Evaluation of CNN based on Hyperparameters to Detect the Quality of Apples
Evaluation of CNN based on Hyperparameters to Detect the Quality of Apples
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : Deepali M Bongulwar, Vijay Prakash Singh, S. N. Talbar
|DOI : 10.14445/22315381/IJETT-V70I10P222|
How to Cite?
Deepali M Bongulwar, Vijay Prakash Singh, S. N. Talbar, "Evaluation of CNN based on Hyperparameters to Detect the Quality of Apples," International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 232-246, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I10P222
It is essential for the food industry that fresh goods are automatically categorized. Various kinds of fruits are in the market, making it difficult to classify them according to quality. Manual sorting and evaluation of agricultural goods are possible, but it is not definitive, time-consuming, subjective, costly, and environmentally sensitive. Thus, fast, accurate, effective and automatic methods need to be introduced for inspecting the quality and grading of fruit. Classification of the quality of fruit and, as a result, gradation is critical in the industry for the development of good quality food products and the highest grade fruits that can be offered in the market. This research develops an automatic fruit grading system to grade apples based on their external qualities. The flaws on the fruit's peel have been used to determine if the apple is fresh/good or rotten/bad. It has been demonstrated that convolutional neural networks (CNNs) are efficient in several agricultural applications. Therefore, CNN architecture is utilized to build and train the classification model. This study's objective is to determine the effectiveness of the proposed CNN model considering four hyperparameters like an optimizer, learning rate, number of epochs and batchsize for determining the quality of apples. The two benchmark datasets, 'Fruits Fresh and Rotten' (Dataset1) and 'FruitsGB' (Fruits Good/Bad) (Dataset2), are employed to analyze the performance of the model. Dataset1 consists of 1,693 fresh and 2,342 rotten apples, and dataset2 consists of 1000 good and 1000 bad apple images. The accuracy and computation time are utilized for the evaluation of the classification performance of the proposed CNN model. Initially, the model's accuracy is improved by changing batchsize and keeping hyperparameters like epochs constant, and the best results for each optimizer and all learning rates are found. The batchsize that produced the best results is chosen, and the model is reassessed by adjusting the number of epochs, optimizer, and learning rates. Finally, the best outcomes are obtained. The presented model has achieved 100% accuracy on dataset2 with the optimizers SGDM and ADAM and 99.31% and 99.70% for dataset1 with SGDM and ADAM, respectively. The results reveal that the model's accuracy lowers with the increase in the learning rate, and adding more epochs does not improve the accuracy. The performance categorization of the model is assessed using additional metrics like Precision, Recall, F1 and F2 score, MCC, and AUC. Thus, a score of 100% is achieved for all these metrics.The optimizer SGDM gives more good results than ADAM and optimizes faster. The acquired experimental findings show that the accuracy of the proposed model relies not only on the hyperparameters but also on the dataset used and its size.
Convolutional Neural Network, Deep Learning, Fruit quality, Fruits Fresh and Rotten dataset, Hyperparameters.
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