Automatic Fruit Detection System using Multilayer Deep Convolution Neural Network

• Published in: 2021 International Conference on Computer Communication and Informatics (ICCCI) pp. 1 - 5
• Main Authors: Latha, R.S., Sreekanth, G.R., Suganthe, R.C., Geetha, M., Swathi, N., Vaishnavi, S., Sonasri, P.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 27-01-2021
• Subjects: Agriculture; Computational modeling; convolutional neural networks; Deep learning; Feature extraction; Fruit detection and recognition; Neural networks; Pipelines; Research and development; Support vector machines
• DOI: 10.1109/ICCCI50826.2021.9402513

Agriculture has become an important thing in everyday life. Among this, fruits are a great thing in every day life. Classification of fruits based on their accuracy is a decent approach to all the fruit sellers. There is many parallelism between apple and cherry and various kinds of similarities are present in many types of fruits, so the classification plays an important role. However, there are troubles in fruit classification using machine learning algorithms like Support Vector Machine (SVM) and Convolutional Neural Network(CNN). So, the methods of CNN, pooling layers and fully connected network have been applied to overcome the problems. The CNN and pooling layers have been applied to extract the features of the fruits. To expose this scheme, various fruits such as Apple, Blueberry, Cherry, Grape blue, Guava, Kiwi, Lemon, Papaya, Strawberry, Plum, Tomato and Mango are considered. By implementing this project, the accuracy of fruit classification is increased.