Food scarcity : An Alarming Situation and importance of AI
Food security is becoming one of the problematic concerns due to world population expansion; which is estimated to reach 10 billion people by 2050.The world faced a stark inflection point in 2024, as the continued rise in the number of people facing crisis-to-catastrophic levels of acute food insecurity meets sharp reductions in funding for humanitarian assistance. The 2025 Global Report on Food Crises (GRFC), released May 16, reports that 295.3 million people across 53 countries/territories faced acute food insecurity in 2024. This represents a tripling of the number of people facing acute hunger since 2016 and a doubling since 2020
Achieving zero hunger is a huge task and humanitarians need to use every advantage that is available to them. AI can be an incredibly important tool for tackling hunger smarter and faster – but it can’t end hunger on its own. However, by combining the best technology with the best academic research, and investing money behind it, AI – with a strong human element supporting – can play a massive role in creating a hunger-free world.
Curious about how AI is transforming the food market? You’re in the right place! In this article, we’ll explore cutting-edge AI techniques that boost food quality and optimize production—insights every food enthusiast and professional should know. But before we dive in, let’s break down some key technical terms that will make it easier to understand the innovations ahead.
Definition of technical terms:
Near Infrared Spectroscopy (NIRS)
A technique that shines near-infrared light on a food item to measure things like moisture, sugar, fat, or protein without touching or damaging it.
Computer Vision System (CVS)
A system that uses cameras and AI to “see” objects. It can check product quality, count items, detect defects, or identify fruits and vegetables by analyzing images.
Artificial Neural Network (ANN)- The machine brain
A computer model inspired by the human brain. It learns patterns from data and can predict outcomes, such as demand forecasting or quality grading.
Lot tracking :
refers to assigning a unique identifier (batch or lot number) to a group of products manufactured under the same conditions. This helps businesses trace products back to their origin in case of quality concerns, recalls, or contamination risks.
Fuzzy Logic Technique (FLT)
FLT is widely used in the industry because of its ease of use and quick and precise problem-solving capabilities. By controlling human reasoning in linguistic terms, FLT has been used in the food business for food modeling, control, and classification as well as for solving food-related issues. FLT could analyze factors like temperature fluctuations during transport, humidity, and ethylene levels to predict the remaining shelf life of fruits and vegetables more accurately than traditional methods. FLT can be used in sensory evaluations of bread or cakes to assess texture, flavor, and appearance, integrating subjective feedback from panels into quantifiable data for product development. Fuzzy analysis of the sensory properties of food products at various stages of unit operations can be utilized to optimize processing steps based on the desired product characteristics. fuzzy modeling was employed to optimize process parameters, such as soaking time, cooking time, frying temperature, and raw material properties (specifically slice thickness) in the production of taro chips.
Combination of AI with external sensors
In the food industry, artificial intelligence (AI) is frequently combined with external sensors for real-time detection. These sensors include near infrared spectroscopy (NIRS), computer vision systems (CVS), electronic nose (E-nose), electronic tongue (E-tongue), and machine learning (ML). This allows for real-time detection and faster, more accurate results. Over the past few years, the food sectors have demonstrated a number of ways to integrate these sensors with artificial intelligence techniques . E-nose, was developed to detect flavors or odors similarly to a human nose. It is made up of a variety of electronic chemical sensors that can identify both straightforward and complex smells .E-nose has been utilized in gas sensing applications where it is necessary to analyze individual components or mixtures of gases or vapors. . Furthermore, it is crucial for maintaining product quality control in the food business. E-tongue, which is often referred to as “a multisensory system,” has a number of low-selective sensors that are available. The signal is processed using sophisticated mathematical techniques based on pattern recognition (PARC) and multivariate data procession. For instance, E-tongue can be used to separate different kinds of chemical compounds in liquid phase samples. E-tongue has been used to detect the umami taste in mushrooms, detect the aging of beverage flavor, and evaluate the bitterness of liquids or dissolved substances. The study introduces a novel sensor-based system for monitoring and analyzing food spoilage. The proposed device can extend the shelf life of food items and prevent spoilage by continuously tracking food quality. It alerts users through voice commands or a display and provides notifications about the predicted time remaining before spoilage occurs. The device demonstrated an accuracy of 95 % in its predictions.
ANN-The Machine Brain
Another artificial intelligence component that is frequently used in the food business is ANN. Synaptic weights, or the connections between neurons, are what allow an artificial neural network (ANN) to learn and become knowledgeable, much like the human brain claims that ANN is versatile, adaptive, and relevant to a variety of issues and circumstance. It is reported that, despite the requirement for modifications, ANN is capable of modeling the majority of non-linear systems and is flexible enough to adapt to new circumstances.
ANNs are used to predict optimal baking times and temperatures for various breads and cakes. By examining data on dough composition, oven temperature, and humidity, the network ensures consistent quality while reducing energy usage. For dairy products like milk or yogurt, ANNs can forecast shelf life by considering factors like microbial growth, temperature changes, and packaging materials, allowing manufacturers to manage storage and distribution more effectively. Moreover, ANNs can predict the flow behavior and texture of chocolate based on its composition, such as cocoa butter content, and processing conditions like temperature, helping to optimize production and maintain texture consistency
Machine learning (ML)
is known to be the subset of AI . Ordinary least square regression (OLS-R), stepwise linear regression (SL-R), principal component regression (PC-R), partial least square regression (PLS-R), support vector regression (SVM-R), boosted logistic regression (BLR) and random forest regression (RF-R) are a few of the machine learning (ML) techniques used in the food industry. Studies have indicated that the application of machine learning (ML) has aided in decision-making, reduced the cost of sensory evaluation, and improved corporate strategies to better meet the needs of users .
Machine learning plays a pivotal role in the food industry by enhancing various processes such as predictive maintenance, food safety, and quality control. It helps optimize supply chains by forecasting demand and managing inventories, while also aiding in product development by analyzing ingredient interactions and consumer Lot Tracking in Food & Beverage Inventory Management
Lot Tracking is essential in food and beverage inventory systems, ensuring full traceability from production to consumer.
Benefits of Lot Tracking
- Ensures adherence to food safety regulations like HACCP and FDA.
- In case of contamination, affected products can be quickly removed from the supply chain.
- Helps businesses monitor product performance over time.
- Minimizes costs by isolating affected batches instead of discarding entire inventories.
Best Practices for Lot Tracking
- Use Inventory Management Software Implement a Warehouse Management System (WMS) that supports batch and lot tracking to maintain detailed records of inventory movements.
- Integrate with Barcoding & RFID Systems Scanning lot numbers during receiving, storing, and shipping ensures real-time visibility and accuracy in inventory control.
- Regularly Audit & Verify Lots Conduct cycle counts and audits to ensure lot numbers match system records, preventing misplacement or errors.
- Enable Supplier Collaboration Ensure suppliers provide detailed lot information for incoming shipments, allowing for seamless tracking throughout the supply chain.
Frequently Asked Questions (FAQs)
1. What is food scarcity and why is it increasing?
Food scarcity refers to the insufficient availability of safe, nutritious food for the global population. It is increasing due to rapid population growth, climate change, geopolitical conflicts, supply chain disruptions, and reduced humanitarian funding.
2. How serious is the global food insecurity situation today?
According to the 2025 Global Report on Food Crises, 295.3 million people in 53 countries faced crisis-to-catastrophic levels of acute food insecurity in 2024—a threefold increase since 2016 and double since 2020.
3. Can AI alone solve world hunger?
No. AI is a powerful tool, but it cannot end hunger on its own. When combined with strong human oversight, academic research, and strategic investment, AI can significantly improve food forecasting, production efficiency, distribution, and waste reduction.
4. How does Near-Infrared Spectroscopy (NIRS) help the food industry?
NIRS shines near-infrared light on food items to measure moisture, sugar, fat, and protein levels without destroying the sample.
It helps in:
- Quality grading
- Authentication and fraud detection
- Monitoring ripeness and freshness
5. What is a Computer Vision System (CVS) in food processing?
A CVS uses cameras and AI to “see” and analyze food items.
It is used for:
- Detecting defects
- Checking size and shape
- Counting items
- Identifying fruits or vegetables on processing belts
6. What is an Artificial Neural Network (ANN) and how is it used in food production?
ANN is a machine-learning model inspired by the human brain.
Applications include:
- Predicting baking time and temperature
- Forecasting shelf life of dairy products
- Optimizing chocolate texture and flow
- Modeling complex non-linear food processing systems
7. How does Fuzzy Logic Technology (FLT) improve food quality?
FLT mimics human reasoning and handles uncertainty. It is used to:
- Predict shelf life during transportation
- Analyze temperature, humidity, and ethylene levels
- Evaluate sensory attributes like taste, texture, and appearance
- Optimize production parameters such as frying temperature, soaking time, and thickness in snacks like taro chips
8. What are E-nose and E-tongue systems?
E-Nose: Detects odors using electronic chemical sensors.
Uses:
- Flavor profiling
- Gas detection
- Identifying spoilage
- Quality control of beverages, dairy, meats
E-Tongue: Detects liquid taste using multisensory arrays.
Uses:
- Measuring sweetness, bitterness, umami
- Beverage aging analysis
- Pharmaceutical and food formulation
9. How accurate are AI-based spoilage prediction devices?
Recent studies show sensor-AI systems predicting food spoilage with up to 95% accuracy, helping reduce waste and extend shelf life through timely alerts.
10. What is the role of Machine Learning (ML) in the food industry?
ML techniques—like SVM, Random Forest, OLS Regression—help with:
- Predictive maintenance of machinery
- Food safety monitoring
- Demand forecasting
- Inventory optimization
- Product formulation and sensory data analysis
11. Why is lot tracking important in the food and beverage industry?
Lot tracking assigns batch numbers to products to ensure full traceability.
Benefits include:
- Compliance with HACCP, FDA, and global food safety laws
- Rapid, targeted recalls
- Reduced financial loss from contamination
- Understanding product performance over time
12. What technologies improve lot tracking accuracy?
Effective technologies include:
- RFID tags
- Barcoding systems
- Cloud-based Warehouse Management Systems (WMS)
- IoT sensors for cold chain tracking
13. How do companies ensure effective lot tracking?
Best practices:
- Use inventory management software
- Integrate barcoding/RFID scanning
- Conduct regular audits and cycle counts
- Collaborate with suppliers for accurate batch documentation
14. What is the future of AI in the food industry?
AI will increasingly support:
- Autonomous food inspection
- Smart agriculture and vertical farming
- Precision dosing in food formulation
- Fully automated supply chain traceability
- Intelligent packaging that detects spoilage
15. How does AI help reduce food waste?
AI reduces waste through:
- Spoilage prediction
- Demand forecasting
- Optimizing cooking times and energy use
- Quality monitoring during storage and transport
Image Credit: Shutter Stocks
