# Interactive Deep Learning for Shelf Life Prediction of Muskmelons Based on an Active Learning Approach

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## Abstract

**:**

## 1. Introduction

- guidance and sample efficient improvement of the DL algorithm through a human-in-the-loop setting,
- potential reduction of the required samples for training DL algorithm and
- introduction of k-DPP as a diverse sampling method with improved capture of the underlying data distribution compared to uncertainty-related metrics.

## 2. Materials and Methods

#### 2.1. Data Collection

#### 2.2. Experimental Design

#### 2.3. Related Fields of Research

#### 2.4. Dataset

#### 2.5. Active Learning Framework

#### 2.6. Choice of Acquisition Function

**Random acquisition**represents the baseline where instances are sampled stochastically without the heuristic calculation of a metric.**Least Confidence**samples the instances where the algorithm is least confident about the label and is calculated by$${X}_{LC}^{\mathcal{U}}=\underset{\mathbf{x}}{argmax}(1-{P}_{\theta}\left(\widehat{y}\right|\mathbf{x}))$$**Margin sampling**calculates the margin between the most probable and second most probable classes represented by $\widehat{{y}_{1}}$ and $\widehat{{y}_{2}}$:$${X}_{MS}^{\mathcal{U}}=\underset{\mathbf{x}}{argmin}({P}_{\theta}\left({\widehat{y}}_{1}\right|\mathbf{x})-{P}_{\theta}\left({\widehat{y}}_{2}\right|\mathbf{x}))$$**Maximum entropy**samples instances yielding the maximum entropy by determining$${X}_{E}^{\mathcal{U}}=\underset{\mathbf{x}}{argmax}\sum _{i}{P}_{\theta}\left({y}_{i}\right|\mathbf{x})\xb7log({P}_{\theta}\left({y}_{i}\right|\mathbf{x}).$$**Ratio of confidence sampling**is very closely related to margin sampling where the two scores with the highest probable classes are determined as a ratio instead of the difference.**Bayesian Active Learning of Disagreement (BALD)**: The goal of BALD is to maximise the mutual information $\mathbb{I}$ between the prediction and model posterior such that, under the prerequisite of being Bayesian, BALD can be stated as$$\mathbb{I}(y;\theta |\mathbf{x},\mathcal{L})=\mathbb{H}\left(y\right|\mathbf{x},\mathcal{L})-{\mathbb{E}}_{p\left(\theta \right|\mathcal{L})}\left(y\right|\mathbf{x},\theta ,\mathcal{L})\left[\mathbb{H}\left(y\right|\mathbf{x},\theta ,\mathcal{L})\right]$$**$\mathbf{k}$-Determinental Point Processes ($\mathbf{k}$-DPP)**: As an diversity-based approach, k-DPP takes an exploratory approach by sampling based on the DPP conditioned on the modelled set being of cardinality k [63] (We like to note that we prefer the use of k-DPP over traditional DPP due to the introduced bias into the modelling of the content. Parameter k allows to take a direct influence on the diversity by taking into regard the repulsiveness of the drawn samples—or in other words—the magnitude of the negative correlation between samples).

## 3. Results

#### 3.1. Model Training

#### 3.2. Training Setting for Active Learning

#### 3.3. Diverse k-DPP Sampling

#### 3.4. Human-in-the-Loop Performance

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

AL | active learning |

ART | Acoustic Resonance Testing |

CNN | Convolutional Neural Network |

DL | Deep Learning |

DPP | Determinental Point Process |

SCNR | Single Channel Noise Reduction |

## Appendix A

#### Appendix A.1. Images Galia Melon

**Figure A1.**State of the a fruit after (

**a**) arrival and after a shelf life of (

**b**) one week, (

**c**) two weeks and (

**d**) three weeks.

#### Appendix A.2. Overview of Preselected Parameters

Objective | Parameters | Value |
---|---|---|

weight span [g] | 837.2–1555.3 | |

difference of the room temperature [°C] | 18.4–22.9 | |

Dataset | difference room humidity [%] | 20.77–49.37 |

measurements on shelf life s | {0, 7, 10, 15, 17, 62} | |

Augmenation types | horizontal flipping, vertical flipping | |

Preprocessing | Gain filter parameter $\alpha $ | 9 |

Gain filter parameter $\beta $ | 45 | |

batch size | 64 | |

learning rate | 0.005; exponential decay | |

optimisation algorithm | SGD | |

Hyperparameters | momentum | 0.9 |

nestorov | activated | |

clipping norm | 1.0 | |

gradient clipping | 0.5 | |

initial set ${\mathcal{L}}_{0}$ | 30 | |

Active Learning | initial set $\mathcal{U}$ | 50 |

k in k-DPP | {1, 40, 200} |

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**Figure 1.**Spectrogram of a sample (

**a**) before processing and (

**b**) after processing based on spectral subtraction by using a SCNR algorithm with parameters $\beta =45$ and $\alpha =9$. As shown in panel (

**b**), a reduction of the low frequency noise is achieved without degrading the signal of interest. A side effect is the generation of artefacts by masking new frequencies at different time periods. As the signal of interest is of short period, the artefacts have little impact. The green box in panel (

**a**) resembles the crop towards the signal of interest.

**Figure 3.**Visualisation of the DL architecture. The information extraction of the amplitude and phase results based on four convolutional modules with descending filter size towards the output.

**Figure 4.**Active learning curves with error bounds for (

**a**) accuracy, (

**b**) loss, (

**c**) recall and (

**d**) precision.

**Figure 5.**Active learning for k-DPP at values $k\in \{1,40,200,400\}$. Results are averaged over five iterations shown with error bounds for (

**a**) accuracy, (

**b**) loss, (

**c**) precision and (

**d**) recall.

**Table 1.**Overview of the number of data within the dataset split in into training, test and validation sets. Based on the summation of all classes ${\mathcal{X}}_{train}$ consists of 0.6, ${\mathcal{X}}_{test}$ of 0.25 and ${\mathcal{X}}_{val}$ of 0.15 based of the total amount of data.

Class | ${\mathcal{X}}_{\mathit{t}\mathit{r}\mathit{a}\mathit{i}\mathit{n}}$ | ${\mathcal{X}}_{\mathit{t}\mathit{e}\mathit{s}\mathit{t}}$ | ${\mathcal{X}}_{\mathit{v}\mathit{a}\mathit{l}}$ |
---|---|---|---|

1 | 968 | 429 | 259 |

2 | 1086 | 422 | 228 |

3 | 582 | 231 | 163 |

4 | 1050 | 454 | 272 |

**Table 2.**Summary of the studied acquisition functions in comparison to the different metrics. Within the table we represent the average and the standard deviation for the results obtained after the last iteration. The best performing acquisition metric is highlighted in by bold caption.

Acquisition Function | Accuracy | Loss | Precision | Recall |
---|---|---|---|---|

BALD | 0.7098 | 0.6935 | 0.7361 | 0.6667 |

(0.1290) | (0.0228) | (0.0132) | (0.0131) | |

least confidence | 0.7174 | 0.6760 | 0.7531 | 0.6760 |

(0.0083) | (0.0132) | (0.0116) | (0.0103) | |

k-DPP | 0.7260 | 0.6747 | 0.7615 | 0.6504 |

(0.0107) | (0.0051) | (0.0093) | (0.0221) | |

margin sampling | 0.7391 | 0.7391 | 0.7742 | 0.6712 |

(0.0150) | (0.0139) | (0.0156) | (0.014) | |

ratio of confidence | 0.7283 | 0.7283 | 0.7596 | 0.6714 |

(0.1827) | (0.209) | (0.0164) | (0.0161) | |

random | 0.7135 | 0.7135 | 0.7509 | 0.6469 |

(0.0220) | (0.0247) | (0.0277) | (0.0162) |

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**MDPI and ACS Style**

Albert-Weiss, D.; Osman, A. Interactive Deep Learning for Shelf Life Prediction of Muskmelons Based on an Active Learning Approach. *Sensors* **2022**, *22*, 414.
https://doi.org/10.3390/s22020414

**AMA Style**

Albert-Weiss D, Osman A. Interactive Deep Learning for Shelf Life Prediction of Muskmelons Based on an Active Learning Approach. *Sensors*. 2022; 22(2):414.
https://doi.org/10.3390/s22020414

**Chicago/Turabian Style**

Albert-Weiss, Dominique, and Ahmad Osman. 2022. "Interactive Deep Learning for Shelf Life Prediction of Muskmelons Based on an Active Learning Approach" *Sensors* 22, no. 2: 414.
https://doi.org/10.3390/s22020414