Accurate identification of broadleaf weeds within 0.5 seconds, SingTown technology drives agricultural machinery upgrades.

In modern agricultural mechanization and intelligent production, precise weeding is a crucial step for enhancing crop yield and reducing the use of chemical agents.

Traditional weeding methods rely on manual labor or simple color sensors, making it difficult to accurately distinguish between crops and weeds. This is particularly challenging when broadleaf weeds closely resemble crops in appearance, leading to frequent issues of misapplication and missed spraying.

To this end, agricultural intelligent equipment manufacturers plan to adopt SingTown's OpenMV smart camera to build a machine vision-based weed identification system for detecting the distribution of weeds on the field surface.

The system performs real-time imaging of crop inter-rows via cameras, utilizing image recognition algorithms to analyze differences in color, shape, and texture, thereby distinguishing "broadleaf weeds" from normal crops.

When weeds are detected, the system outputs control signals to the spraying unit or weeding device, enabling automated and precise operations.

This solution supports operation in complex environments such as strong outdoor light and shadows, with adjustable detection range and high recognition accuracy, effectively improving weeding efficiency and reducing pesticide usage.

Agricultural intelligent equipment manufacturers have developed a weed identification system based on SingTown Technology's OpenMV smart camera, which enables real-time weed detection on agricultural machinery. The system integrates computer vision, AI image recognition, color space segmentation, morphological analysis, and feature classification algorithms.

The system captures field surface images via cameras, performs HSV color space conversion and brightness analysis on green areas, and extracts the main regions of plant leaves. Utilizing typical characteristics of broadleaf weeds (wide leaf surface, coarse texture, lighter color, and disordered orientation), the system achieves target classification by integrating indicators such as edge complexity, brightness variance, and shape roundness.

The OpenMV module can adapt to complex lighting conditions in the field through dynamic exposure and illumination compensation algorithms, and perform inter-frame stabilization analysis on moving vegetation to effectively reduce false detections. When the system determines the presence of broadleaf weeds in the detection area, it immediately outputs a high-level signal to the main control unit, triggering the spraying or mechanical weeding procedure.

Furthermore, the system can also integrate with GPS or route planning modules to achieve recording and statistical analysis of mowing trajectories.

The test results indicate that the system can achieve an accuracy rate of over 90% in identifying broadleaf weeds under varying outdoor lighting conditions and diverse terrains, with a response time of less than 0.5 seconds.

SingTown Technology's OpenMV embedded vision module features a compact size, low power consumption, and customizable algorithms, making it highly suitable for integration into devices requiring edge deployment, such as agricultural robots, intelligent sprayers, and unmanned agricultural vehicles. It supports smart agriculture in achieving precise weeding and ecological management.