The AI-based worldwide smart agriculture market is predicted to grow from $13.8B in 2021 to $22B in 2025, attaining a Compound Annual Growth Rate (CAGR) of 9.8%, according to Markets&Markets.
According to Mordor Intelligence, The Artificial Intelligence (AI) market in agriculture reached $766.41M in 2020 and is predicted to reach $2.5B by 2026, attaining a CAGR of 21.52% over the forecast.
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.
IoT-enabled Agricultural (IoTAg) monitoring is smart, connected agriculture's fastest-growing technology segment projcted to reach $4.5 billion by 2025, according to PwC.
Combining real-time monitoring data with AI and machine learning techniques is delivering exponential gains in the agricultural industry. Starting with 24/7, always-on security of remote facilities and fields to provide greater precision of crop health, fertilizer, and pesticide effectiveness, combining real-time data, machine learning, and AI offers new insights and intelligence that define the current state and future direction of agriculture.
AI and machine learning prove to be strong catalysts driving the adoption of integrated agricultural technology (Agritech) stacks that automate every phase of the agriculture value chain. Sensors capture the real-time impacts of climate, crop, fertilizer, pesticide, and soil treatments all in real-time. Actuation technologies enable more precise use of fertilizers and pesticides, traceable through IoT sensors capable of picking up moisture and growth levels changes. Combining on-the-ground real-time process and plant data with data captured from drones surveying fields with thermal-sensing cameras, agricultural specialists improve yields and reduce costs. As each of the remote monitoring, AI, and machine learning technologies have a different clock speed or cadence they run at, they all must be orchestrated using a common framework. The collection of these technologies taken together creates an Agriculture 4.0 framework, an illustration of which is shown below:
The Current State Of AI And Machine Learning In Agriculture
Real-time data streams from remote video surveillance, sensors, thermal infrared cameras, and drones contain valuable insights into how agricultural operations can be made more secure and productive. Unlocking those insights often starts with video analytics, created to automate remote site monitoring and security surveillance. Industry-leading remote surveillance monitoring systems, including Twenty20 Solutions' cloud-based platform that provides real-time updates, alerts, and analysis via a web-enabled dashboard, are defining the future of AI in agriculture today. AI and machine learning algorithms analyze real-time data streams and protect the perimeters of remote agricultural sites, buildings, and equipment, then provide real-time alerts of any breach attempts or suspicious activity.
By capitalizing on the quickly growing variety, velocity, and volume of data, remote real-time monitoring is one of the primary growth engines driving AI and machine learning adoption in agriculture. Real-time data is used to train machine learning models over time to identify and provide alerts of anomalous events, including breaches, potential security, and safety threats. Agritech engineers are finding new ways to improve agriculture with AI and machine learning all the time. The following are examples of the current state of AI and machine learning adoption in agriculture:
Real-time remote site monitoring that combines video analytics, heat, thermal, and security sensors provide a 360-degree, always-on view of agricultural locations and assets with each potential breach or security threat assessed in real-time. Securing remote agricultural locations and the machinery they rely on to maximize crop yields and farm productivity need 24/7 security as these assets are critical to continued operations. AI and machine learning-based remote monitoring platforms can accept data from IoT, digital, thermal, and infrared cameras. Having teams on the ground is still essential, yet having the real-time, always-on monitoring providing a data stream to train models is invaluable.
AI and machine learning are optimizing how pesticides are actively used to treat farms with multiple kinds of crops, selecting the best and safest pesticide for a given crop at risk. Identifying pest-based threats to crop yields is possible thanks to advances in real-time monitoring, AI, and machine learning techniques. Before these technologies existing, farmers and agriculture professionals discovered too late that an entire crop season was under attack from pests. Real-time data feeds from in-ground sensors combined with the thermal imaging data from drones combine to provide an early-warning system for farmers so they can save their crops – and their growing seasons and their financial livelihoods at the same time.
Real-time monitoring of crops' health levels, including potential disease outbreaks using thermal infrared cameras and drone data analyzed using AI and machine learning techniques, drives higher crop and field yields. Machine learning algorithms excel at finding patterns that are not easily seen in the massive amount of data real-time remote monitoring technologies can produce. Combining real-time sensor monitoring of soil composition, nutrient, fertilizer levels, and moisture and temperature variables with aerial data from drones using thermal infrared cameras provides Agritech engineers with the insights they need to improve crop yields. Thermal infrared cameras can identify patterns in nutrient levels and identify potentially damaging diseases before they spread across a crop field.
Combining real-time video monitoring and thermal sensors on agricultural equipment and temperature-sensitive supplies, including water, propane is preventing costly accidents and emergencies. Monitoring remote agricultural locations with real-time video streams, analyzed using AI effectively identifies potentially dangerous conditions of supplies and systems providing them. Knowing the temperature, pressure levels, and fluid levels of each storage container and its contents combined with real-time video analytics data have provided enough early warning to stop accidents, overflows and overheating, and potentially fires from damaging remote agricultural sites.
Crop simulation modeling optimizes the best mix of fertilizers, crops, planting schedule, and regional conditions to achieve sustainability and yield levels. One of the more advanced uses of AI and machine learning is simulating crop lifecycles based on historical data. The goal of crop simulations is to achieve a high degree of accuracy in a smaller acreage lot then scale the results across hundreds of acres to gain greater scale and yield levels. They're an ideal use of AI and machine learning in agriculture. They use key input variables, including crop management information, weather, and soil data, to estimate crop productivity and have become powerful tools to link physiology and genetics. Benchmarks are used for evaluating how effectively the simulation scales from the pilot field to full production. The following figure explains how a typical simulation system works and its applications.
The answers agricultural professionals are looking for on improving crop yields while improving the security of remote facilities, machinery, and assets are available today from real-time monitoring data the widening number of sensors produce. Mining that data with AI and machine learning proves exceptionally accurate in securing remote site perimeter security, protecting remote facilities and assets, and learning new ways to improve crop yields. Agricultures' future is being predicted today by real-time monitoring and the rapid advances in AI and machine learning technologies used for interpreting and taking action on contextually rich, real-time data streams.
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