PricewaterhouseCoopers pointed out that IoT-based agriculture (IoTAg) monitoring has become the fastest growing technology field in the field of connected smart agriculture, and the total market is expected to grow to US$4.5 billion by 2025.
- According to the forecast report released by BI Intelligence Research, by 2025, global expenditures in the field of connected smart agriculture technologies and systems (including artificial intelligence and machine learning) are expected to triple to reach 15.3 billion US dollars.
- According to data released by Markets&Markets, spending on AI technology and solutions in agriculture alone is expected to grow from US$1 billion in 2020 to US$4 billion in 2026, with a compound annual growth rate (CAGR) of 25.5%.
- PricewaterhouseCoopers pointed out that IoT-based agriculture (IoTAg) monitoring has become the fastest growing technology field in the field of connected smart agriculture, and the total market is expected to grow to US$4.5 billion by 2025.
AI, machine learning (ML), and IoT sensors can provide algorithms with rich real-time data to improve agricultural production efficiency, increase crop yields, and reduce food production costs. According to the UN's forecast data on population and hunger, by 2050, the global population will further increase by 2 billion, and agricultural productivity needs to increase by 60% to provide adequate food. According to data released by the Bureau of Economic Research of the U.S. Department of Agriculture, in the United States alone, the total market for planting, processing and food distribution businesses is as high as $1.7 trillion. By 2050, artificial intelligence and machine learning are likely to become the core of new technologies, helping us to calmly cope with the expected food demand brought by the 2 billion new population.
"Agriculture"-one of the most promising artificial intelligence and machine learning application scenarios
Imagine that in these large farming areas, which are usually hundreds of acres as the basic planning unit, there are at least 40 basic processes that need to be tracked, highlighted, and monitored simultaneously. In-depth analysis of weather changes, seasonal sun differences, grasping the migration patterns of birds and insects, understanding the needs of special fertilizers, choosing suitable pesticides for crops, monitoring the planting cycle and irrigation cycle, etc., are all for machine learning It is a major problem that is expected to be solved and of great practical significance. Today, crop production is increasingly dependent on excellent data collection and analysis capabilities. Because of this, farmers, cooperatives, and agricultural development companies have decided to further adopt a data-centric approach and continue to introduce AI and machine learning elements to improve agricultural yields and crop quality. Looking at 2021, the following ten methods are expected to promote further development of agriculture:
1. Use a monitoring system based on AI and machine learning to track the real-time video source of each crop field, thereby identifying violations of animals or humans and issuing an alarm immediately.
AI and machine learning can reduce the possibility of accidentally destroying crops by domestic or wild animals or breaking into farms in remote areas. With the rapid development of AI and machine learning algorithms in the field of video analysis, every participant in agricultural production can use this to protect their fields and agricultural facilities. AI and machine learning video surveillance systems can be easily expanded to adapt to large-scale agricultural operations, covering the entire farm. Over time, we can program or train a monitoring system based on machine learning to teach it to recognize people and vehicles. As a leader in the field of AI and machine learning monitoring systems, Twenty20 Solutions has proven that these technologies can effectively protect remote facilities, optimize crop production, and identify accidental intruders on the field through machine learning. The following figure shows an example of real-time monitoring by Twnty20 Solutions:
Figure: Relying on AI and machine learning algorithms to identify people and vehicles can help global agricultural companies to simplify remote operations.
2. AI and machine learning-improve crop yield prediction through real-time sensor data and visual analysis data of drones.
With real-time video streams provided by smart sensors and data captured by drones, agricultural experts have access to new data sets that they have never had access to before. Now, researchers can combine sensor data such as moisture, fertilizer, and natural nutrient levels to analyze the changing growth patterns of each crop over time. Machine learning is responsible for integrating a large number of data sets and ingesting recommendations based on constraints to optimize crop yields. The following figure shows an example of a scenario where AI, machine learning, on-site sensors, infrared images, and real-time video analysis technology are used in combination. This allows farmers to gain new insights on improving crop health and yield levels:
Figure: Facts have proved that drones have become an extremely reliable platform that can collect data on the impact of specific fertilizers, irrigation methods and pesticide treatment methods on actual crop yields.
3. Yield mapping is an agricultural technology that uses supervised machine learning algorithms to find patterns in large-scale data sets and understand the orthogonality between different patterns in real time, thereby bringing unmeasured value to crop production planning.
Today, we have been able to roughly judge the potential yield of a particular field before the planting cycle begins. By combining machine learning technology with 3D mapping, sensor data, and drone-based field color data, agricultural experts can quickly predict the yield of specific crops under potential soil conditions. These data sets captured by drones are accurate and reliable. The following figure shows the results of the output mapping analysis:
Figure: With the support of supervised and unsupervised machine learning algorithms, agricultural experts can determine how to maximize field yield.
4. The United Nations, various international agencies and large-scale agricultural projects have combined drone data with on-site sensors to improve pest management capabilities.
By combining the drone’s thermal imaging camera data with sensors that can monitor the relative health of plants, the agricultural management team can predict and identify pests before they occur with the help of AI. Currently, the United Nations is cooperating with PricewaterhouseCoopers to assess potential pest infestation problems in palm plantations in Asia, as shown in the following figure:
Figure: The United Nations combines on-site sensors and drone data to tune machine learning algorithms and help farmers obtain higher yields from plantations.
5. Nowadays, there is a serious shortage of agricultural workers, making smart tractors, agricultural robots and other smart machinery based on AI and machine learning the first choice for agricultural planting in remote areas.
At present, large agricultural companies cannot find enough employees and can only rely on robot technology to collect crops from hundreds of acres of land. This also brings positive impetus to the security situation in remote areas. By programming autonomous robotic devices, they can spread fertilizer for crops, thereby reducing operating costs and further increasing field yields. At present, the complexity of agricultural robots is rapidly increasing. The following figure shows the dashboard information of the VineScout robot in operation.
Figure: Facts have proved that agricultural robotics technology can quickly capture valuable data to tune AI and machine learning algorithms to further increase crop yields.
6. By removing a series of traditional obstacles, emerging technologies are expected to deliver fresher and safer crops to the market, while greatly improving the traceability of the agricultural supply chain.
The outbreak of the new crown epidemic in 2020 has accelerated the deployment of tracking and traceability functions in the agricultural supply chain, and this trend will remain stable in 2021. This well-managed tracking system can provide greater visibility and comprehensively improve the overall control of the supply chain, thereby effectively reducing inventory. The latest tracking system can even distinguish the batches of incoming goods, the belonging items and realize the fine-grained records at the container level. In addition, with the rapid popularity of RFID and IoT sensors in the entire manufacturing process, most advanced tracking systems now begin to rely on advanced sensors to obtain more status information about each batch of goods. Wal-Mart is promoting a pilot project to study how to use RFID to simplify the tracking performance of goods in distribution centers and increase the efficiency to 16 times that of manual operations.
7. Use the combination of AI and machine learning to optimize the correct mixing ratio of biodegradable pesticides and use them only when necessary, thereby reducing operating costs and increasing unit field yield.
By combining smart sensors with drone vision data streams, agricultural AI applications can now detect the most severe pests and diseases in the planting area. Based on this, and using supervised machine learning algorithms, agricultural experts can determine the best combination of pesticides to effectively control the threat of pests, prevent their further spread and infect other healthy crops.
8. Determine the total output according to the crop yield rate, so as to formulate a reasonable and effective crop pricing strategy.
Accurately grasping the yield and quality level of crops will help agricultural enterprises, cooperatives and farmers to better formulate pricing strategies. Considering that the overall market demand for specific crops is basically constant, all parties can choose strategies such as fixed selling prices, unified selling prices and even flexible selling prices based on the crop's harvest. These data alone can eliminate millions of dollars in losses for agricultural companies every year.
9. AI can help farmers find leaks in the irrigation system, optimize system performance, and measure how to adjust irrigation frequency to increase crop yields.
In many parts of North America, water is one of the most scarce resources, and even directly determines the life direction of the entire community who earns a living from agriculture. Efficient use of water resources may be able to turn a farm back into profit and bring it back to life. Through linear programming, we can quickly calculate the optimal amount of water required for a specific field or crop to reach the desired yield level. Supervised machine learning algorithms can ensure that the fields and crops get enough water to optimize yields without excessively wasting this precious resource.
10. Monitoring and maintaining the health of livestock-including life weight, daily activity level, and food intake-has become a new application for AI and machine learning.
To ensure long-term good care of livestock, we must always understand the actual response of various types of livestock to the current diet and living conditions. Using AI and machine learning technology, agricultural experts can understand which factors determine the mood of dairy cows, and make appropriate adjustments to increase their milk production. For the livestock industry, which is dominated by cattle and other livestock, the intervention of emerging technologies has brought unprecedented new directions for ranches to open up new profit margins.