Automatic Detection and Classification of Nutrients Deficiency in Fruit Based on Automated Machine Learning
Yogesh1, Ashwani Kumar Dubey2, Rajeev Ratan3
1Yogesh*, Electronics & Communication, Amity University Uttar Pradesh, Noida, India.
2Ashwani Kumar Dubey, Electronics & Communication, Amity University Uttar Pradesh, Noida, India.
3Rajeev Ratan, Electronics & Communication, MVN University, Palwal, India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 1901-1909 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1029109119/2019©BEIESP | DOI: 10.35940/ijeat.A1029.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Machine learning-based classification and detection of surface defect of fruit involve manual feature identification and selection from input datasets. Deep learning discovers the useful features from the input data. This approach simplifies the training of the neural network and makes them faster. The selection of useful patterns from the fruit features results in better accuracy. The number of layers represents the depth of the model. Neural network provides learning to the model. As the dataset contains many features. It is obvious that all features are not relevant to the system. The proposed system learns from these features by identifying the pattern and select the relevant features. This is the most crucial phase of the machine learning to identify the appropriate features to make the system faster and accurate. In this paper, we propose solving fruit surface defect detection using Automated Machine Learning (AML). The outcome is the prediction of the fruit surface defect in terms of probability due to nutrient deficiency
Keywords: Automated machine learning, Surface defect, fruit classification, Convolutional Neural Network, fruit defect.