• Mrs.Uzma I Satteekar
• Ms. Tarannum B Kodekar
• , Ms. Swaleha R Kolhar

Grading and classification of fruits is based on observations and through experiences. The system utilizes image-processing techniques to classify and grade quality of fruits. Two-dimensional fruit images are classified on shape and colour based analysis methods. However, different fruit images may have similar or identical colour and shape values. Hence, using colour or shape features analysis methods are still not effective enough to identify and distinguish fruits images. Therefore, we used a method to increase the accuracy of the fruit quality detection by using colour, shape, and size-based method with a combination of artificial neural network (ANN). Proposed method grades and classifies fruit images based on obtained feature values by using cascaded forward network. The proposed system starts the process by capturing the fruit’s image. Then, the image is transmitted to the processing level where the fruit features like colour, shape and size of fruit samples are extracted. After that, by using artificial neural network, fruit images are going through the training and testing. In this proposed paper neural network is used to detect shape, size and colour of fruit and with the combination of these three features, the results obtained are very promising.

ANN, Tensor Flow, Sorting, Grading, Fruit images

"Fruit Quality Assessment and Sorting System ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.355-364, June 2019, Available :http://www.jetir.org/papers/JETIR1907807.pdf

"Fruit Quality Assessment and Sorting System ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp355-364, June 2019, Available at : http://www.jetir.org/papers/JETIR1907807.pdf