From breeding to planting, big models make agriculture smarter

Wang Yaojun

Recently, the text generation video large model Sora was released. The large model has the ability to deeply simulate the real physical world. By understanding the existence of objects in the physical world, it can generate complex scenes with multiple roles. Since the advent of ChatGPT in 2022, large models have attracted attention from all walks of life. Large models, especially large language models, have become a milestone in the development of science and technology in the field of artificial intelligence. Simply put, a large model is a model with a large number of parameters trained with large-scale data combined with advanced algorithms. It can capture complex patterns and laws in large-scale data, thereby achieving understanding of the problem and then giving reasoning conclusions.

From the perspective of application fields, big models can be divided into general big models and industry big models. General big models are based on large-scale, multi-field data training and are applied to multiple general industries. Industry big models are targeted at the needs of a specific industry or field. Among them, agricultural big models are based on large-scale agricultural data and use big model-related technologies to build services for agricultural scenario applications.

Traditional crop breeding is mainly carried out in the mode of empirical breeding, and most of them are organized in the form of scientific research teams to carry out scientific research. Thousands of crop breeding teams, large and small, have accumulated massive amounts of data, but there are problems such as inconsistent data formats, low levels of informatization, and basically unable to complete effective integration and data sharing. Biological breeding requires the discovery of genes first, and the analysis of which genes are related to the traits of the variety. The genome of agricultural organisms contains hundreds of millions or billions of base pairs, which are eventually assembled into tens of thousands or hundreds of thousands of genes. The large agricultural model can analyze and process massive agricultural genetic data, use algorithms to select and match the relationship between different traits and genes, and achieve scientific management, professional division of labor, and streamlined operations in the breeding process, helping to transform "empirical breeding" to "precision breeding."

Traditional agricultural planting mainly depends on the weather and is greatly affected by natural changes. The application of large agricultural models can collect, analyze and make decisions on data such as climate change, soil type, water and fertilizer conditions, and realize intelligent planting.
Agricultural big models can help agricultural production cope with the impact of climate change. By collecting and analyzing climate data, climate prediction models can be generated, enabling agricultural producers to take timely measures to mitigate the negative impact of adverse weather events and reduce agricultural risks.

The agricultural big model can provide farmers with accurate and reliable data information. Through real-time monitoring and analysis of key information such as soil moisture conditions, crop growth, disaster conditions, insect conditions, etc., it helps farmers formulate scientific and reasonable agricultural production plans, predict the best sowing time for crops, and provide fertilization recommendations based on crop varieties and growth cycle data.

Traditional agricultural breeding mainly relies on manpower and puts tremendous pressure on the environment. The application of large agricultural models can collect, analyze and make decisions on data such as livestock and poultry breeding environment, feed consumption, growth rate, health status, etc., to achieve smart breeding.

The big agricultural model can help to intelligently and real-timely control the breeding environment, such as temperature, humidity, ammonia concentration, etc. Through intelligent algorithms, it can realize automatic temperature control, humidity control, lighting control and ventilation system management, creating the best environment suitable for the growth of livestock and poultry, thereby helping to reduce the probability of disease, reduce energy consumption, waste of resources, etc.

The agricultural big model can use electronic tags or biometric technology to individually identify each livestock and poultry, and achieve precise feeding and health management based on the animal's growth stage, health status and nutritional needs. This not only helps to improve feed utilization efficiency, but also helps to identify and deal with animal health problems at an early stage.

The agricultural big model can provide real-time diagnosis by linking the intelligent monitoring platform and the livestock and poultry big data platform, and realize real-time monitoring and timely warning of the entire breeding process. By analyzing the recorded data of the entire breeding process, the agricultural big model helps to optimize the entire breeding process.

(The author is an associate professor and doctoral supervisor at the College of Information and Electronics, China Agricultural University)