Fruit Detection and Classification application Based on Machine Learning Techniques Framework
Keywords:
Detection, fruits, vegetables, descriptor, Deep Learning, Machine LearningAbstract
The paper presents a comprehensive framework for accurately and efficiently recognizing fruits and vegetables, addressing the challenge systematically. Various feature descriptors based on color and texture are examined. These descriptors capture different aspects of the fruits and vegetables, aiding in their accurate recognition. Otsu's thresholding is utilized for background subtraction, a crucial step to isolate the fruits and vegetables from their surroundings. All segmented images are used in this phase to extract relevant features. This step likely involves removing texture and color information, utilizing the chosen descriptors. The extracted features are used to train and classify fruits and vegetables. Two classifiers, C4.5 and KNN, are employed for this purpose. Various performance metrics such as Classification Accuracy (CA), precision, recall, F-measure, Matthews Correlation Coefficient (MCC), Precision-Recall Curve (PRC), and False Positive Rate (FPR) are used to evaluate the proposed system's performance for the recognition problem. C4.5 classifier achieves a CA value of 92.43%, indicating high accuracy in classifying fruits and vegetables. KNN classifier performs a slightly lower CA value of 89.58% but still demonstrates significant accuracy in classification.
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