Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms
Abstract
:1. Introduction
- Augmentation methods have been applied to enhance the size of the dataset.
- Developed a deep CNN to identify and classify bananas’ maturity range.
2. Related Work
3. Data Collection
4. Proposed Methodology
4.1. AlexNet
4.2. Proposed CNN Model
5. Results and Discussion
6. Conclusions and Future Work
Author Contributions
Funding
Conflicts of Interest
References
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Year and Ref. | Fruits | Applications | Techniques | Remarks and Result |
---|---|---|---|---|
2022 [7] | Mango | Fruit Grading and Automatic Classification | CNN, VGG16, InceptionV3 | To stop the spoilage of this seasonal fruit and to remove the manual process. Classification accuracy was 99.2%, and grading accuracy was 96.7% |
2021 [3] | Banana, Apple, and Orange | Ripeness Identification and Maturity Classification | CNN | To reduce harvest losses. Provides good-quality fruits to farmers and people who used this system. Training accuracy was 99.46%, and testing accuracy was 99.6% |
2021 [5] | Apple | Quality Identification and Maturity Classification | CNN | For speed and precise apple fruit grading. Achieved high accuracy for grading on maturity level. Grading accuracy was 98.98% |
2021 [6] | Banana | Fruit Recognition and Classification | CNN, Alex-Net | To help industrial applications and automate the process of recognition and classification. Classification accuracy was 96.98% |
2021 [16] | Banana | Ripeness Classification | CNN | To increase farmers’ income by reducing harvesting loss. To obtain a good-quality banana. Using a pre-trained model for ripeness classification. Classification accuracy was 96.18% |
2021 [15] | Banana | Freshness Detection | CNN, GoogLeNet | Providing fresh fruit to customers and automating this task. Bananas are good for bone health, weight loss, heart, and to prevent cancer. Detection accuracy was 98.02% |
2021 | Strawberry | Quality Evaluation | Alex-Net | To obtain worldwide cultivated quality strawberry fruit. AlexNet was the best-suited algorithm for this work. Accuracy was 95.75% |
2021 | Tomato | Maturity Grading | Alex-Net | To reduce pre- and post-harvesting loss. Accurately and efficiently recognized the maturity of tomato. Detection Accuracy was 100% |
Title 1 | Original Dataset | Augmented Dataset | Fruit 360 Dataset | |||
---|---|---|---|---|---|---|
Training | Testing | Training | Testing | Training | Testing | |
Ripe (0) | 490 | 210 | 4410 | 1890 | 460 | 196 |
Unripe (1) | 490 | 210 | 4410 | 1890 | - | - |
Overripe (2) | 490 | 210 | 4410 | 1890 | 460 | 196 |
Total Images | 1470 | 630 | 13,230 | 5670 | 920 | 392 |
2100 | 18,900 | 1312 |
Layers (Type) | Image Size | Filters | Filter Size | Pooling Size | Output Shape | Params# |
---|---|---|---|---|---|---|
Conv2d | 227 × 227 | 32 | 3 × 3 | - | (None, 225, 225, 32) | 896 |
Max pooling2d | 112 × 112 | - | - | 2 × 2 | (None, 112, 112, 32) | 0 |
Conv2d_1 | 112 × 112 | 64 | 3 × 3 | - | (None, 110, 110, 64) | 18,496 |
Max_pooling2d_1 | 56 × 56 | - | - | 2 × 2 | (None, 55, 55, 64) | 0 |
Conv2d_2 | 56 × 56 | 64 | 3 × 3 | - | (None, 53, 53, 64) | 36,928 |
Flatten | Fully Connected Layer | (None, 179776) | 0 | |||
Dropout | 20% | (None, 179776) | 0 | |||
Dense | “Units = 64, Activation = relu” | (None, 64) | 11,505,728 | |||
Dense_1 | “Units = 3 (Categorical), Activation = softmax” | (None, 3) | 195 |
Datasets | CNN | AlexNet | ||
---|---|---|---|---|
Training | Validation | Training | Validation | |
Original Dataset | 99.18% | 98.25% | 99.18% | 81.75% |
Augmented Dataset | 99.42% | 99.36% | 99.80% | 99.44% |
Fruit 360 Dataset | 100% | 81.96% | 100% | 81.75% |
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Aherwadi, N.; Mittal, U.; Singla, J.; Jhanjhi, N.Z.; Yassine, A.; Hossain, M.S. Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms. Electronics 2022, 11, 4100. https://doi.org/10.3390/electronics11244100
Aherwadi N, Mittal U, Singla J, Jhanjhi NZ, Yassine A, Hossain MS. Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms. Electronics. 2022; 11(24):4100. https://doi.org/10.3390/electronics11244100
Chicago/Turabian StyleAherwadi, Nagnath, Usha Mittal, Jimmy Singla, N. Z. Jhanjhi, Abdulsalam Yassine, and M. Shamim Hossain. 2022. "Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms" Electronics 11, no. 24: 4100. https://doi.org/10.3390/electronics11244100
APA StyleAherwadi, N., Mittal, U., Singla, J., Jhanjhi, N. Z., Yassine, A., & Hossain, M. S. (2022). Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms. Electronics, 11(24), 4100. https://doi.org/10.3390/electronics11244100