GPS-system control can be done with tractors and combines.

With the autonomous GPS system, it is possible to prevent overlapping of technological operations and repeated trips in the management of the unit.

In addition, by saving agrochemicals, it reduces energy and labor consumption.

The artificial intelligence technology has profoundly impacted various industries of robotics, and agricultural autonomous vehicles have also greatly benefited from it. I believe its main profound impacts lie in two aspects: intelligent perception and intelligent decision-making.

Intelligent perception: Whether it is a large-scale harvesting agricultural robot or a small-scale harvesting agricultural robot, they need to be familiar with the working environment. Therefore, the perception sensors they carry are crucial. Commonly used perception sensors include optical vision cameras, LiDAR, millimeter-wave radar, etc. These sensors can help agricultural robots identify farmland roads, farmland weeds, fruit positions, and also assist agricultural robots in mapping the working environment for tasks such as path planning. The processing of these sensors requires mature deep neural networks such as CNN, RNN, pointnet, YOLO, etc.

Intelligent decision-making: Advanced artificial intelligence technologies such as ChatGPT and transfer learning will certainly be applied in the agricultural field in the future. For example, based on various parameters of agricultural areas such as temperature, humidity, light intensity, oxygen, carbon dioxide concentration, and expert experience, these technologies can autonomously predict decisions like watering, weeding, pest control, harvesting, and send these commands to agricultural robots for execution. This ability to predict instructions based on expert experience is the application of artificial intelligence technology in agriculture.