36Kr Research Institute | 2024 China "AI + Agriculture" Industry Research Report
As the development foundation and pillar industry of the national economy, agriculture is directly related to national food security and people's livelihood and well-being. With the intensification of global climate change, extreme weather such as high temperatures, floods, and droughts occurs frequently, and the problem of agricultural labor shortage is becoming increasingly prominent. The traditional agricultural model is facing unprecedented challenges. These uncertain external factors not only put forward higher requirements for the efficiency of agricultural production but also pose a threat to the quality and safety of agricultural products.
In this context, the new generation of information technology represented by artificial intelligence is bringing a revolutionary change to the traditional agricultural industry. Through the deep integration of advanced technologies such as the Internet of Things and big data analysis, artificial intelligence can not only effectively alleviate the dilemma of agricultural labor shortage but also show great potential in improving production efficiency, optimizing resource allocation, and reducing production costs. The traditional agricultural production model that relies on manpower and experience is gradually transforming into an intelligent and digital one, opening up a new path for the sustainable and high-quality development of agriculture.
For this reason, this paper will deeply analyze the development background and current situation of the "AI + Agriculture" new production model, focus on discussing the innovative applications of artificial intelligence in the planting and animal husbandry industries, analyze its performance in improving production efficiency and optimizing resource allocation, and look forward to future development trends.
1. Industry Development Overview
Development Drivers
1) Policy-driven: Policies guide the deep integration of artificial intelligence and agricultural scenarios
In recent years, the state and local governments have intensively issued a series of policy measures aimed at promoting the deep integration of artificial intelligence and agriculture to meet the urgent needs of modern agricultural development. These policies not only clarify the key role of the new generation of information technology represented by artificial intelligence in the development of smart agriculture but also emphasize the importance of promoting the digital transformation of agriculture. The "Opinions on Doing a Good Job in Key Work in the 'Three Rural' Areas to Ensure the Timely Realization of a Moderately Prosperous Society in All Respects" issued by the Central Committee of the Communist Party of China and the State Council clearly states that it is necessary to strengthen the construction of modern agricultural facilities and accelerate the application of modern information technologies such as the Internet of Things, big data, blockchain, and artificial intelligence in the agricultural field. In addition, the policy level also emphasizes accelerating the breakthrough of technologies such as artificial intelligence to further expand the application scenarios of the new generation of information technology in the agricultural field. The "Notice on Supporting the Construction of a New Generation of Artificial Intelligence Demonstration Application Scenarios" issued by the Ministry of Science and Technology lists smart farms as the first batch of demonstration application scenarios, putting forward higher requirements for the application depth and breadth of "AI + Agriculture". In terms of social investment, relevant policies have also provided clear guidelines. For example, documents such as the "Opinions on Expanding Effective Investment in Agriculture and Rural Areas to Accelerate Making Up for Outstanding Shortcomings in the 'Three Rural' Areas" and the "Guidelines for Social Capital to Invest in Agriculture and Rural Areas" encourage social capital to participate in the construction of smart agriculture, accelerate the construction of big data projects in agriculture and rural areas, carry out the construction of new infrastructure such as agricultural Internet of Things, big data, blockchain, and artificial intelligence, and carry out basic algorithm research in artificial intelligence to break through the core algorithms of biological big data mining and analysis. The introduction of these policies provides strong policy support for the development of "AI + Agriculture".
2) Technology-driven: The integrated development of multiple technologies to improve the intelligence level of agricultural production
The continuous breakthroughs in technologies such as satellite remote sensing, 5G, big data, and artificial intelligence have laid a solid foundation for the intelligent development of agriculture. Satellite remote sensing technology realizes the precise collection and analysis of agricultural situation information through advanced satellite images, promoting the development of precision agriculture. 5G, with its low latency and large bandwidth characteristics, lays a reliable foundation for the immediate transmission of agricultural data. Big data realizes the precise prediction of crop yield and quality through the processing and mining of massive data such as weather, disasters, geography, and soil. With its advantages in data processing, computer recognition, and deep learning, artificial intelligence has shown significant results in reducing costs and resource consumption, improving crop yields, and ensuring food safety. On the one hand, in the production materials link, artificial intelligence provides the possibility of realizing smart breeding and precise seed selection through the recognition and screening of seeds or high-quality variety genes. On the other hand, in the production and operation link, the combination of smart agricultural machinery equipped with artificial intelligence technology and agricultural management systems realizes automated operations while forming a closed loop between agricultural data collection, analysis, prediction, and planning management, helping to improve the input-output ratio of agriculture.
3) Demand-driven: Coping with resource shortages and improving production efficiency
From the demand side, the effective and efficient utilization of resources such as manpower and production materials has become an important challenge in China's agricultural production field, and "AI + Agriculture" provides an innovative path to solve these challenges. On the one hand, with the intensification of population aging and the continuous increase in labor costs, the problem of agricultural labor shortage is becoming more and more prominent. According to data from the National Bureau of Statistics, in 2023, the elderly population and the aging level in China were 297 million and 21.1% respectively, and there is an outflow of young and middle-aged people from rural areas in China. The aging process of the agricultural workforce will continue, and the contradiction between manpower supply and demand will become more and more prominent. According to the Rural Revitalization Network statistics, in 2025, the total population gap in China's agriculture in the fields of production and operation, technical operation, and agricultural informatization will exceed 10 million. Unmanned and automated agricultural machinery and equipment have gradually become an important carrier to fill the manpower gap, helping to improve agricultural production efficiency while reducing reliance on manpower. On the other hand, under the traditional agricultural production model, the problem of resource waste is prominent. Agricultural operations that rely on manpower and production experience lack scientific planning and management. The introduction of artificial intelligence technology is driven by agricultural data. With the precise monitoring and data analysis capabilities, combined with automated processes such as intelligent irrigation and precise pesticide application, it provides a solid data basis for the optimal allocation of agricultural resources and improves the utilization rate of agricultural resources and production efficiency.
Development Status: The rapidly growing AI agriculture, with agricultural big data, smart agricultural machinery, and integrated solutions as the main application directions
In the context of the accelerated transformation of new technologies, the "AI + Agriculture" market continues to expand. According to data from the Forward-looking Industry Research Institute, the market size of "AI + Agriculture" in China has reached approximately 68.5 billion yuan in 2021, and is expected to exceed the 90 billion yuan mark in 2024, with an average annual compound growth rate of about 10%.
Currently, artificial intelligence technology has three main application directions in the agricultural field. The first is agricultural big data. In the AI agricultural model, the data collected by intelligent unmanned aircraft or software detection can be processed through computer vision and deep learning algorithms to accurately judge the impact of the external environment on crops and make corresponding predictions, achieving the precise utilization of "water, fertilizer, and pesticide", and accurately identifying the growth status of animals through computer recognition technology to realize the integrated management of livestock from production to slaughter. The second is smart agricultural machinery, which is typified by agricultural robots, agricultural unmanned aircraft, and autonomous agricultural machinery. Smart agricultural machinery is a key application to solve the labor shortage problem and has great advantages in sowing, farming, picking, weeding, inspection, and information collection. It has already been applied in some machinery such as tractors, combine harvesters, and water and fertilizer integrated machines. Third is the integrated solution. The combination of agricultural Internet of Things, big data, and artificial intelligence provides all-round integrated service solutions from production, management, trading to consultation for agricultural and animal husbandry enterprises, improving the digital and intelligent level of agricultural and animal husbandry enterprises, and ultimately achieving cost reduction and efficiency increase.
2. "AI + Agriculture" Industrial Structure Analysis
The "AI + Agriculture" industrial chain mainly includes upstream equipment and technology suppliers, midstream solution providers, and downstream agricultural producers. Upstream equipment and technology suppliers include hardware facilities such as sensors and satellite remote sensing equipment, as well as software technologies such as cloud computing and big data; the midstream includes agricultural automation machinery, data platform services, agricultural intelligent analysis, and marketing analysis; the downstream includes agricultural product producers such as farms, family farms, and rural cooperatives, as well as supporting service facilities such as logistics and e-commerce platforms.
Upstream Equipment and Technology Suppliers: The Cornerstone of the Development of "AI + Agriculture"
The ecological upstream includes hardware infrastructure such as satellite remote sensing systems and sensors, as well as software technology facilities such as large models and cloud computing. Specifically, the satellite remote sensing system is an important technical means to obtain ground data. Based on remote sensing technology, it can quickly and accurately obtain information on the planting area, crop growth, drought and flood conditions, pest and disease conditions, and soil moisture content of the space and the surrounding environment of the crops. Agricultural sensors are the basis for realizing agricultural informatization. The combination of various sensors such as soil, temperature, humidity, light, image, and spectrum makes the information types covered by agricultural emotion perception more accurate. Through the use of sensors, multi-dimensional data can be obtained, and crops can be monitored in real-time from multiple aspects to assist in decision-making. Cloud computing enables various "data islands" to be interconnected through algorithms and analysis systems, providing guidance for agricultural activities and realizing the modernization and upgrading of agriculture on the "cloud". The breakthrough of AI large model technology further promotes the rapid development of modern agriculture. AI can quickly process and mine massive data, and through deep learning and training, it can provide guidance and predictions for agricultural situation decisions, and has a wide range of applications in many fields of agriculture. For example, in the plant protection link, the AI large model relies on historical pest and disease data to make more accurate judgments and early warnings on the trend of pest and disease, helping plant protection personnel to grasp the dynamics of pests and diseases in a timely manner, thereby accurately formulating prevention and control strategies; in the animal husbandry link, through recognition technology to track and monitor animal behavior and growth conditions, precise control of livestock is carried out; in the breeding link, the AI large model is combined with biotechnology. Based on the analysis of massive breeding data, high-quality genes can be selected, and breeding and mating can be simulated to accelerate the entire breeding process and reduce costs and the risk of breeding failure.
Midstream Solution Providers: Based on hardware equipment and technology, providing solutions for smart agriculture through design, integration, and implementation
The midstream of the industry is mainly solution providers, who integrate and transform the upstream hardware equipment and technology to form solutions in the agricultural field. Currently, it is mainly divided into three parts: agricultural automation machinery, agricultural intelligent analysis, and data platform services.
Agricultural automation machinery refers to integrating AI intelligent perception and algorithms into agricultural machinery according to needs to improve the intelligence and unmanned operation of various agricultural links such as cultivation and harvesting. Such as agricultural unmanned aircraft, unmanned vehicles, agricultural autonomous driving vehicles, intelligent harvesters, picking robots, etc.
Agricultural intelligent analysis Based on technologies such as artificial intelligence, sensors, satellite remote sensing, and big data, it provides digital solutions covering the entire process of planting, including weather prediction, environmental monitoring, pest and disease control, sowing, fertilization, and irrigation, as well as the entire process of animal breeding, disease identification, individual monitoring, feeding, and weighing in the breeding industry.
Data platform services For specific demand scenarios, integrate technology and equipment to form a complete set of efficient solutions to meet the actual needs of customers, including smart solution providers covering the integrated management of agricultural and animal husbandry enterprises, big data platforms focusing on data mining and analysis, and marketing. Smart solutions provide integrated intelligent enterprise management for agricultural and animal husbandry enterprises, linking the upstream and downstream of the industry from breeding, production, collection, to quality inspection, sale, and traceability. Big data platforms focus on the mining, cleaning, and analysis of data from the upstream and downstream of the agricultural industry. Combined with the cloud data brain, it provides a comprehensive vertical query and update of agricultural data, providing enterprises with visual analysis and real-time summaries of price and market trend changes. The marketing data platform conducts in-depth mining of the sales data of agricultural and sideline products, consumer behavior, marketing performance, and market trends to provide more accurate market guidance for farmers and agricultural enterprises, helping agricultural producers optimize planting strategies, predict market demand, and formulate corresponding marketing plans to improve the conversion rate level.
Downstream Service Providers and Agricultural Producers: The entire chain of processing, sales, and transportation undergoes intelligent upgrading
The downstream service providers are mainly e-commerce platforms, smart logistics, and agricultural producers. Among them, agricultural producers include agricultural and sideline product producers and processors such as farms, agricultural cooperatives, and family farms, while e-commerce platforms and smart logistics provide necessary sales channels and transportation guarantees for agricultural and sideline products. With the support of AI technology, all links of sales and transportation have achieved efficient, precise, and intelligent upgrades. Through technical capabilities such as personalized product recommendations, smart logistics planning, and product traceability, the entire chain optimization of agricultural and sideline products from the field to the consumer's life is promoted.
Three. Overview of Key Segmented Application Scenarios
Monitoring and Prevention: Safeguarding the Growth of Crops
Monitoring and prevention mainly include the monitoring and prediction of the growth environment and status of crops, as well as the monitoring and prevention of pests and diseases. In terms of environmental monitoring, AI combined with sensors and Internet of Things devices can collect and analyze environmental data such as soil moisture, temperature, light intensity, and air humidity in real-time, thereby predicting the growth trend of crops and promptly suggesting adjustments when the environmental conditions are unfavorable. From the perspective of pest and disease control, the traditional manual field inspection and visual identification methods are time-consuming, labor-intensive, and have limited accuracy. However, AI can quickly identify the types and extent of damage of pests and diseases on crops through computer vision technology. Using unmanned aircraft or intelligent cameras to collect field images, AI models can analyze large areas of farmland in a short period of time, identify disease areas, and propose prevention and control solutions. For pests, AI can also combine data analysis to predict the outbreak time and range of pests, and formulate precise plans for pesticide spraying to reduce the amount of drug use, reduce costs, and reduce environmental pollution at the same time.
Animal Individual Identification: AI Enables Differential Management of Livestock
China is the world's largest producer and consumer of pork, with nearly 700 million live pigs slaughtered each year, and the self-sufficiency rate reaches 95%. According to statistics from Industrial Securities, the production cost of pork in China is significantly higher than that in the United States and other countries. The feed cost required to produce one kilogram of pork in China is twice that of the United States, and the labor cost required per unit of pork production is about four times that of the United States. In order to solve the supply and demand and cost problems in the breeding industry, the country has been exploring the path of scientific breeding and AI breeding in recent years, among which the identification of individual animals and their behaviors is an important application of artificial intelligence in the breeding industry. Only by grasping the growth and health status of a large number of livestock in real-time can a stable slaughter rate and quality traceability be guaranteed, ensuring the safety of meat products and preventing the spread of diseases. Through computer image and sound recognition technology, as well as deep learning and mining of massive historical data, AI has the ability to achieve precise identification of individual animals and convert the growth situation of livestock into data so that the farm can grasp it in real-time.
Smart Solutions: Building a Full-chain Service Cloud Platform to Promote the Digitalization and Intelligence of the Agricultural Industry Chain
Based on technologies such as big data, the Internet of Things, artificial intelligence, blockchain, and the rapid popularization of the mobile Internet in rural areas, data service platforms have become an indispensable part of agricultural digital intelligence, providing digital enterprise management, intelligent production, and integrated platform services for agricultural and animal husbandry enterprises, connecting the upstream and downstream of the industry, building a smart agricultural.