Agricultural Insights

Conducting research on AI in agricultural automation technologies.

A large agricultural machine navigates through a vineyard at dusk. The machine is orange with black details, and bright lights illuminate the surrounding grapevines. Several people stand nearby, watching the operation as the sky transitions from daytime to evening.

Data Collection

Gathering production and cost data from various farms.

A field is being harvested by three large combine harvesters, each in different colors—red, beige, and green. Dust and chaff fill the air behind the machines as they move through the golden wheat field. The landscape in the background is flat, likely indicating a rural setting.

Statistical Analysis

Utilizing Python and R for data analysis and insights.

As the global agricultural modernization process accelerates, artificial intelligence technology is reshaping the traditional agricultural landscape with subversive power. From precision planting to intelligent breeding, from agricultural product processing to supply chain optimization, AI technology effectively solves problems such as resource waste, inefficiency, and extensive decision-making in agricultural production through data analysis, image recognition, and machine learning, and promotes the development of agriculture towards intelligence, precision, and sustainability, injecting strong impetus into agricultural innovation.

In the field of food safety testing, AI technology combined with spectral analysis, mass spectrometry and other methods can quickly detect indicators such as pesticide residues, heavy metal content, and microbial contamination in agricultural products. By training machine learning models, the system can analyze and learn a large amount of test data to improve detection sensitivity and accuracy. Some EU countries use AI-driven food safety testing equipment to shorten the detection time of pesticide residues from several hours to a few minutes, greatly improving detection efficiency and effectively ensuring consumers' food safety.

Research Locations

Conducting on-site investigations in various agricultural regions to gather essential production, cost, and quality data related to AI technologies.

Two large agricultural machines are working in a vast, open field of grain. A combine harvester is in the process of collecting the crop, while another vehicle, possibly a tractor with a trailer, receives the harvested grain through a long chute. The arrangement of the field and the patterns created by the machinery are visible on the ground.

A red tractor with a planting attachment drives across a large, freshly tilled agricultural field. The field is expansive, bordered by a line of green trees and a small water body on one side. The sky is cloudy, providing a peaceful backdrop to the rural landscape.

Farmer Surveys

Understanding farmer awareness and challenges with AI technologies.

Research Findings

Presenting results on AI adoption in agriculture.

The milking robot controlled by the AI algorithm realizes the automated milking process. The robot can perform milking at the best time based on the AI system's judgment of the cow's condition, and can accurately adjust the milking equipment according to the milk production and nipple position of each cow to ensure the safety and efficiency of the milking process. After the introduction of this technology in dairy farms in the Netherlands, milk production increased by 15% and labor costs decreased by 30%, greatly improving farming efficiency and economic benefits.

AI can also analyze the relationship between environmental data and livestock and poultry growth indicators and continuously optimize environmental control strategies. When it is found that the environmental humidity is too high, resulting in an increase in the incidence of respiratory diseases in livestock and poultry, the system will automatically increase ventilation and dehumidification to improve the breeding environment. Through intelligent environmental control, the growth rate of livestock and poultry is accelerated, the feed conversion rate is improved, and the breeding efficiency is further improved.

an abstract photo of a curved building with a blue sky in the background

AI technology can integrate various data in the farm, including animal growth data, health data, environmental data, feed consumption data, etc. Through big data analysis and machine learning algorithms, it can explore the laws and values behind the data and provide scientific decision-making support for farmers. For example, by analyzing the growth performance and economic benefits of different breeds of livestock and poultry in a specific environment, it can help farmers choose the most suitable breeding breeds; according to market demand and price fluctuation trends, combined with the actual production situation of the farm, it can formulate the best breeding scale and sales plan to avoid market risks and increase breeding income.