Automatic Detection and Classification of Nutrients Deficiency in Fruit Based on Automated Machine Learning

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

Classification of Fruits using Deep Learning Algorithms

“…The statistical and Grey Level Co-occurrence Matrix features were extracted from the images. Yogesh et al, [16] introduced three primary steps such as model building, model testing, and model con guration. The apple and mangosteen fruits were analyzed using the CNN algorithm.…”