Recognition of Fruits Using Neural Classification Methods - Vanitha, K; Chellam, G. Heran (International Journal of Computer Sciences and Engineering, 2025)
Object recognition is emerging technology to detect and classify objects based in their characteristics. Fruit it is also a domain of object recognition and it is still a complicated task due to the various properties of numerous types of fruits. Different fruits have different shapes, sizes, color, textures and other properties. Tangerines and Madarin oranges have the same characteristics such as color, texture size, etc. Multi-feature extraction methods are based on supervised machine learning algorithms and image processing mechanisms. These algorithms are used to find a better fruit classification. Firstly, we pre-process the training sample of fruits images. The preprocessing is included a separating foreground and background, scaling and cropping and it reduce the dimension. So the processing is fast then, we extract features the fruit’s image, which includes color, texture and shape of the fruit image. Extracted features are then fitted into the neural classifier machine learning algorithm. This paper is obtained the results from the test sample is cross validated using machine learning network. The output obtained will give us the fruit that it has acknowledged