconsiderate use of chemical fertilizers and pesticides has a drastic effect on nearby water sources. Every year, billions of gallons of water get contaminated by these chemicals. It’s not only putting the residents in the area in danger but also hampering biodiversity.
Also, overusing the freshwater reserves for irrigation is causing the water levels to go down fast.
You already know the importance of healthy soil for healthier crops. But every year, the soil is contaminated by the farming process. The CO2 content in the soil increases with every harvest.
It’s not possible to monitor this with traditional methods. Farmers are unknowingly hampering the productivity of their lands by not giving the soil enough attention.
Of course, the fertilizers and pest control measures taken every year also degrades the quality of the soil. Apparently, it messes with the pH level in the soil, ruining the balance of nitrogen, calcium, phosphorus, and other necessary nutrients.
Why Use Artificial Intelligence in Agriculture
Agriculture and artificial intelligence surely sound like the most distant entities on our evolution spectrum. One has been around for millennia while the other isn’t even a century old.
Believe it or not, it’s possible to marry the two for the betterment of all. And it surely comes with benefits.
Data-Driven Decision Making
Data is everything in the modern world. The more data we collect, the faster we can grow. Interestingly, AI systems are also fueled by data. At its core, the purpose of artificial intelligence is to mimic human intelligence without human effort.
The process of feeding data to AI is called training. The larger the dataset, the more accurately a model can mimic human intelligence.
Take ChatGPT’s latest rendition, GPT-4, for example. OpenAI claims it has been trained with over 1 petabyte of data. That’s 1,000,000 gigabytes of raw data for us peasants!
The agriculture industry happens to have a lot of data, collected over centuries. The best thing about data is that it doesn’t have to be digital. Even age-old documents smelling like the attic at your grandma’s house can also be used for training Ai in agriculture.
AI then uses this data for predictive analytics. It’s the process of “using data to forecast future outcomes”. In simple words, AI can analyze what we’ve been doing in the agricultural industry and share the most optimized route to the best harvest quality.
Of course, we humans can also analyze but it’s not going to be nearly as fast or accurate as modern computer systems.
Saving Money in the Long Run
Sure, setting up the equipment to use AI in agriculture sounds expensive. And it is. But when you look at the big picture, we can actually save a lot of money and other resources by delegating the work to AI. The interesting thing is that it may not always come off as cost savings and more as profits.
For example, farmers are trying to get a better harvest quality using fewer resources. Resources cost money. If they can use AI-based robots to reduce wastage while keeping the yield the same, it means more margin on the profits.
Also, predictive analytics by adopting AI can help optimize all the phases in an agricultural cycle. For example, farmers can save in water bills for the irrigation systems if the AI maps the usage based on weather conditions.
Don’t worry about it too much right now because I’ll share some practical applications of AI in agriculture soon.
One of the main appeals of artificial intelligence technology has been automation. Manufacturing robots have been around for many years, doing tedious jobs on behalf of factory workers.
Then there are self-driving cars. Yes, when you’re taking a nap in your fancy Tesla, it’s an AI driving the car.
Similar to other industries, the agricultural industry can also benefit hugely from emerging technologies like AI. Imagine agricultural robots preparing your land, sowing seeds, and harvesting the crops while maintaining the highest quality.
The best part is that, unlike human labor, AI labor is not unpredictable. It’ll perform the tasks over and over without getting tired. And if it’s a self-learning model, it’ll only get better over time.
Tools like driverless tractors, IoT-powered drone sprayers, and smart irrigation systems are already in place, replacing traditional methods.
Practical Applications of AI in Agriculture
As I promised multiple times so far, I’m going to take you through the application of AI and machine learning in the agricultural sector. This is the section so pay attention.
Monitoring the Soil and Crops
Monitoring is arguably the most important part of any agricultural venture. Farmers need to look out for nutrients in the soil for the seeds to grow to their full potential. Also, the growing crops need consistent monitoring for insects and diseases.
Soil health is arguably more important in this context as proper levels of macro and micronutrients ensure higher crop yield. But it’s not using the quantity of yield we’re interested in. High-quality soil also ensures healthier crops.
Before the age of AI, all of these were manual processes. Back in the day, an experienced farmer could determine the quality of soil just by the looks of it.
Fast forward a little bit, and soil analysis became a thing. Farmers used to dig into the soil to collect a sample and drop it off at designated labs. It then took a few working days for the lab professionals to give the report back.
Needless to say, the introduction of soil analysis improved crop health dramatically. It’s simply because no matter how experienced a farmer is, he/she can’t determine the nutrient levels in the soil just by looking at it.
But testing soil health still remained a time-consuming process. If the queue is long enough, farmers may even miss the harvest season entirely waiting for the reports to come back.
Fast forward to the age of AI in agriculture, farmers can now use drones to capture aerial image data to train computer vision.
In case you’re not aware, computer vision is a branch of modern artificial intelligence designed to extract meaningful data from visual inputs. These inputs can be image data and video data.
Believe it or not, this has proved to be a sustainable model for smart farming where farmers get to track crop health, make yield predictions, and detect plant diseases without actually roaming the fields!
Tracking the Crop Maturity
In agricultural terms, “maturity” refers to the stage of a crop’s life when it’s ready for harvesting. Needless to say, it’s a very labor-intensive process where you need to observe every single plant closely.
Researchers have found success in this by applying artificial intelligence. They trained AI models with over three years of image data for different stages of crop life. In this case, the crop was wheat and its head growth.
The researchers namely created a “two-step coarse-to-fine wheat ear detection mechanism”. The project was a success as the trained AI model managed to accurately predict and identify different stages of wheat growth.
For other crop types like Tomato or other yields that grow above ground, computer vision technology comes into play again. Researchers trained another model to observe the color and texture of tomatoes at different stages of the farming process.
The model managed to identify maturity at the right time based on the inputs. Apparently, the success rate of this project is a whopping 99.31%!
Although it’s not clear exactly how they conducted the test, we imagine they deployed agricultural robots to stroll through the fields capturing thousands of images for the duration of the harvest.
Computer Vision Can Also Do Soil Analysis
We’ve circled back to where it all started. The soil health. Up until now, soil analysis at labs has been the best method for testing it. But when researchers applied computer vision for soil monitoring, they got amazing results!
Apparently, the machine learning algorithm in place can successfully detect soil organic matter (SOM) as closely as lab tests!
The test was done by collecting image data using inexpensive microscopes. Then they used the images to train the artificial intelligence model to match the soil color and texture with reports from the lab tests.
Of course, the results are not 100% there just yet. But it’s at a stage where using AI in agriculture can result in major cost savings by eliminating lab tests. It can also save a lot of time, time farmers have to waste waiting for the test results to come back.
Insect and Plant Disease Detection
Plant diseases are no less than a terror to farmers. Just like one bad apple can ruin an entire basket, one infected plant can ruin the entire harvest.
Agricultural fields have recently started experimenting with plant disease detection by adopting AI. It blends a lot of emerging technologies together to get the best results. The reason is that diseases in plants are very unpredictable and the dataset of diseases is not nearly as big as other datasets like growth stages or maturity signals.
Hence, researchers needed to bring deep learning into the mix. Deep learning is an advanced stage of artificial intelligence technologies that uses neural networks as well as typical machine learning.
The AI model used to detect diseases used image classification, image segmentation, detection, and data from multiple other models to monitor crop health very closely.
Diagnosing Disease Severity is Also Important
Identifying the disease is not the end of smart farming. To have actionable steps, we also need to know how severe the infection is. Using the wrong dosage of any chemical on plants can do more harm than good.
Can you guess what crop researchers used to train the AI that can predict disease severity?
To be more precise, the apple black rot disease. The study involved a series of Deep Convolutional Neural Networks based on the four stages of severity identified by botanists.
Of course, computer vision was used for this image-based training as well. The study concluded with a jaw-dropping 90.4% success in detecting the severity.
Generally speaking, we seem to be successful with special-purpose AI the most. This means separate AI models need to be trained to detect the level of infection in different crops.
But there is good news. The YOLO v3 project has seen success in detecting a multitude of diseases commonly seen on tomato plants. The detection time for this technology is a mere 20.39 ms, faster than we can even look at an infected tomato, let alone identify what’s wrong with it.
Plant diseases are not the only concern for farmers. There are different types of insects and bugs that can ruin a year’s harvest overnight! As a preventative measure, most farmers use pesticides on their crops. However, inaccurate application can have an impact not only on the quality of the harvest but also on the safety of the crop!
An innovative study seems to have found a way to not only identify what kind of bugs you have on a plant but also how many! You can’t possibly imagine counting the bugs with traditional methods.
Knowing at least an approximate number of bugs terrorizing your fields has a lot of significance in the agriculture sector. When you know how much pesticide it needs to kill one bug and how many there are, you can calculate the intensity of pest control you need to apply.
This has two benefits. One, you’re killing all the bugs and insects. Two, you’re not poisoning the crop by over-applying.
The study started as the researchers set up sticky traps to capture different kinds of flying insects. The purpose is obviously to take high-quality pictures. The pictures are then fed to computer vision for analysis.
They also used other known methods like the YOLO object detection and Support Vector Machines (SVM) for classification. The model ended up successfully detecting bees, moths, mosquitos, flies, fruit flies, and chafers.
The detection accuracy for the model is 90.18% while the counting accuracy came in about 92.5%. These are odds I’ll take over manual inspection any time to optimize yields.
Crop Health is Not the Only Thing (Animal Husbandry)
So far, we’ve been focusing mostly on plant-based agriculture. But you’d be glad to know that many of the principles can be carried over to livestock monitoring, known as animal husbandry.
There’s no way to deny that livestock is a major pillar of our society as well as agricultural businesses. It needs just as much attention as plant-based crops.
Artificial intelligence and machine learning can do wonders for this segment as well. From counting animals to detecting diseases to monitoring growth, many of the concepts we’ve discussed so far can be integrated into livestock.
Arguably, using AI technologies for livestock is easier than for crops. It simply comes down to size and characteristics. If computer vision can detect flying insects, it can surely detect a chicken or a cow.
Most importantly, it’s easy to identify uncommon behavior in livestock compared to plants.
For example, if you have a pregnant cow in the sheds and it’s due anytime, you’d want to be present during the birth. Instead of spending time under duress only to wake up to a baby cattle in the morning, you may use AI monitors to send you notifications.
Something similar can be said for other activities like feeding the animals. Instead of visiting the sheds every few hours, you can stock up and monitor remotely.
CattleEye is an ambitious project working with livestock in the field of artificial intelligence. It uses a visual monitoring system with defined points on cattle to monitor growth and behavior.
The best part of this system is that there is no need for consistent monitoring. If the machine vision detects anything unusual, it can send notifications right away to the responsible party.
V7 Labs is another major player in this space, successfully creating a model to monitor chickens. It can even detect what the chickens are doing in real-time and color-code them for the operator.
Spray Where It Matters
The most cost-effective way of applying pesticides for pest control or fertilizers for agricultural produce is spraying. If you do it with a crop duster, for example, you can cover acres of land within minutes. For agricultural businesses, manual spraying is not even an option.
But what if it was possible to provide farmers with an even better way of spraying? Of course, I’m talking about the use of AI and machine learning. You already know that we can use computer vision to identify most conditions of crops.
From there, it’s as simple as mapping the drones (UAV) to spray targeted plants instead of all at once. Sure, this can take longer than usual but it’s worth it because individual plants are getting the care it needs.
Most importantly, you eliminate the risk of contaminating healthy crops or water sources by using a mass spraying mechanism like crop dusters. UAV drones not only use real-time data to identify the target area but also quantify the application.
Let’s say two plants side by side need a different dose of pesticide based on their infection severity. The intelligent spraying method will determine how much to spray on both plants individually, taking out the tedious human work from the equation.
The success of a harvest often depends on how quickly farmers can get the weeds out of the field. All of the agriculture challenges we’ve overcome so far have been in the form of monitoring.
But what if I told you AI engineers have made robots that can do the weeding for you? Of course, these are computer vision robots we’re talking about that can effectively identify weeds among a field of similar-looking plants.
It works so well simply because the method of identifying a weed is no different than identifying a bug or a disease. The AI behind the robots are trained with images of the weeds and programmed to take them out.
There is another great perk to this approach. If you use robots to get the weed out, you no longer need to spray herbicides to kill them. Many of the herbicides in the market today are non-biodegradable, meaning they’re harmful to the environment as well.
The Beginnings of Precision Agriculture
Also known as precision farming, this concept is based on the idea of “observing, measuring, and responding to inter and intra-field variability in crops”. In other words, precision agriculture is an advanced branch of information technology (IT) that ensures the production of the right crops at the right time, in the right type of soil.
Precision agriculture is primarily dependent on artificial intelligence. Specialized equipment like robots and drones (UAVs) are used to gather data on the field.
The AI models in place then do predictive analytics using machine learning to optimize yields. Yield mapping is an integral part of precision agriculture that maps out the harvesting process.
Agricultural robotics has come a long way in the past decade. Paired with the rapid growth of artificial intelligence, it’s possible to automate the harvesting process to save time. And most importantly, save money on labor.
Imagine a robot that has a high-definition camera and a mechanical arm equipped with whatever is needed for harvesting a particular crop.
The robot goes out on the field, scanning all the plants. We already know that there are computer vision models that can detect crop maturity. So, the robot will keep harvesting all the mature crops without any human assistance.
Grading and Sorting
Not every plant is created equal by mother nature. Even though the crop is mature, you can expect significant quality differences. Needless to say, the prices also depend on the quality of the harvest.
That’s where grading and sorting comes into play. Using a similar computer vision model to collect data, researchers have already managed to determine the quality of crops.
This enables farmers to streamline their business processes using fewer resources. Instead of spending hours grading through the crops, they can delegate the work to AI.
Companies that Are Making an Impact
All of the applications I discussed in this post are tested by researchers. But they can’t implement the solutions in the field. That’s where innovative startups come into play.
Let me list some of the popular startups making an impact on the industry.
- AGEYE Technologies
A US-based company developing intelligent machine solutions to improve the quality as well as the profitability of farming. The products are aimed directly at the farmers who can deploy readymade solutions.
At the time of writing, the solutions by AGEYE Technologies specialize in plant stress monitoring and disease detection.
- HelioPas AI
Drought is not one of the sudden natural disasters like floods. It’s a result of long-term supply shortages and a lack of rain. HelioPas AI has developed a solution to track the intensity of droughts.
Factors the average rainfall, level of water on the surface, and groundwater recharge are used to train the model. The German company is also innovating smart irrigation systems for farmers to maintain optimum water levels.
Based in Belarus, OneSoil is a solution offering satellite imaging using computer vision to farmers. It extracts the data from the Copernicus program. Farmers can derive important data like when and how to fertilize the crops based on the analysis provided by OneSoil.
The solutions can detect the status of the clouds covering the fields, boundaries, and even soil quality.
Remember the AI robots we’ve been talking about? Well, Root AI is one of the pioneers in agricultural robotics. One major difference between Root AI and other startups in the field is that Root AI is focusing more on indoor farming.
In this day and age, indoor farming makes more sense due to the changing climatic factors. Over time, more AI startups will surely start to develop solutions for indoor farming rather than outdoor.
The sad reality is of the AI revolution in agriculture is limited to mostly rich countries. Countries in the West like the US, Canada, and many European countries benefit more compared to the others.
An Indian startup by the name of Wolkus has embarked on a journey to change it. It’s developing affordable AI solutions for farmers who can’t afford mainstream tech.
The platform is called Fasal and it mostly uses sensors to gather data and insights in a horticulture setting. Apart from providing data, it also guides the farmers on best practices
Challenges of AI in Agriculture
Nothing is without challenges in the world of technology, especially such advanced technology as artificial intelligence. Adopting AI for precision farming has a set of challenges to overcome as well.
Let me illustrate.
Data is Scarce
Sure, there is an abundance of data across the agriculture sector. But high-quality data eligible to feed into AI models is still scarce. As you’d know, machine learning without proper data is like showering without soap.
Machine learning algorithm in the agricultural sector requires big data like weather conditions for decades, sensor data from the field, soil analysis report, and so on.
A big problem is the relevance of the data. Scientists are always experimenting with genome sequencing, resulting in various hybrid seed choices these days. This makes a lot of the data outdated.
Also, climatic factors are a major component of food security for the global population. Dramatically different weather conditions in different regions make it hard for adopting AI on a global scale.
Whether you want to do soil monitoring, sow seeds, or harvest produce with AI, you need to deploy the equipment. In the current state of artificial intelligence technology, it’s very expensive to deploy and maintain.
Let’s be honest here. Most farmers are not in a position to invest hundreds of thousands in agricultural robotics or drones. Internet connectivity is another barrier because it’s not easy to ensure reliable internet out in the fields.
There is no direct solution to this challenge. Thankfully, large-scale agricultural businesses are investing a lot in making intelligent machines to reduce the dependency on natural resources. We can expect this to scale in the near future as big data becomes more available and affordable.
AI and machine learning is one of the latest technologies in our world. It can not only benefit the agricultural production of conglomerates but also small families.
The issue here is that families in remote farms are more used to older principles than their predecessors have taught them. They may also not feel the need to upgrade their business processes as they’re happy with the yields.
This makes it harder for AI to penetrate the individual farmers’ space and increase the compound annual growth rate.
Pretty much all countries around the world want to be on top of their food demand. In many of them, the economic sector is also dependent on agriculture, especially in many Asian countries.
For this reason, governments are very strict with regulations that make the data collection process harder for researchers. Also, involved parties don’t want to experiment with new technology when they believe they already have a proven system in place.
What is the Future of Artificial Intelligence in Agriculture
From the contents of this post, it’s clear that global food production is heading toward a new era powered by AI and machine learning. But what does it mean for end-users like farmers and consumers?
For now, it’s the scientists, researchers, and big agriculture businesses that are the major players in AI adoption. But there’s no doubt that farmers will play a critical role in implementing artificial intelligence in agriculture.
No, they won’t become AI engineers themselves. But they’ll be the ones operating modern irrigation systems, smart tractors, harvesting robots, and modern inventory management systems.
Computer vision will perhaps be the cornerstone of the AI revolution in agriculture as most of the operations can be done with visual data. As time elapses, we can expect AI technologies to become more affordable and user-friendly.
Most importantly, this will improve the sustainability of our environment by streamlining the production process. It’s like killing 2 birds with 1 stone. We’ll have better food security as well as environmental security in the long run.
How AI is used in agriculture?
AI has the potential to disrupt every aspect of agriculture with the help of machine learning and big data. From soil analysis to sowing seeds to monitoring growth to harvesting, every step of the farming process can be automated and optimized for the best yields with AI.
How many farmers use AI?
Well, there is no objective answer to this question. The number is changing every day. It’s easier for us to look at the Ai in agriculture industry rather than the number of users. Experts predict that by 2030, the market will reach valuable of $7.43!
How AI is transforming agriculture?
The biggest impact AI has on agriculture has to do with the quality, quantity, and accuracy of the farming process. By eliminating guesswork and inefficiently from all the steps, AI can help us advance to a more sustainable future with strong food security.
What are the disadvantages of artificial intelligence in agriculture?
The biggest disadvantage is associated with cybersecurity. As you already know, everything is dependent on data and cloud computing. This means all the operations are connected to the internet. As the practices get more traction, they may also attract hackers.