Artificial Intelligence in food technology
- 1. AIIN Food Technology Presented to :-M.Tech (Food Eng. & Tech.)
- 2. Industrial Revolution Artificial Intelligence Market size of AI Kinds of AI Terminology with examples Application in food Industry Development of AI model Advantages and Disadvantages of AI Future Outlook Case Study ROADMAP
- 3. Industrial Revolution 1st Industrial Revolution 18th century Steam engine 2nd Industrial Revolution 19th -20th century Electric power 3rd Industrial Revolution 1980-2016 Internet age 4th Industrial Revolution Happening now Cyber physical System (AI, IoT, Automation)
- 4. • An artificial creation of human-like intelligence • To think and behave like humans and copy their actions. • Objective: learning, analyzing, and perception. • Types: • Strong AI: • To carries out more complex tasks. • Self-driving cars and hospitals. • Weak AI: • Do simple and single task-oriented. • Video games, Amazon’s Alexa, Apple’s Siri. Artificial intelligence
- 5. Emerging tech 17% Non IT sector 23% IT& KPO/BPO 60% Emerging tech Non IT sector IT& KPO/BPO Market size by types of sector 9% 11% 14% 15% 23% 28% 0% 5% 10% 15% 20% 25% 30% Indian AI Market size by Non IT sector Indian AI Market size by type of sector Source: Analytics India Magazine (2022)
- 6. With AI 38% Without AI 62% Adaption of AI in FMCG Top AI Users in India (FMCG Sector) Source: Analytics India Magazine (2022)
- 7. Artificial intelligence Machine learning Planning Robotics Vision Speech Recognition Expert System Natural Language Processing Deep learning Supervised Unsupervised Content extraction Classification Machine translation Question answering Text Generation Image Recognition Machine Vision Speech Text
- 8. MACHINE LEARNING (ML) • Subset of artificial intelligence that is based on the idea that the system can gain self- knowledge from data that is fed into it. • Identify the patterns and take decisions with minimal/no interference from human beings. • Computers are learned by self as we humans do.
- 13. NEURAL NETWORK Artificial neural network Convolutional neural network Example: Sorting of Apple
- 14. Output Input Machine learning Deep learning Feature extraction is the method for creating a new and smaller set of feature that capture most of the useful information of raw data.
- 15. Supervised machine learning Unsupervised machine learning Deep learning Example: Meat Quality testing
- 16. Machine learning • The ANN model was able to classify the chemical components in the beer with a high overall accuracy of 95% (Claudia Gonzalez et al., 2017). • To classify and evaluate the quality of different types of biscuits with an accuracy up to 99% (De Sousa Silva et al., 2020). • The system is able to detect the maturity of the mangoes based on their quality attributes (Pise & Upadhye., 2018)
- 17. Machine Vision Image Recognition
- 18. Expert System • Knowledge-based expert system: White winemaking during the fermentation process supervision, control, and data recovery software. • Web-based ES application: To calculate the nutritional value of the food for the users. • To monitor and forecast the product quality in the production process Nidhi Rajesh Mavani et al., 2022 A piece of software which uses database of expert knowledge to offer advice or make decisions such areas as medical diagnosis , account ,coding , games, etc.
- 19. Adaptive Neuro Fuzzy Inference System Fuzzy Logic • To rank the sensory attributes of aromatic foods packed in films made from corn starch. • Decide the least number of rolling steps based on the quality of the dough which improves the sheeting process of the dough. Nidhi Rajesh Mavani et al., 2022
- 20. INTERNET OF THINKS IoT device Cloud Supervisor E.g. Maintaining flow rate of liquid ingredients in beverage industry. Nidhi Rajesh Mavani et al., 2022
- 21. SEGMENTS IN FOOD INDUSTRY Agriculture Food Technology & Processing Supply Chain Marketing
- 22. APPLICATION OF AI IN AGRICULTURE Soil moisture estimation Arsenic level monitor weather forecasting Crop nutrient deficiency detection Crop health monitoring Optimal irrigation estimation Scientific crop calendar Land area mapping
- 23. APPLICATION OF AI IN AGRICULTURE Crop Disease Detection: Classification Recommends type and amount of pesticide required Camera fitted drone Algorithms Leaf detection disease detection Crop nutrient deficiency Detection: Soil sensor Nutrient data stored in database ANN Deficiency Detection Recommends type and amount of fertilizer
- 24. APPLICATION IN FOOD PROCESSING Sorting fresh produce: Sensor-based optical sorting • Combination of X-ray, NIR, Spectroscopy, laser, camera and unique machine learning algorithm. • Sorting on the basis of size, shape and colour. • TOMRA uses AI: to analyse the different aspects of F&V for Sorting. • E Tongue: to quantify taste and smell as actionable data. • Electronic nose: to distinguish food’s aromas and odours. • Kewpie: to detect defective ingredients during processing. • Qcify: Use machine vision to classify dry fruits. Nidhi Rajesh Mavani et al., 2022 Chidinma-Mary-Agbai., 2020
- 25. AI enable sensor in fruit sorting AI in the detection of defective ingredients Optical sorting Of potato
- 26. Food safety compliance monitoring • Fujitsu (a Japanese company): hand washing monitor • Key advantages: • Reducing the need for visual checks. • To increase the speed and accuracy • AI-enabled cameras: to ensure food safety compliance • Object-recognition and facial-recognition software: check whether workers adopt good personal hygiene or not and to extract images of the screen for review in case of a violation. Hand washing monitor Chidinma-Mary-Agbai., 2020
- 27. Effective cleaning in place systems • To optimize the cleaning process by using Ultrasonic sensing Optical fluorescence imaging • It measures food residue and microbial debris in a piece of equipment. Ultrasonic sensing Chidinma-Mary-Agbai., 2020
- 28. New Product Development • AI technology uses machine learning and predictive algorithms • Ex: Coca-Cola’s “Cherry Sprite’’, Kellog’s “Bear-Naked-Custom”. • IBM research recommending new seasoning formulas, drawing on the existing proprietary data. Ai in food waste: • To understand overproduction and allocate the correct resources • To control the number of production materials used in correlation with weather forecasts • To create the maximum amount of product needed for distribution. • Food-tracking app: Reduction of waste as unacceptable crops can be sold before they become useless. Chidinma-Mary-Agbai., 2020 Nidhi Rajesh Mavani et al., 2022
- 29. • Lasers to hit chips and then listen to the sounds coming off the chip with the help of algorithm determine the chip texture to automate the quality check for Frito-Lay’s chip processing systems. STUDY ON USE OF AI • A machine learning model that could be used with a vision system to be able to predict the weight of potatoes being processed. This led to considerable savings for the company i.e. $300,000 per line • PepsiCo gains a better sense of what customers are interested. For the launch of Mountain Dew Rise Energy, PepsiCo determined which consumers would be more likely than average to enjoy the drink, and then narrowed in further to determine a core target.
- 30. Application of AI in the Supply chain Supplier Selection Natural Language Processing Warehouse Management Supply Chain Planning Operational Procurement
- 31. Application of AI in the Marketing • Smart bidding • Micro-moment targeting • Responsive ads • Performance analysis • Dynamic search ads • Price optimization • Account management Applications of AI AI-powered customer insights Sales forecasting Content generation AI-enhanced PPC advertising Dynamic pricing Automated image recognition Ensure data quality and privacy
- 32. CASE STUDY Predicting Fruit’s Sweetness Using Artificial Intelligence Image Acquisition RGB values Sweetness prediction Classification algorithm • KNN • Tree • Support Vector Machines • Neural Network • Logistic Regression (To train the model) Results by different model Fruits Good sweetness: 9 high sweetness: 33 Very high sweetness: 8 • AI can predict the sweetness of orange fruits • Red color and sweetness of orange fruits are interrelated Al-Sammarraie et al., 2022
- 33. Flowchart for developing AI model
- 38. Advantages & Disadvantages of artificial intelligence
- 39. • AI has been playing a major role in the food processing industry for various intents such as for modeling, prediction, control tool, food drying, sensory evaluation, quality control, and solving complex problems in the food processing. • It minimizing human error and reduce waste. • AI is able to enhance the business strategies due to its ability in conducting the sales prediction and allowing the yield increment. • AI encourage researchers and industrial players to venture into the current technology that has been proven to provide better outcome in future. Future Outlook
- 40. • Claudia Gonzalez FRV, Sigfredo F, Damir T, Kate H, Dunshea (2017) Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and machine learning algorithms. J Sci Food Agr 1–39. • De Sousa Silva M, Cruz LF, Bugatti PH, Saito PTM (2020) Automatic visual quality assessment of biscuits using machine learning. In L. Rutkowski, M. Scherer Rafałand Korytkowski, W. Pedrycz, R. Tadeusiewicz, & J. M. Zurada (Eds.), J Artif Intell Soft (pp. 59–70). Springer International Publishing. • Pise D, Upadhye GD (2018) Grading of harvested mangoes quality and maturity based on machine learning techniques. 2018 Int Conf Smart City Emerg Technol ICSCET 2018 1–6. https://doi.org/10.1109/ICSCET.2018.8537342 • Nidhi Rajesh Mavani1 · Jarinah Mohd Ali1 · Suhaili Othman1,2 · M. A. Hussain3 · Haslaniza Hashim4 · Norliza Abd Rahman. Application of Artificial Intelligence in Food Industry—a Guideline., Food Engineering Reviews (2022) 14:134–175 Reference