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

In modern agricultural mechanisation and intelligent production, precise weeding is a crucial step in enhancing crop yields and reducing the use of chemical agents.

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 Technology's OpenMV smart camera to build a machine vision-based weed identification system for detecting the distribution of weeds on field surfaces.

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

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

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

An agricultural intelligent equipment manufacturer has 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 via camera, performs HSV colour space conversion and luminance analysis on green areas, extracting the primary regions of plant leaves. Utilising typical characteristics of broadleaf weeds (wide leaf surface, coarse texture, lighter colour, disordered orientation), the system achieves target classification by integrating indicators such as edge complexity, luminance variance, and shape roundness.

The OpenMV module can adapt to complex lighting conditions in the field through dynamic exposure and illumination compensation algorithms, and performs inter-frame stability 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 the 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's OpenMV embedded vision module is compact, low-power, and features customisable 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.