• ANSHUL PAYASI
• DHIRAJ RAJVAIDHYA

Image classification is growing and becoming a trend among the recent year technology developers especially with the growth of data in different organization such as e- commerce, automotive, healthcare, and gaming. Image classification is the one of most critical use case in digital image analysis. The image classification problem is to categorize all the pixels of a digital image into one of the define class. Flower classification is a challenge task due to the wide range of flower species, which have a similar shape, appearance or surrounding objects such as leaves and grass. In this project proposes the classification of flower images using a powerful convolutional neural networks (CNN) algorithm. A flower image database with images is considered for the experimentation. The entire database is categorized into classes a CNN is used to image classification method. The CNN training is initiated in three classes and the validation is carried out on all the datasets and tested with flower image data to obtain better accuracy to flower classification.

Flower image classification, CNN (Convolutional neural network, ReLu activation function.

"ACCURACY ENHANCEMENT OF FLOWER CLASSIFICATION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 8, page no.e903-e908, August-2024, Available :http://www.jetir.org/papers/JETIR2408496.pdf

"ACCURACY ENHANCEMENT OF FLOWER CLASSIFICATION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 8, page no. ppe903-e908, August-2024, Available at : http://www.jetir.org/papers/JETIR2408496.pdf