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Agriculture is the foundation upon which human beings depend, and it occupies a fundamental position in the three industries, which is vital to the stability and development of the economy and society. However, with the rapid growth of population, the gradual shrinking of arable land and the acceleration of urbanization, the challenges facing agriculture are becoming increasingly severe. In order to meet this challenge, both at home and abroad are exploring the use of information technology to promote agricultural quality and efficiency. Among them, the new model of intelligent agriculture based on artificial intelligence has developed rapidly, and many typical cases have emerged. The application of artificial intelligence for extensive and deep integration of agriculture Provides useful references.
Basic situation of the integration of artificial intelligence and agriculture
There are many pain points in the fields of agricultural production and services, such as extensive production methods and incomplete agricultural services. Many companies are guided by industrial pain points, actively exploring the integration and innovation of artificial intelligence in agricultural production services, and found new breakthroughs to solve the problem of agricultural pain points.
In agricultural production, artificial intelligence facilitates the refinement of agricultural production, thereby promoting agricultural quality and efficiency. In the field of planting, companies use artificial intelligence to model and analyze crop growth and environmental data to provide precise guidance for agricultural production. For example, Infosys, IBM Watson IoT, and Sakata Seed Inc. deployed test beds on two fields in California, the United States, and used machine vision-based drones, environmental sensors, and soil sensors to collect plant heights, air humidity, and soil fertility in three dimensions. Wait for 18 kinds of data, and upload the data to the Infosys information platform for big data management and artificial intelligence technology analysis. The analysis results are fed back to the enterprise ERP system and plant breeding research and development system to guide the next production and breeding. In the breeding field, companies collect and analyze diversified data on livestock and poultry to achieve precise breeding. For example, Alibaba Cloud cooperates with Sichuan Special Drive Group and Dekang Group to promote intelligent pig farming. There are cameras connected to the ET agricultural brain in the pig farm , which automatically collect and analyze the pig's body shape and exercise data. Pigs that do not meet the standard will be driven out. Continue to exercise outdoors to ensure the quality of pork. In addition, using ET agricultural brain, combined with acoustic characteristics and infrared temperature measurement technology, you can judge whether the pig is sick through the data such as cough, barking, and temperature of the pig, and timely alert the epidemic situation.
In agricultural services, artificial intelligence can alleviate problems such as imbalances in the supply and demand of agricultural products caused by information asymmetry, and difficulties in agricultural financing. On the one hand, industry authorities or enterprises use artificial intelligence to establish a forecast model of agricultural product price trends and guide agricultural production entities to dynamically adjust production capacity, which can reduce cost waste due to blind production and improve consumer satisfaction. For example, IBM uses machine learning to analyze satellite image, weather, population, land and other data to predict the supply and demand of crops; Descartes Lab uses machine learning models based on satellite data training to predict domestic corn production for farmers ' production Provide a reference for decision-making. On the other hand, financial institutions relying on agricultural big data to establish farmers 'credit information systems can improve their ability to control risks in agricultural finance, increase farmers' financing opportunities, and reduce financing costs. For example, the "Eight Precepts" developed by the Internet credit evaluation platform Sanyin and the Internet financial company Nongxinbao, collects more than 300 dimensions of farmer data online, and uses artificial intelligence models for analysis in the background, which can complete the analysis in seconds. The credit scores of hog farmers and feedback to reviewers help financial institutions reduce risk control costs and bad debts, and also significantly reduce financing costs for farmers.
In general, some typical cases of integrated applications have emerged in the field of agricultural production and service of artificial intelligence, providing new ideas for promoting the intelligent transformation and upgrading of agriculture. However, these fusion applications are currently mainly in the exploration and pilot stage. The fusion model still needs to be optimized and perfected, and the scope of application needs to be gradually expanded.
China's artificial intelligence and agricultural integration are facing multiple challenges
Big data, artificial intelligence and other technologies have formed a relatively mature fusion model and a wide range of applications in foreign agricultural fields. Although there have been some typical cases in China, the overall situation is still in its infancy, and the digital, network, and intelligent transformation of agriculture still faces many challenges.
First, the rural network infrastructure is weak. The integrated application of artificial intelligence in the agricultural field has high requirements for real-time network response and massive data accumulation. However, China's village-level information service network is not sound enough, and the level of network in the agricultural sector needs to be improved. According to statistics, the Internet penetration rate in rural areas in China is 36.5%, which is only half that in urban areas.
The second is the insufficient supply of intelligent agricultural equipment. Dedicated chips for intelligent agricultural equipment are relatively scarce, and general-purpose chips are very prone to damage at agricultural sites with poor environments, which in turn leads to the application of agricultural intelligent facilities being blocked. At the same time, due to the complexity of agricultural scenarios and the inefficient application of agricultural intelligent robots and other equipment in practical applications, the performance of intelligent equipment needs to be further improved.
Third, farmers are not willing and capable of applying artificial intelligence. On the one hand, the large amount of investment in intelligent agricultural equipment and the long recovery cycle have led farmers to "dare to use it." On the other hand, the operation method of intelligent agricultural equipment is quite different from that of traditional agricultural equipment. Farmers have insufficient ability to operate intelligent equipment, and "unusable" also hinders the intelligent development of agriculture.
Suggestions to accelerate the deep integration of artificial intelligence and agriculture
In response to the challenges of the deep integration of artificial intelligence and agriculture, industry authorities should start from multiple perspectives such as infrastructure, technology supply, and industrial demand, comprehensively promote the deep integration of artificial intelligence and agriculture, and explore effective paths for the high-quality development of modern agriculture .
In terms of support capabilities, efforts have been made to strengthen the construction of rural network infrastructure and agricultural information service platforms. On the one hand, we will further strengthen the construction of rural information infrastructure, expand the coverage of broadband and mobile networks, increase network speed, and lay a good foundation for deploying intelligent agricultural facilities and collecting agricultural big data. On the other hand, the establishment of a sound agricultural information service platform to improve the intelligent forecast level of agricultural product supply and demand, price and other information, to provide more reference and guidance for agricultural production decision-making.
In terms of technology supply, the artificial intelligence technology supply level in the agricultural field has been continuously improved. On the one hand, it is necessary to increase support for the development and application of agricultural specialized chips, sensors and other basic components, as well as intelligent equipment such as agricultural drones and agricultural robots, to improve the supply capacity and quality of intelligent agricultural equipment. On the other hand, we should focus on cultivating integration solution providers in the agricultural field, and promote the integration of artificial intelligence solutions in the agricultural field.
In terms of industrial demand, farmers' willingness and ability to apply artificial intelligence should be vigorously cultivated. On the one hand, the propaganda work of deep integration of artificial intelligence and agriculture should be strengthened, so that farmers can fully realize the long-term benefits of applying artificial intelligence, and motivate farmers to develop smart agriculture. On the other hand, it is necessary to strengthen financial subsidies for investment and application of agricultural intelligent equipment, strengthen the training of farmers in the application of intelligent facilities, and improve farmers' ability to carry out agricultural intelligent production and management.