Crop Monitoring

The yield and quality of important crops such as rice and wheat determine the stability of food security. Traditionally, the monitoring of crop growth mainly relies on subjective human judgment and is not timely or accurate.

Flowering Detection

The heading date of wheat is one of the most important parameters for wheat crops. An automatic computer vision observation system can be used to determine the wheat heading period.

Plantation monitoring

In intelligent agriculture, image processing with drone images can be used to monitor palm oil plantations remotely.

Insect Detection

Rapid and accurate recognition and counting of flying insects are of great importance, especially for pest control. Traditional manual identification and counting of flying insects is inefficient and labor-intensive.

Plant Disease Detection

Automatic and accurate estimation of disease severity is essential for food security, disease management, and yield loss prediction. The deep learning method avoids labor-intensive feature engineering and threshold-based image segmentation.

Automatic weeding

Weeds are considered to be harmful plants in agronomy because they compete with crops to obtain the water, minerals and other nutrients in the soil. Spraying pesticides only in the exact locations of weeds greatly reduces the risk of contaminating crops, humans, animals and water resources.

Automatic Harvesting

In recent years, with the continuous application of computer vision technology, high-end intelligent agricultural harvesting machines, such as harvesting machinery and picking robots based on computer vision technology, have emerged in agricultural production, which has been a new step in the automatic harvesting of crops. 

Agricultural Product Quality Testing

The quality of agricultural products is one of the important factors affecting market prices and customer satisfaction. Compared to manual inspections, Computer Vision provides a way to perform external quality checks and achieve high degrees of flexibility and repeatability at a relatively low cost and with high precision.

Irrigation Management

Soil management based on using technology to enhance soil productivity through cultivation, fertilization or irrigation has a notable impact on modern agricultural production.

UAV Farmland Monitoring

Real-time farmland information and an accurate understanding of that information play a basic role in precision agriculture. Over recent years, UAV, as a rapidly advancing technology, has allowed the acquisition of agricultural information that has a high resolution, low cost, and fast solutions.

Yield Assessment

Through the application of computer vision technology, the functions of soil management, maturity detection and yield estimation for farms have been realized. Moreover, the existing technology can be well applied to methods such as spectral analysis and deep learning.

Animal Monitoring

Animals can be monitored using novel techniques that have been trained to detect the type of animal and its actions. There is much use for animal monitoring in farming, where livestock can be monitored remotely for disease detection, changes in behavior, or giving birth.

Farm Automation

Technologies such as harvest, seeding, and weeding robots, autonomous tractors, and drones to monitor farm conditions and apply fertilizers can maximize productivity with labor shortages.