Vision-Based Weed Classification for Selective Soil Cultivation

The patent replaces manual mechanical weeding with an automated system combining optical sensors (cameras), neural network-based image processing, and automated actuators. The system captures images of crops and weeds, processes them through neural networks to identify and classify plants, then activates actuators to selectively remove weeds while preserving crops, eliminating manual labor while maintaining high selectivity.

The system enables self-service by autonomously performing the complete weeding process without human intervention. The sensor unit automatically detects plants, the evaluation unit independently classifies them as crops or weeds using neural networks, and the actuator unit self-activates to remove identified weeds, creating a fully autonomous weeding system that operates independently.

The system implements continuous feedback through optical sensors that capture real-time images of the field. These images are processed by neural networks that provide feedback classification of each plant as crop or weed. Based on this feedback, the actuator unit adjusts its operation to selectively target only weeds, ensuring high productivity while maintaining accurate crop-weed differentiation and preventing crop damage.

The patent replaces blind mechanical control systems with an intelligent vision-based system. Instead of relying on pre-programmed paths or distance-based control, the system uses optical sensors and neural networks to visually identify and differentiate crops from weeds in real-time, enabling selective weed removal with high productivity and accuracy.

The system segments the data processing task into distinct modules: image acquisition by sensors, pre-processing to extract relevant features, neural network-based classification of plants, and actuator control signals. This segmentation allows each module to operate independently and efficiently, achieving real-time processing speed while managing computational complexity through modular architecture.

The system extracts only the essential features from raw images that are necessary for plant classification, such as color, shape, and texture characteristics. By taking out and focusing on these key discriminative features rather than processing entire high-resolution images, the system achieves real-time processing speed while reducing computational complexity and data volume.