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How does artificial intelligence affect agriculture?
2022/1/5 14:15:40


How does artificial intelligence affect agriculture?


AI-affect--agriculture-1.jpgAgriculture is the foundation of the national economy and the top priority in economic and social development. Since the reform and opening up, the level of agricultural development in my country has been greatly improved, but at the same time it is also facing problems such as shortage of land resources, low degree of agricultural industrialization, severe quality and safety of agricultural products, and destruction of the agricultural ecological environment. How to steadily improve the level of agricultural development and achieve sustainable agricultural development while resources are scarce has become a major proposition facing my country's economic and social development.


In this situation, large-scale innovation and technological change will be an effective way to solve agricultural problems and promote the modernization of agriculture. At present, how to improve productivity through artificial intelligence technology has become a research and application hotspot in the field of agriculture.


No.1 Smart agriculture supported by technology

Traditional agricultural technology will cause water waste, excessive use of pesticides and other problems, not only high cost, low efficiency, product quality can not be effectively guaranteed, but also cause soil and environmental pollution. With the blessing of artificial intelligence technology, farmers will be able to achieve precise planting and reasonable water and fertilizer irrigation, thereby achieving low-consumption and high-efficiency agricultural production, and high-quality and high-yield agricultural products.


Provide scientific guidance. The use of artificial intelligence technology for analysis and evaluation can provide farmers with scientific guidance for pre-production preparations, realize soil composition and fertility analysis, irrigation water supply and demand analysis, seed quality identification and other functions, and scientifically perform production factors such as soil, water and seeds. Reasonable allocation can effectively guarantee the smooth development of follow-up agricultural production.


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Increase productivity. The use of artificial intelligence technology in the middle stage of agricultural production can help farmers plant crops more scientifically and manage farmland more reasonably, effectively increasing crop yields and agricultural production efficiency. Promote the transformation of agricultural production to mechanization, automation, and standardization, and accelerate the process of agricultural modernization.


Realize the intelligent sorting of agricultural products. The machine vision recognition technology is applied to the agricultural product sorting machinery, which can automatically identify, inspect and classify the appearance and quality of agricultural products. Its inspection and recognition rate is much higher than that of human vision. It has the characteristics of fast speed, large amount of information, and multiple functions. Complete multiple index tests.


No.2 The application status of artificial intelligence in agriculture

At present, artificial intelligence technology is becoming a strong driving force for changing agricultural production methods and promoting agricultural supply-side reforms, and is widely used in a variety of agricultural scenarios. For example, intelligent robots such as farming, sowing and picking, intelligent identification systems such as soil analysis, seed analysis, and disease and pest analysis, as well as intelligent wearable products for poultry and livestock, etc. The extensive use of these applications can effectively improve agricultural output and efficiency, while reducing the use of pesticides and fertilizers.


Analysis of soil composition and fertility. The analysis of soil composition and fertility is one of the most important tasks in the pre-production stage of agriculture, and it is also an important prerequisite for realizing quantitative fertilization, selection of suitable crops, and economic benefit analysis. Using non-invasive ground penetrating radar imaging technology to detect the soil, and then using artificial intelligence technology to analyze the soil conditions, an association model can be established between soil characteristics and crop varieties suitable for planting.


For example, IntelinAir has developed an unmanned aerial vehicle that uses similar MRI technology to take photos of the soil. Through intelligent analysis, the soil fertility can be determined and the crops suitable for planting can be accurately judged.


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Analysis of irrigation water supply and demand. The intelligent irrigation control system based on artificial intelligence technology integrates expert system technology, automatic control technology, communication technology, sensor technology and other high-tech. It can monitor soil moisture in real time. According to the detected climate index and local hydrometeorological observation data, Analyze the supply and demand of irrigation water and select the best irrigation planning strategy.


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Seed quality identification. As one of the most important means of production in agricultural production, the quality of seeds is directly related to crop yields and production benefits. Using image analysis technology and neural networks and other non-destructive methods to detect the types, purity and safety of crop seeds can effectively control and improve the quality of agricultural products.


Agricultural expert system. The agricultural expert system is an intelligent computer program system with a considerable amount of expert-level knowledge and experience in the agricultural field, which can simulate the thinking of agricultural experts and solve problems in the agricultural field. The agricultural expert system can analyze data in the field of agricultural production and obtain timely solutions to problems that may be encountered in various stages of agricultural production.


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Animal and plant health monitoring. For example, Connecterra is a Dutch agricultural technology company that mainly develops and produces electronic wearable devices for dairy cows. These devices have multiple built-in sensors, the supporting analysis software incorporates machine learning technology, and the hardware and software cooperate to monitor the health of livestock in real time. By learning the behavior patterns of cows through wearable sensors, dairy farmers can also notice possible problems earlier, such as lameness or indigestion of cows, and get advice. With the help of this information, the output of dairy products on Connecterra customer farms has increased by 30%.


Intelligent robots for sowing, farming, and harvesting. Artificial intelligence technology is widely used in various scenarios such as sowing, farming, and picking in agricultural production, which has greatly innovated agricultural production methods and improved production efficiency. The American company Aboundant Robotics has developed an apple picking robot, which uses a camera to obtain photos of fruit trees, uses binocular stereo vision, image recognition and other technologies to locate the fruits and determine their maturity, determine the fruits suitable for picking, and then use the robot Precise control technology for non-destructive picking of fruits, the picking speed is as high as one per second.


Weed control. Relying on excellent sensor technology and image recognition capabilities, Blue River Technology has developed a robot called See&Spray to help control weeds in cotton fields. It relies on computer vision and machine learning to determine whether it is crops or weeds in front of it. Even if the target is only the size of a postage stamp, it can accurately identify it. Once it is determined that it is not a crop, the robot will control the nozzle to spray to avoid corroding the cotton.


Precise spraying and spray nozzles can help prevent weeds from becoming resistant to herbicides and can reduce herbicide usage by up to 90%. This not only improves the efficiency of weeding and helps farmers stabilize their income, but also protects crops and the environment by reducing the use of chemicals.

Analysis of soil composition and fertility. The analysis of soil composition and fertility is one of the most important tasks in the pre-production stage of agriculture, and it is also an important prerequisite for realizing quantitative fertilization, selection of suitable crops, and economic benefit analysis. Using non-invasive ground penetrating radar imaging technology to detect the soil, and then using artificial intelligence technology to analyze the soil conditions, an association model can be established between soil characteristics and crop varieties suitable for planting.


For example, IntelinAir has developed an unmanned aerial vehicle that uses similar MRI technology to take photos of the soil. Through intelligent analysis, the soil fertility can be determined and the crops suitable for planting can be accurately judged.


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Analysis of irrigation water supply and demand. The intelligent irrigation control system based on artificial intelligence technology integrates expert system technology, automatic control technology, communication technology, sensor technology and other high-tech. It can monitor soil moisture in real time. According to the detected climate index and local hydrometeorological observation data, Analyze the supply and demand of irrigation water and select the best irrigation planning strategy.


Seed quality identification. As one of the most important means of production in agricultural production, the quality of seeds is directly related to crop yields and production benefits. Using image analysis technology and neural networks and other non-destructive methods to detect the types, purity and safety of crop seeds can effectively control and improve the quality of agricultural products.


Agricultural expert system. The agricultural expert system is an intelligent computer program system with a considerable amount of expert-level knowledge and experience in the agricultural field, which can simulate the thinking of agricultural experts and solve problems in the agricultural field. The agricultural expert system can analyze data in the field of agricultural production and obtain timely solutions to problems that may be encountered in various stages of agricultural production.


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Animal and plant health monitoring. For example, Connecterra is a Dutch agricultural technology company that mainly develops and produces electronic wearable devices for dairy cows. These devices have multiple built-in sensors, the supporting analysis software incorporates machine learning technology, and the hardware and software cooperate to monitor the health of livestock in real time. By learning the behavior patterns of cows through wearable sensors, dairy farmers can also notice possible problems earlier, such as lameness or indigestion of cows, and get advice. With the help of this information, the output of dairy products on Connecterra customer farms has increased by 30%.


Intelligent robots for sowing, farming, and harvesting. Artificial intelligence technology is widely used in various scenarios such as sowing, farming, and picking in agricultural production, which has greatly innovated agricultural production methods and improved production efficiency. The American company Aboundant Robotics has developed an apple picking robot, which uses a camera to obtain photos of fruit trees, uses binocular stereo vision, image recognition and other technologies to locate the fruits and determine their maturity, determine the fruits suitable for picking, and then use the robot Precise control technology for non-destructive picking of fruits, the picking speed is as high as one per second.


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Weed control. Relying on excellent sensor technology and image recognition capabilities, Blue River Technology has developed a robot called See&Spray to help control weeds in cotton fields. It relies on computer vision and machine learning to determine whether it is crops or weeds in front of it. Even if the target is only the size of a postage stamp, it can accurately identify it. Once it is determined that it is not a crop, the robot will control the nozzle to spray to avoid corroding the cotton.


Precise spraying and spray nozzles can help prevent weeds from becoming resistant to herbicides and can reduce herbicide usage by up to 90%. This not only improves the efficiency of weeding and helps farmers stabilize their income, but also protects crops and the environment by reducing the use of chemicals.


Intelligent greenhouse system. In western developed countries, the intelligent greenhouse system has been widely and deeply applied. For example, currently about 85% of greenhouses in the Netherlands are controlled by computers, and Germany has successfully applied 3S technology (Geographic Information System GIS, Global Positioning System GPS, RS) to greenhouse control and management.


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Through various sensors installed in the greenhouse, data such as soil moisture, soil moisture, air humidity, air temperature, light intensity, and plant nutrient content can be monitored in real time, and the collected data can be analyzed and processed through an artificial intelligence system to simulate the most It is suitable for the environment for the growth of crops in the greenhouse, and remotely and automatically controls the water supply system, heating device, humidification device, deworming device, roller blind equipment, shading equipment, fertilization system, etc., so as to improve the growth environment of the crops in the greenhouse and achieve growth regulation Cycle, improve product quality, reduce production costs, increase economic efficiency and other purposes.



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