Through the utilization of a variety of VLSI-based ensemble clustering techniques, the research proposes a novel approach to the separation of images of larvae. Increasing the speed of the segmentation process is accomplished by the utilization of Very Large-Scale Integration (VLSI) technology. Increasing the accuracy of the identification of larval structures is the goal of the ensemble clustering method, which incorporates several distinct clustering algorithms. The utilization of parallel processing, which is made possible by VLSI, results in improved separation performance. The purpose of this strategy is to effectively address issues such as variances in the appearance of larvae and complex backgrounds. When it comes to reliably segregating larvae, tests have demonstrated that VLSI-based ensemble clustering is an effective method. Not only does the utilization of VLSI allow for the acceleration of the process, but it also makes it suited for real-time applications in domains such as ecology and bug research. The study contributes to the advancement of image processing for automatic larval analysis, which provides a better means for biological research to be more precise and efficient.

Very Large-Scale Integration, Larve Images, Food Processing, Ensemble Clustering.