Precise 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 critical component for enhancing crop yields and reducing chemical usage.

Traditional weeding methods rely on manual labour or simple colour sensors, making it difficult to accurately distinguish between crops and weeds. This is particularly challenging as broadleaf weeds and crops are highly similar in appearance, often leading to issues of misapplication or missed spraying.

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

The system performs real-time imaging of crop inter-rows via cameras, utilizing image recognition algorithms to analyze differences in colour, 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, colour space segmentation, morphological analysis, and feature classification algorithms.

The system captures field surface images through cameras, performs HSV colour 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 colour, and disordered orientation), the system achieves target classification by combining 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 program.

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

Test results indicate that the system achieves an identification accuracy of over 90% for broadleaf weeds under varying outdoor lighting conditions and diverse terrains, with a response time of less than 0.5 seconds.

SingTown OpenMV embedded vision modules feature compact size, low power consumption, and customizable algorithms, making them ideal for integration into devices requiring edge deployment, such as agricultural robots, smart sprayers, and unmanned farm vehicles, thereby supporting precision weeding and ecological management in smart agriculture.