Global spending on smart, connected agricultural technologies and systems, including AI and machine learning, is projected to triple in revenue by 2025, reaching $15.3 billion, according to BI Intelligence Research.
Spending on AI technologies and solutions alone in Agriculture is predicted to grow from $1 billion in 2020 to $4 billion in 2026, attaining a Compound Annual Growth Rate (CAGR) of 25.5%, according to Markets&Markets.
IoT-enabled Agricultural (IoTAg) monitoring is smart, connected agriculture's fastest-growing technology segment projected to reach $4.5 billion by 2025, according to PwC.
AI, machine learning (ML) and the IoT sensors that provide real-time data for algorithms increase agricultural efficiencies, improve crop yields and reduce food production costs. According to the United Nations' prediction data on population and hunger, the world's population will increase by 2 billion people by 2050, requiring a 60% increase in food productivity to feed them.
In the U.S. alone, growing, processing and distributing food is a $1.7 trillion business, according to the U.S. Department of Agriculture's Economic Research Service. AI and ML are already showing the potential to help close the gap in anticipated food needs for an additional 2 billion people worldwide by 2050.
Agriculture Is One Of The Most Fertile Industries There Are For AI & Machine Learning
Imagine having at least 40 essential processes to keep track of, excel at and monitor at the same time across a large farming area often measured in the hundreds of acres. Gaining insight into how weather, seasonal sunlight, migratory patterns of animals, birds, insects, use of specialized fertilizers, insecticides by crop, planting cycles and irrigation cycles all affect yield is a perfect problem for machine learning.
How financially successful a crop cycle has never been more dependent on excellent data. That's why farmers, co-ops and agricultural development companies are doubling down on data-centric approaches and expanding the scope and scale of how they use AI and machine learning to improve agricultural yields and quality. The following are ten ways AI has the potential to improve agriculture in 2021:
1. Using AI and machine learning-based surveillance systems to monitor every crop field's real-time video feeds identifies animal or human breaches, sending an alert immediately. AI and machine learning reduce domestic and wild animals' potential to accidentally destroy crops or experience a break-in or burglary at a remote farm location. Given the rapid advances in video analytics fueled by AI and machine learning algorithms, everyone involved in farming can protect their fields and buildings' perimeters. AI and machine learning video surveillance systems scale just as easily for a large-scale agricultural operation as for an individual farm. Machine-learning based surveillance systems can be programmed or trained over time to identify employees versus vehicles. Twenty20 Solutions is a leader in the field of AI and machine learning-based surveillance and has proven effective in securing remote facilities, optimizing crops and deterring trespassers by using machine learning to identify employees who work onsite. An example of Twenty20 Solutions' real-time monitoring is shown here:
2. AI and machine learning improve crop yield prediction through real-time sensor data and visual analytics data from drones. The amount of data being captured by smart sensors and drones providing real-time video streaming provides agricultural experts with entirely new data sets they've never had access to before. It's now possible to combine in-ground sensor data of moisture, fertilizer and natural nutrient levels to analyze growth patterns for each crop over time. Machine learning is the perfect technology to combine massive data sets and provide constraint-based advice for optimizing crop yields. The following is an example of how AI, machine learning, in-ground sensors, infrared imagery and real-time video analytics all combine to provide farmers with new insights into how they can improve crop health and yields:
3. Yield mapping is an agricultural technique that relies on supervised machine learning algorithms to find patterns in large-scale data sets and understand the orthogonality of them in real-time – all of which is invaluable for crop planning. It’s possible to know the potential yield rates of a given field before a vegetation cycle is ever started. Using a combination of machine learning techniques to analyze 3D mapping, social condition data from sensors and drone-based data of soil color, agricultural specialists can now predict the potential soil yields for a given crop. A series of flights are completed to get the most accurate data set possible. The following graphic shows the result of a yield mapping analysis:
4. The UN, international agencies and large-scale agricultural operations are pioneering drone data combined with in-ground sensors to improve pest management. Using infrared camera data from drones combined with sensors on fields that can monitor plants' relative health levels, agricultural teams using AI can predict and identify pest infestations before they occur. An example of this is how the UN is using working in conjunction with PwC to evaluate data palm orchards in Asia for potential pest infestations, as is shown in the image below:
5. Today, there’s a shortage of agricultural workers, making AI and machine learning-based smart tractors, agribots and robotics a viable option for many remote agricultural operations that struggle to find workers. Large-scale agricultural businesses can’t find enough employees and turn to robotics for hundreds of acres of crops while also providing an element of security around the perimeter of remote locations. Programming self-propelled robotics machinery to distribute fertilizer on each row of crops helps keep operating costs down and further improve field yields. Agriculture robots’ sophistication has grown quickly, an example of which is shown in the dashboard of the VineScout robot in use.
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6. Improving the track-and-traceability of agricultural supply chains by removing roadblocks to getting fresher, safer crops to market is a must-have today. The pandemic accelerated track-and-traceability adoption across all agricultural supply chains in 2020 and will continue to drive its adoption this year. A well-managed track-and-trace system helps reduce inventory shrinkage by providing greater visibility and control across supply chains. A state-of-the-art track-and-trace system can differentiate between inbound shipments' batch, lot and container level assignments of materials. Most advanced track-and-trace systems rely on advanced sensors to gain greater knowledge of each shipment's condition.
RFID and IoT sensors are now becoming more commonplace across manufacturing. Walmart ran a pilot to see how RFID could streamline a distribution center's track-and-trace performance and improved efficiency by 16 times over manual methods.
7. Optimize the right mix of biodegradable pesticides and limiting their application to only the field areas that need treatment to reduce costs while increasing yields is one of the most common uses of AI and machine learning in agriculture today. By using intelligent sensors combined with visual data streams from drones, agricultural AI applications can now detect a planting area's most infected areas. Using supervised machine learning algorithms, they can then define the optimal mix of pesticides to reduce pests' threat spreading further and infecting healthy crops.
8. Price forecasting for crops based on yield rates that help predict total volumes produced are invaluable in defining pricing strategies for a given crop. Understanding yield rates and quality levels of crops help agricultural firms, co-ops and farmers better negotiate for the best possible price for their harvests. Considering the total demand for a given crop to determine if the price elasticity curve for a given crop is inelastic, unitary, or highly elastic defines what the pricing strategy will be. Knowing this data alone saves agricultural businesses millions of dollars a year in lost revenue.
9. Finding irrigation leaks, optimizing irrigation systems and measuring how effective frequent crop irrigation improves yield rates are all areas AI contributes to improving farming efficiencies. Water is the scarcest resource in many parts of North America, especially in communities that rely most on agriculture as their core business. Being efficient in using it can mean the difference between a farm or agricultural operation staying profitable or not. Linear programming is often used to calculate the optimal amount of water a given field or crop will need to reach an acceptable yield level. Supervised machine learning algorithms are ideal for ensuring fields and crops get enough water to optimize yields without wasting any in the process.
10. Monitoring livestock’s health, including vital signs, daily activity levels and food intake, ensures their health is one of the fastest-growing aspects of AI and machine learning in agriculture. Understanding how every type of livestock reacts to diet and boarding conditions is invaluable in understanding how they can be best treated for the long-term. Using AI and machine learning to understand what keeps daily cows contended and happy, producing more milk is essential. For many farms who rely on cows and livestock, this area opens up entirely new insights into how farms can be more profitable.
