Paper #1 - AI-enabled Efficient and Safe Food Supply Chain
Key Notes
- Predicting plant growth and tomato yield in greenhouses
- Optimizing energy consumption across large networks of food retail refrigeration systems
- Optical recognition and verification of food consumption expiry date in automatic inspection of retail packaged food
- Long Short-Term Memories (LSTM) are a variation of the Recurrent Neural Network (RNN) architecture
- Networks composed of LSTM units have been able to solve the gradient vanishing problem met in long-term time series analysis
- To achieve this, the LSTM structure contains three modules: the forget gate, the input gate and the output gate
- LSTM-based encoder-decoder models
- Attention mechanisms help to focus on feature segments of high significance
- Output Predictions can be derived using the conditional probability distribution of the input signal and of the previous samples of the output.
Yield Prediction
- Tomato crop growing in greenhouse environments is a dynamic and complex system
- A linear relationship between flowering rate and fruit growth
- Weekly yield fluctuations in terms of fruit size and harvest rate.
- The environmental data were collected on an hourly basis, while the yield on a weekly basis.
Food Retailing Refrigeration Systems
- Nemesyst system [60] has been capable of predicting which refrigerators to select and how long to turn them off, whilst maintaining food quality and safety
- In the experimental study the target was to predict the time (in seconds) until the refrigerator temperature rises from the point it is switched off until it breaches a food safety threshold
Quality Control in Retail Food Packaging
- Incorrectly labelled product information on food packages, such as the expiry date, can cause food safety incidents, like food poisoning.
- The Food Packaging Image dataset used next consists of more than 30,000 images classified in two categories (existing valid date and non-existing or non-valid date)
Paper #2 - Food Supply Chain and Business Model Innovation
- Food supply chain (FSC) consists of a chain of activities elaborating how a product is produced and delivered to the final consumers
- Farmers, processors, distributors, and retailers
Four main aspects of a business:
- value proposition, which refers to the products and services the business is providing
- value delivering, which implies the mechanisms the business is connected with its final customers to deliver the products and services to them
- value creation, points out the main activities which are necessary to create and deliver the values to the customer
- value capturing, which indicates the ways a business makes money through the value creation and delivering processes
Five strategies to innovate their business model:
- 1) innovating the value proposition,
- 2) reconsidering the value delivering mechanisms,
- 3) innovating the value creation processes,
- 4) providing new value capturing models, and
- 5) proposing a quite new business model.
Value Delivering - One of the most important issues in the FSC is food distribution, where cold chain management plays a vital role. Having a frozen storage with the risk of high-energy consumption and cool storage with the threat of bacterial decay is a dilemma the distributors in the food industry deal with
- Flight kitchen business model is quite similar to CVS convenience store (CVS) indirect delivery business
- Model where the only difference is the lower supply volume and fewer supply spots
Paper #3 - Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion
- We decompose demand into baseline and promotional demand and propose a hybrid model to forecast demand.
- CoV to measure the volatility of demand and propose appropriate forecasting models
- Pearson’s correlation between demand uplift only due to promotions and price
- Coefficient of variations (CoV) where promotion causes volatility over the entire demand series
- CoV by definition is the sample standard deviation divided by the sample mean
- Low volatility demand where CoV is smaller than 0.5
- Moderate volatility where CoV is greater than 0.5 and smaller than one
- High volatility where CoV is greater than one.
Paper #4 - Mathematical modeling on tomato plants: A review
- Crop variables
- Climatic conditions (air
- Temperature, CO2 concentration, humidity and
- Photosynthetically active radiation (PAR))
Paper #5 - Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments
- The environmental data were collected on an hourly basis, while the yield on a weekly basis. To deal with these data characteristics, we performed data augmentation, through interpolation of weekly data, resulting in daily data measurements
- Plant density was approximately 15 pots per 𝑚2, where every pot contained 3 cuttings
More Reads
- Solution of Large-Scale Supply Chain Models using Graph Sampling & Coarsening
- TRACEABILITY AS AN INTEGRAL PART OF SUPPLY CHAIN LOGISTICS MANAGEMENT: AN ANALYTICAL REVIEW
Keep Exploring!!!
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