Agriculture
The Technologies Turning Food Waste Into an Opportunity
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As food production and related supply chains evolved from local farmers’ markets to advanced logistics that move goods across half the world into large supermarkets, food waste increased, even with refrigeration.
This is not only a financial loss or the waste of food resources that could be consumed by humans, but a heavy ecological burden as well.
“Beyond the direct loss of edible food, FLW is associated with substantial environmental burdens, including avoidable greenhouse gas emissions, land and water use, and disruption of circular economy ambitions”
Because technological advances and complex systems contribute to food waste, further improvements and new technologies could help solve the problem.
Blockchain and AI are often touted as potential methods to tackle the issue.
But a recent review by researchers affiliated with Universidad de Las Palmas de Gran Canaria in Spain, Qassim University and Shaqra University in Saudi Arabia, and Lusófona University in Portugal found that IoT-enabled monitoring and smart logistics currently have the strongest direct evidence for reducing spoilage risks. AI, consumer applications, and blockchain still require stronger validation under real-world operating conditions.
They published their findings in Trends in Food Science & Technology1, under the title “Digitalization pathways for food loss and waste prevention in agri-food supply chains: Current evidence, challenges and future directions”.
Understanding Food Waste
Food loss and food waste describe related but distinct problems. Following the terminology used by the researchers, food loss occurs between production and the point immediately before retail. Food waste occurs at the retail, food-service, and consumer stages.
Both can arise because food is perishable and modern supply chains involve numerous producers, processors, warehouses, distributors, retailers, and consumers. A disruption or miscalculation at any stage can leave otherwise usable food spoiled, rejected, overstocked, or discarded.
“Empirical studies highlight factors such as poor handling of perishable products, inadequate cold chains, supply chain interruptions, misaligned buyer–supplier agreements, and behavioral norms at downstream stages”
This leads to making food waste an outcome of routine practices rather than isolated failures. As a result, solving the issue requires integrated, system-level approaches that combine technological, organizational, and policy interventions.
Technologies For Solving Food Waste
Getting The Big Picture
Agriculture is currently undergoing the same paradigm shift as industry, the so-called “Agriculture 4.0” and “Industry 4.0”; integrating Internet of Things (IoT) devices, real-time sensor networks, and data platforms enables continuous monitoring of quality parameters and logistics conditions.
However, digital technologies are rarely adopted primarily to limit food waste. Across agriculture and the wider supply chain, investments are more commonly justified by productivity gains, operational efficiency, or cost reduction.
In addition, global action is limited by poor integration of upstream and downstream perspectives, with many published studies conceptual, pilot-scale, or context-specific.
To get a better overview of what technology actually works to reduce food waste, the researchers gathered scientific literature published from 2019 to 2026, using keywords like “food loss”, “food waste”, “FLW”, “food waste prevention”, etc., intersecting with keywords like “digitalization”, “digital technologies”, “Agriculture 4.0”, “Internet of Things”, “IoT”, etc.
This data gathering brought to light essential technologies deployed to fight food waste:
- IoT/RFID monitoring (Radio-frequency identification).
- Big data and cloud infrastructures.
- AI/ML (Artificial Intelligence / Machine Learning).
- Blockchain-enabled traceability.
- Mobile or web-based platforms.
How Can Technology Contribute
The causes of food waste change depending on the stage of the food supply chain:
- During primary production, losses are commonly associated with demand uncertainty, cosmetic quality standards, and weather-related variability.
- During postharvest handling and storage, spoilage stems from inadequate temperature and humidity control, infrastructure limitations, and insufficient monitoring.
- Processing operations may generate losses through inefficient scheduling, production changeovers, and off-specification batches.
- Distribution and retail are affected by forecasting errors, cold-chain disruptions, promotional strategies, and inventory imbalances.
- At consumer and food-service stages, the main causes are over-purchasing, inadequate meal planning, misunderstanding of date labels, and inappropriate portioning.
As such, different technologies contribute different improvements for individual stages of the food supply chain.
IoT-enabled real-time monitoring is great for detecting and recording deviations in temperature, humidity, or gas composition before product quality is compromised.
Big data platforms shine by integrating inventory, sales, and contextual information to improve demand forecasting and inventory control, reducing structural overproduction and excessive stock levels.
AI and ML algorithms can support routing optimization, dynamic pricing, quality grading, and remaining shelf-life prediction.
Blockchain-based traceability systems improve transparency, accountability, and coordination among supply-chain partners.
Consumer-oriented digital platforms provide inventory management, purchasing guidance, surplus redistribution, and behavioural feedback.
Assessing What Works in Real Life
While all these technologies can potentially help, their implementation and uses massively impact how effective they will be in real-world use cases.
The study found that regarding the primary production stage, it is unclear how huge an impact technology actually has on food waste.
“Relatively few studies directly quantify reductions in food loss, with most reported benefits inferred from improvements in crop productivity or resource-use.”
IoT-based storage and postharvest monitoring has some of the strongest evidence in the review. Field trials, pilots, and commercial deployments show that monitoring temperature, humidity, and other environmental conditions can reduce spoilage risks and preserve perishable products. However, quantified reductions remain context-specific, while infrastructure requirements, investment costs, and interoperability challenges continue to constrain adoption, particularly among SMEs and smallholder producers.
Predictive analytics and AI appear to have proven their accuracy in supporting yield prediction, disease detection, and remaining shelf-life estimation. However, here too, real-world efficiency has rarely been assessed.
“Most published studies evaluate predictive accuracy rather than directly measuring reductions in FLW, making it difficult to determine the real contribution of AI to waste prevention under commercial operating conditions”
For consumer-oriented digital technologies, the challenge is less technical and more social. For example, food-sharing platforms and surplus redistribution applications connect households, retailers, restaurants, and other food businesses with individuals willing to receive surplus food. In addition, environmental benefits from reduced food waste might be absorbed by subsequent changes in consumers’ behaviors.
“Analysis of more than 750,000 food-sharing transactions in the United Kingdom demonstrated that between 59% and 94% of the anticipated greenhouse gas benefits were offset when users redirected monetary savings towards the purchase of other goods and services, with comparable rebounds observed for land and water use”
Overall, this points out that IoT systems, including RFID tracking, have so far been the technology with the best track record of demonstrated impact on food waste.
Its only limitation is that deployment is still slow and at times costly. The lack of a shared standard and unified system between the many companies involved is also a problem that still needs to be solved.
Lessons could be taken by the food industry at large from cold-chain monitoring, as it represents one of the most mature digital applications for FLW prevention.
How Useful New Technologies Could Be
While the study brings to light that mostly IoT-based technologies are demonstrated to reduce food waste at scale, this is also simply the result of these technologies being older and mature, and having been implemented for a longer period of time.
Still, across all technology categories, a common pattern emerges: technologies directly controlling physical product conditions generally provide the strongest evidence of measurable FLW prevention.
In contrast, technologies optimizing information flows, including AI, big data analytics, and blockchain, frequently demonstrate improvements in forecasting, inventory control, and traceability. Some AI inventory applications have also reported reductions in waste, but the broader evidence still relies heavily on operational proxies rather than standardized measurements of food actually saved. The same can be said for technologies requiring large changes in social behaviors.
Another factor in improving these technologies’ performance is the need for unified data architectures and standards.
“Standardized event-based models such as GS1 EPCIS, together with common identifiers, APIs, middleware, and adapter layers, can reduce integration friction by allowing heterogeneous systems to exchange traceability and sensor data in a common structure.”
The situation can also be complicated when involving cross-border trade, as regulations and standards between different countries are likely to differ quite a lot.
“Differences in national regulations concerning data protection, cybersecurity, digital identity, and certification complicate cross-border implementation while reinforcing uncertainty regarding responsibilities, accountability, and trust among supply-chain actors”
Making Techs Successfully Reduce Food Waste
Ultimately, a strong factor in the adoption of technologies reducing food waste is the alignment of all actors’ interests.
Companies will always be more interested in improving productivity and reducing costs, and should be incentivized to reduce food waste through that lens. Improvement of digital infrastructure, connectivity, financial resources, and technical skills also helps greatly, as it reduces the cost of implementing these technologies.
At the consumer level, continued user participation rather than one-time technology adoption can be the main challenge, impacted by usability, trust, perceived usefulness, and integration into everyday routines.
Emerging technologies quickly growing in capacity and ease of use will likely play a bigger role in the future. For example, it is to be expected that AI, machine vision, and digital twins of facilities (farms, warehouses, food processing plants) will keep improving and start to provide greater increases in cost savings, yields, and reductions in food waste all at once.
It should also be noted that these technologies will likely not replace, but actually increase the demand for “older” IoT and RFID technologies, as they will provide the AIs with the constant and accurate data stream they need to perform optimally.
Investing In Supply Chain Traceability
Zebra Technologies Corporation
ZBRA Price Chart
Because supply chain technology relying on IoT is unlikely to be specific to the food industry, investing in that theme is likely best done by finding companies already providing this technology to other sectors.
Zebra Technologies (ZBRA ) provides barcode scanning, RFID, mobile computing, machine vision, environmental sensing, and inventory-management technologies.
Source: Zebra Technologies
By providing the traceability and real-time tracking of goods across the supply chain, Zebra is a key supplier that directly addresses the physical-data layer required for further progress in digitalization of physical economic activities: retail, healthcare, logistics, manufacturing, food production & distribution, etc.
The company’s products align with several of the most commercially mature applications discussed in the review, notably:
- Monitoring storage and transportation conditions.
- Tracking perishable inventory.
- Improving supply-chain visibility.
- Identifying products before expiration.
- Supporting better replenishment and allocation decisions.
While its sales were initially focused on North America, the company has also been growing its activities abroad, which now make up half of total sales. Retail and e-commerce have been the initial adopters of these technologies, but they are quickly being used by the manufacturing and logistics sectors.
Source: Zebra Technologies
Besides these sectors, healthcare is also a quickly growing market for Zebra. It was Q2 2026’s highest growth end market, with Urgent Care carrying the category’s growth.
Source: Zebra Technologies
With net sales growing by 20.4% year-to-year in Q2 2026, and adjusted EBITDA by 61.4%, the company is offering potential investors a strong growth profile. Contrary to many investments, the company is not likely to be directly threatened by the rise in AI capabilities (it benefits from it instead), but is not overly dependent on the AI boom continuing for its growth to continue, as demand for better traceability and efficiency is anyway growing.
This can make Zebra’s stock a good option to balance a portfolio that is either too little or too much exposed to pure AI stocks, by offering an upside if AI becomes ubiquitous, but also independent solid growth if AI somehow starts slowing down.
Latest Zebra Technologies Corporation (ZBRA) Stock News and Developments
Study Referenced
1. Esteban Pérez-García et al. Digitalization pathways for food loss and waste prevention in agri-food supply chains: Current evidence, challenges and future directions. Trends in Food Science & Technology. December 2026. Article: 106084. Volume 178. 10.1016/j.tifs.2026.106084
