The flowering period is a critical time for the growth of plants. Counting flowers can help farmers predict the corresponding fields' yield information. As there are several works proposed for flower counting purposes, they lack the prediction of different flowers with counts. Hence, a novel model has been proposed in this study. Initially, this model is fed with different flower images as input, then these images undergo pre-processing. In pre-processing, the images are converted to grayscale for improved accuracy, and then the image's noise is removed using bilateral filters. Noise-removed images are then given for edge detection, using GI-CED. Edge-detected images are then augmented to improve the learning rate of the model. Augmented images are labeled using ZHA-KNN. Labeled images feature extracted and are given to MA-YoloV9, which is pre-trained to detect flowers in the image count and obtained as output. Overall, the proposed model was implemented and obtained an accuracy value of about 98.8% and F1-Score obtained 92.2% which is far better than the previous counting models.