Performance evaluation of RGB and YCrCb colour space models for submerge aquatic vegetation area estimation in the shallow lake 7/1F Shah Alam, Selangor

The uncontrolled population of the submerged aquatic vegetation (SAV) in the shallow lake leads to water quality deterioration, which negatively impacts the beauty of its surroundings and limits the recreational activities of the community there. One of the lakes affected by this problem is Community Lake 7/1F Shah Alam, Selangor, Malaysia. One way to overcome this problem is by identifying the distribution of the submerged aquatic vegetation in the lake before applying any treatment to reduce its number. Avoiding unnecessary chemical or bioorganic treatment waste in the lake ecosystem is important, which can invite another problem. An unmanned aerial vehicle (UAV) is an advantage in surveying while estimating the affected area caused by these parasite plants. Unfortunately, identifying the population of this small vegetation accurately from the image requires extensive image processing techniques. To address this issue, this paper presents a population area estimation method for vegetation in shallow lakes based on the Colour Space Model and Edge Detection in image processing. The edge detection technique initially segments and extracts the lake’s boundary from the image. Then, the Color Space Model, with the RGB and YCrCB models, are utilized to find the best area estimation. These techniques are compared, and the YCrCb colour space model estimates the SAV area 12% more accurately than the RGB colour space model. In conclusion, integrating image processing with the UAV in estimating the small vegetation area in a shallow lake is feasible with the high-performance processor and technique.

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