The United Nations estimates that by 2050, the global population will increase to more than 9.7 billion people, and the global food shortage may become more serious. At that time, many hungry people need to feed. Compared with the massive increase in population, the area of arable land will only increase by 4%. Therefore, mankind urgently needs to launch the next “agricultural green revolution” to find the next growth point in food production in addition to the existing agricultural production mode.
The internal and external dilemma of agriculture
The United Nations Intergovernmental Panel on Climate Change (IPCC) made it clear in its latest report to guide governments that changes must be made to land management and agricultural production methods in the world to curb global warming. During the period 2007-2016, agriculture, forestry and other land use activities accounted for 23% of the total net man-made greenhouse gas emissions. When the pre-production and post-production activities are also included, this proportion rises to 37%.
Climate warming will lead to more heat waves, droughts and heavy rainfall, as well as land degradation and desertification. Extreme weather events become more frequent, and the global food supply chain will suffer more damage, which will lead to higher food prices and increase the risk of food insecurity and hunger.
In addition, agriculture is a dynamic system full of variables. Whether it is climate, soil, seeds, or nutrients, it is difficult to accurately monitor in traditional agricultural production, and it usually depends on decades of experience accumulated by agricultural experts to make a correct judgment. And AI can not only learn based on existing information, but also detect and analyze it based on machine learning, and promote the refined management, detection, and optimization of every variable and every production process.
With the introduction of AI technology to agriculture, farmers can use sensors to monitor information to extract characteristic laws, and use simulators that integrate expert experience to simulate, explore, and optimize to form a set of real-time and accurate decision-making programs. It can be said that AI is The key to improving land use efficiency.
5G + Internet of Things + new agricultural formats will usher in an explosion
With the arrival of 5G and the explosion of AIoT devices, the efficiency of land use is likely to reach new heights with the assistance of new technologies. Through the in-depth integration of various elements of science and technology and agriculture, it can improve manufacturing efficiency, improve product quality, reduce product costs and resource consumption, and promote traditional agriculture to a new stage of intelligent agriculture. The combination of technology and agriculture has at least the following aspects that have great potential to be tapped.
1. AI selection to increase production
Now Alibaba Cloud Agricultural Brain has realized AI image collection and even DNA sequencing seed selection technology. Compared with traditional agriculture, AI helps us select seeds with a higher survival rate and better quality.
This is a transition from a physical farm to a digital farm. Using big data, AI and predictive analysis to provide farmers with solutions to daily farm problems, such as precision agronomy, crop management, risk management, etc. By connecting agricultural services and technology provision on a single platform, AI provides insights about the farmland field, providing farmers and agronomy consultants with “deep” information about farm operations.
It can assist farmers to adjust their farming plans and even change crops; reconsider the fertilization plan to achieve the goal of improving the fertility of the cultivated land; keep abreast of weather changes and forecasts, and review the selection of seeds according to the analysis of soil samples; moisture and nitrogen content Adjust the time course of spring plowing; monitor the growth of crops and pests, optimize land use, and make yield forecasts.
This scenario generally requires AI to analyze and predict farmland IoT data and remote sensing imaging or farmland hyperspectral imaging. Farmers can establish more accurate and effective crop planting models to support current season decision-making and increase profit potential. Not affected by data complexity or variability caused by climate patterns.
2. Weather tracking and forecasting
Weather tracking and forecasting is an important application of AI in agriculture because it helps to collect the latest information on popular weather conditions, such as temperature, rain, wind speed and direction, and solar radiation. According to a study, 90% of crop losses are caused by weather events, and 25% of these losses can be prevented by using predictive weather models.
Use various devices such as handheld instruments, sensors, GPS and field weather stations for weather tracking applications to obtain real-time information. Real-time information helps farmers make various decisions, such as planting crops in time and analyzing crops before harvest. The weather tracking and forecasting application market is expected to grow at the highest compound annual growth rate of 25% within 5 years.
3. Establish animal files and improve breeding efficiency
At present, based on animal face recognition technology, it is possible to establish an exclusive ID for each poultry. The animal’s weight, body temperature, food intake and other indicators will be collected through the Internet of Things technology, and analyzed in the cloud, and even through the calls of pigs To judge whether it has disease, or even whether it is full. This can provide us with meat with better meat quality and more balanced nutrition.
Different from crops, the individual economic value of livestock and poultry is higher. Once affected by disease, the loss will be greater and the impact will be farther. In the breeding process, even experienced breeders can’t know every animal well. The emergence of AI technology can solve this problem.
Collect and process data through the principles of machine vision and supporting IoT equipment to understand the health of each animal in an intuitive way, or machine learning can use audio data analysis to correctly identify diseases that are not detectable by other methods, or use AI Detect milk quality to monitor the health of the herd and improve milk quality. Correctly diagnosing livestock diseases and treating them as early as possible before the loss occurs can eliminate the loss caused by the disease, and the loss can be up to 2 billion US dollars.
In addition to focusing on the main AI financing plans in the above scenarios, AI is also used in scenarios such as autonomous driving of agricultural machinery, crop recognition, and academic research.
4. IoT (Internet of Things) scientific irrigation and fertilization
Environmental factors such as soil, temperature and humidity play a great role in agriculture. Through the analysis of the Internet of Things and even technology, farmers can quickly and accurately monitor changes in farm soil, environment and other factors, and use feedback technology to correct environmental parameters. Of course, there are also plans to use drones to scan the entire farm and evaluate the level of irrigation and nitrogen in the soil, and use this to scientifically use water and fertilize, minimize fertilizer use, and achieve a win-win situation for economic benefits and environmental protection.
In addition, indoor agriculture has become the trend of agricultural development in recent years, and there are more and more financial investments in indoor agriculture. Its main advantages can be roughly summarized into three main items: water consumption, land area, and chemical safety. However, the development of indoor agriculture still faces many challenges, so indoor agriculture is more intelligent while being automated.
Similar to improving field management efficiency through AI, indoor agricultural management also requires sensors to collect a large amount of physical data. AI constantly learns and predicts how to produce the best quality products, controls light, regulates water and nutrients, and takes camera images of each plant. To monitor its health. In the end, you only need to press a certain crop “button”, and the indoor farm will automatically configure the most suitable climate conditions for it.
5.AI solves the problem of weeds and pests
AI can distinguish between healthy and diseased leaves, and then integrates with Iot robots to remove pests and weeds. This technology can reduce the use of chemicals by up to 80%, and the money spent on herbicides can be reduced by 90%. Weed control is very important for farmers, because there are currently about 250 varieties that are resistant to modern herbicides, and the growth of weeds on soybeans and corn crops alone causes more than 40 billion US dollars in losses each year. It can be said that the future development potential of AI to solve weeds and pests is still very huge.
In such scenarios, the combination of AI and robots is the most. Weeds are the most important part of farmland management, and modern agriculture relies heavily on chemical herbicides. The consequences are large amounts of pesticide residues, additional cost input and herbicide-resistant weeds. Using AI image recognition technology, we have developed a smart pesticide sprayer or weeding robot that can identify weeds, which can accurately determine weeds and crops before spraying herbicides. Compared with the traditional method of spraying pesticides in the past, it not only reduces the amount of pesticides by 90%, reduces costs but also improves efficiency, and provides more appropriate protection for the environment and crops.
6.AI teaches you the correct harvest posture
Determining the best harvest time has always been a highly technical task. Only the most experienced farmers can give a more appropriate time point. With the advancement of technology, the fruits are currently illuminated by incandescent lamps and UVA lamps. Image analysis to determine the maturity of the technology has also been born, which means that farmers can choose to pick only the most mature fruits or vegetables, and leave other immature fruits for a while.
The harvest season can be said to be the “Super Bowl” in agriculture. It often constitutes the highest cost and requires the most labor, while the farm may earn or lose annual profits. The picking of special crops, especially berries, is mostly done by manpower, and growers are suffering from labor shortages. Picking robots can help increase productivity and reduce crop losses, while also providing solutions to labor shortages. At this stage, automatic picking machines and robots have been able to identify and pick ripe apples, strawberries and tomatoes.
Between 2017 and 2018, the amount of funds in 83 financial investment transactions for robotic equipment technology increased by 53%, including weeding robots and robots that focus on food safety, and even robots that make octopus balls. It is estimated that by 2024, The scale of the agricultural robot industry will reach 5.7 billion US dollars.
It can be said that through the “technology + agriculture” solution, it will promote the comprehensive digital upgrade and transformation of agriculture, implement large-scale, industrialized, and intensive development, and increase the agricultural yield per mu to an unprecedented level. This will greatly improve the efficiency of agricultural production and the profitability of enterprises. For the agricultural industry with an output value of tens of trillions of yuan, an increase in efficiency of 5% to 10% will generate a value of several trillion yuan.
By 2025, North America is expected to occupy a major share of AI in the agricultural market, while the Asia-Pacific market is expected to grow at the highest compound annual growth rate during the forecast period. The presence of many agricultural technology providers such as IBM, Microsoft, John Deere in the region is driving the growth of the North American agricultural AI market.
The advent of the 5G and even 6G era has directly led to the increase in land utilization at a speed visible to the naked eye, and AI has also played a revolutionary role in all aspects of agricultural projects. For more developing countries and even backward countries, these breakthroughs are an important indicator of the liberation of manpower and the release of technology-“the more difficult the environment, the more breakthroughs from the land are needed”, perhaps the new agriculture is like a globalization The catalysts that have reduced the progress of human progress for at least decades.