Abstract This review examines how Artificial Intelligence, Internet of Things, and Blockchain technologies enhance mycotoxin prevention, detection, and control in cereals and legumes by enabling real-time monitoring, predictive analytics, traceability, and improved decision-making across the food supply chain. This review employed a systematic literature search across databases of 264 papers through search using Scopus, Web of Science and PubMed. Keywords related to AI, IoT, blockchain, mycotoxins, cereals, and legumes were used to identify relevant studies published between 2000–2025. The review criteria were satisfied by 75 publications. Selected articles were screened, categorised, and critically analysed to synthesise technological applications, effectiveness, limitations, and emerging trends in mycotoxin prevention and control. Mycotoxin contamination in cereals and legumes threatens global food safety, health, and trade due to its toxic effects. Traditional detection and control approaches are slow, costly, and lack real-time capability. Emerging digital technologies – artificial intelligence (AI), the Internet of Things (IoT), and blockchain – offer powerful tools to improve prevention, monitoring, and control of mycotoxins across the supply chain. AI models, including machine and deep learning, can rapidly predict mycotoxin risks from climatic, agronomic, and storage factors, enabling proactive management. IoT sensor networks provide continuous monitoring of temperature, humidity, and CO 2 , helping prevent fungal growth during pre- and post-harvest stages. Blockchain enhances transparency, traceability, and trust by securely recording production and testing data. Integrating AI analytics with IoT data on blockchain supports automated decisions, early warnings, rapid recalls, and smart-contract-based alerts. Although challenges such as interoperability, costs, and standardisation persist, developing scalable hybrid platforms could transform real-time, data-driven mycotoxin control and strengthen supply chain integrity.
Abstract Maize ( Zea mays L.) contamination by mycotoxins represents a major threat to food safety and economic stability in Ethiopia. This study investigated the occurrence of toxigenic fungi and multi-mycotoxin contamination in 180 maize samples collected across three critical value chain stages (at-harvest, three-month storage, and six-month market) from four districts: Bako Tibe, Ilu Galan, Boricha, and Halaba Special. Mycological analysis revealed Fusarium and Aspergillus as the dominant genera, with Fusarium verticillioides prevailing at harvest (42.8% mean incidence) and Aspergillus flavus dominating during storage (53.6% mean incidence) as moisture content declined from 21.5 to 13.5%. LC-MS/MS analysis detected Fusarium toxins in 73.9% of samples, with fumonisin B 1 (FB 1 ) reaching 6,570 μ g/kg at harvest before declining during storage. In contrast, deoxynivalenol (DON) and zearalenone (ZEN) levels increased progressively from harvest to market, with ZEN prevalence rising from 21.7 to 93.3% and a mean concentration of 100 ± 317 μ g/kg. Maximum ZEN (3,140 μ g/kg) surpassed the EU regulatory limit of 100 μ g/kg, while aflatoxin B 1 remained below detectable risk thresholds (max 0.72 μ g/kg). Spatial variation was evident across districts: Boricha exhibited the highest pre-harvest FB 1 contamination (80%), whereas Bako Tibe showed a pronounced post-harvest ZEN escalation (13.3 to 100%). Halaba Special recorded a toxic cocktail of 100% ZEN and 86.7% DON prevalence at the market stage. Widespread occurrence of CIT was observed, reaching 60% in Boricha and 53.3% in Bako Tibe market samples. The co-occurrence of multiple mycotoxins was common across all stages. Therefore, mycotoxin mitigation strategies both under field conditions and post-harvest stages, including improved storage technologies and drying practices, require urgent intervention to address food safety concerns in the studied areas and at the national level.
Abstract The aim of this study was to investigate the occurrence of major and emerging mycotoxins in maize samples collected during the 2024 and 2025 growing seasons from different production regions of the Autonomous Province of Vojvodina (Bačka, Banat, and Srem), in the Republic of Serbia, and to evaluate their relationship with vegetation stress indicators derived from satellite data. Results suggest that satellite-derived vegetation indices on plant health status showed potential for classifying maize fields, infected with aflatoxin and fumonisin, using machine learning techniques. Given that food safety is an increasingly important global concern, the integration of remote sensing and analytical data represents a promising approach for developing predictive models and improving food safety monitoring systems. In the analysed samples of the major mycotoxins, the presence of aflatoxin B 1 (AFB 1 ), ochratoxin A (OTA) and the sum of fumonisins (F) was determined above the maximum regulated level prescribed in the Republic of Serbia and the EU. Among the emerging mycotoxins in analysed maize samples beauvericin (BEA), moniliformin (MON), alternariol (AOH), alternariolmethylether (AME) and tenuazonic acid (TeA) were detected. Emerging mycotoxins often occur simultaneously with major mycotoxins and increase the risk to human and animal health. Co-contaminations with major and emerging mycotoxins were frequent between aflatoxins and fumonisins out of major, and BEA, and MON out of emerging mycotoxins. The most frequent combination in maize samples involved FB 1 -MON (69/77 = 89.6%) in 2024 and FB 1 -BEA (61/64 = 95.3%) in 2025. Results from two monitoring years indicate widespread mixed fungal infections ( Aspergillus , Fusarium , Alternaria ) in maize samples from all investigated regions.
Abstract Aflatoxin contamination in staple crops presents considerable health risks and economic losses for smallholder farmers in Uganda. This study assessed the knowledge and awareness of aflatoxin contamination and mitigation strategies among smallholder farmers in the West Nile region. A survey of 393 farmers, supplemented by four focus group discussions with 60 participants, was conducted. Quantitative results revealed that only 21.9% of farmers were aware of aflatoxins, while 24.6% were aware of mitigation strategies. Multivariate analysis indicates that age ( ), gender ( ), and education level ( ), are significant predictors of awareness. Qualitative findings from thematic analysis indicated that limited technical knowledge, financial constraints, and insufficient agricultural extension services are barriers to aflatoxin awareness and mitigation. The study underscores the need for improved agricultural extension services, hands-on training, and policy interventions to promote effective aflatoxin management practices, including the use of biological control products, such as Aflasafe, among smallholder farmers in Uganda.
Abstract Understanding the link between mycotoxin contamination and rural farmer household food and nutrition security is pivotal for guiding evidence-based policy interventions in rural communities. This study investigated the sources, challenges, effects and existing mitigation approaches for mycotoxin contamination, and their implications on food and nutrition security in eastern Ethiopia. A semi-structured questionnaire was used to collect comprehensive data from 240 households across East and West Hararghe zones in the 2025 cropping season. The data were analysed using descriptive statistics and econometric models. Descriptive analysis revealed that inadequate crop drying and poor storage conditions were the primary factors for mycotoxin contamination, with mould most frequently observed on crops stored in traditional sacks and underground pits. Over three-quarter of the sampled households were aware of poisonous moulds but their primary information source was informal networks. However, the adoption of advanced contamination prevention practices remained low. Furthermore, 64% and 58% of households were food insecure in East and West Hararghe zones, respectively. Households in East and West Hararghe zones grouped into low (48% and 40%), medium (43% and 45%) and high (9.2% and 15%) nutritional status categories, respectively. The binary and ordered logit models showed that among other socio-economic factors, access to mycotoxins information, education, distance to market and access to main road, and extension advice significantly affected households’ food safety and nutrition security. These results imply that improving rural communities’ knowledge and perception of the mycotoxin challenge influences rural households’ food and nutrition security status. Rural communities should be capacitated through strengthening post-harvest extension services by integrating formal and informal social networks, small-scale crop drying and improved storage technologies, which may reduce mycotoxin contamination and improve food safety and household nutrition. Thus, integrating mycotoxin management into broader food and nutrition-enhancing policies and strategies is essential, with interventions focusing on determinant socio-economic factors.
