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Reliable, hands-off measurement of bovine body dimensions and weight is vital to increasing output and accelerating genetic gains in tropical beef enterprises. Traditional techniques are time-consuming, demand heavy machinery, require handling livestock, induce animal stress, and are frequently unworkable for resource-constrained smallholders lacking adequate infrastructure. This paper introduces an original, smartphone-operated platform for instantaneous body dimension capture and weight forecasting in Brahman cattle, driven by a cloud-based artificial intelligence (AI) engine. A corpus of 12,660 lateral photographs was assembled and tagged for hip depth (HD) and body length (BL), with YOLOv11 convolutional neural network variants undergoing training and evaluation. The YOLOv11m configuration achieved the highest metrics (precision: 99.85%, recall: 100%, F1-score: 99.92%, IoU: 97.68% ± 1.31%), and the automatic extractions displayed tight concordance with manual ImageJ-derived figures (MAPE < 0.4%). HD and BL were strongly intercorrelated in both sexes (r = 0.98–0.99) and moderately indicative of body weight (r = 0.57–0.59). A multiple regression formula incorporating HD and BL delivered the most precise body weight predictions (MAE = 43.44 kg; MAPE = 8.91%). The system was fielded via a LINE messaging chatbot interface, allowing users to upload cattle photos and receive immediate dimensional outputs and weight approximations straight to their smartphone—doing away with any need for bespoke hardware. This inexpensive, easy-to-operate AI instrument supplies a scalable avenue for digital phenotyping, herd oversight, and evidence-based selection in smallholder environments. The concept exhibits substantial capacity to enable data-informed management and enduring productivity improvements in tropical beef networks.

Crop production around the globe is increasingly at risk from invasive organisms, particularly the Fall Armyworm (FAW, Spodoptera frugiperda), which has caused severe deterioration of maize harvests across both Africa and Asia starting in 2016. To enable targeted pest control, an adaptive Echo State Network (ESN) was constructed that projects yearly maize output while factoring in FAW burden. A 15-year (2010–2024) monthly repository was built, integrating vegetation indicators from satellites, field-level meteorological measurements, soil nutrient readings, and FAW observation tallies. The intensity of FAW infestation is represented on a continuum from 0 to 100, derived by fusing monthly trap catch figures (40%) with larval concentration (60%). The ESN is initially calibrated on the entire body of environmental features to estimate crop performance. Next, isotonic regression is deployed to link infestation intensity with the ESN's positive residual errors, yielding a monotonic penalty mapping. This mapping captures the magnitude of output depression at varying infestation levels. When generating estimates, the inferred penalty is applied to the unadjusted ESN projection, modifying harvest forecasts to incorporate pest-driven losses without changing the foundational ESN configuration. In testing across held-out folds, the FAW-sensitive ESN yields an R² near 0.55 and cuts forecast discrepancies by as much as 67% compared to unadjusted reference models, faithfully reproducing documented harvest declines beyond 20% during major infestation episodes. The approach beats standard regression and deep neural architectures by comparable margins. It supports agriculturalists in directing countermeasures toward high-threat areas, lowering both chemical input volumes and operational expenditures. These outcomes emphasize its promise as a forward-looking alert mechanism for pinpointed actions that curtail agrochemical usage and improve input distribution. Ongoing field assessments will gauge its capacity to scale and its tangible efficacy within maize-growing zones affected by FAW.

Agricultural output and the assurance of food supplies depend fundamentally on plant breeding. A new chapter in this field has been opened by Genomic Selection, a technology distinguished by its power to exploit variation across the entire genome for predictive purposes. This technique makes it unnecessary to have pre-existing knowledge of the genes controlling particular characteristics. Yet, the enormous scale of genomic information, contrasted with the usually scarce set of phenotypic observations, frequently leads to the “curse of dimensionality,” in which classic statistical approaches, machine learning, and deep learning architectures tend to memorize noise rather than learn patterns, yielding poor predictive performance. To address this barrier, we propose a consolidated Variational auto-encoder-based Multi-task Genomic Prediction model (VMGP) that fuses a self-supervised process of shrinking and rebuilding genomic data with several simultaneous forecasting objectives. This method provides a durable solution, offering a powerful inferential engine that has undergone rigorous verification across publicly available datasets for wheat, rice, and maize. The model we have built displays extraordinary proficiency in forecasting across multiple phenotypes and varying environments, effectively mastering the challenges of genomic selection spanning different populations and highlighting its distinct capabilities and practical importance. In addition, by fusing VMGP with tools for model transparency, we can efficiently sift through and prioritize influential single-nucleotide polymorphisms, thereby boosting prediction accuracy and putting forward avenues for potentially more economical genotyping workflows. The VMGP framework, characterized by its clean design, strong forecasting capabilities, and publicly accessible source code, is well-suited for widespread rollout across plant breeding operations. It proves especially valuable for practitioners whose main focus is phenotypic projection, yet who might not be deeply versed in deep learning methodologies or the intricacies of hyperparameter optimization.

Over the past few years, intense genetic pressure for rapid weight gain and elevated breast meat output has left the international poultry sector wrestling with the difficult condition known as woody breast (WB). This myopathy has inflicted massive economic damage, estimated at up to $200 million each year, and its fundamental cause has still not been pinpointed. Manual tactile assessment remains the standard approach for distinguishing WB fillets from regular ones, but this technique is labour-intensive and prone to human inconsistency. Hyperspectral imaging (HSI) combined with machine learning algorithms can assess fillet WB status in a non-invasive, impartial, and high-throughput manner. For the present work, a total of 250 uncooked chicken breast fillet specimens—spanning normal, mild, and severe categories—were collected, and, for the first time, the spatially unequal distribution of hardness was taken into account in the design of HSI-based processing models. This investigation not only categorized WB grades from HSI data but also formulated a regression framework linking the spectral inputs to measured sample firmness values. To build an effective classification and regression pipeline, a neural architecture search (NAS) strategy was used to construct a wide-deep neural network, christened NAS-WD. In this design, NAS was initially deployed to tune both the network layout and its hyperparameters autonomously. Classification outcomes indicated that NAS-WD achieved 95% total accuracy in classifying the three WB grades, exceeding the performance of classic machine learning methods. At the same time, the regression model linking spectral signatures to hardness achieved an R² of 0.75. This result substantially surpasses that of conventional regression techniques.

Ensuring food quality, safety, and authenticity has become a central challenge for contemporary food systems because risks now emerge across production, processing, distribution, retail, and consumption. Artificial intelligence is increasingly used to interpret complex food data, including spectral signals, images, sensor outputs, molecular profiles, traceability records, and supply-chain alerts. These developments suggest that AI can support a shift from reactive food inspection toward predictive and preventive food integrity assurance. Despite this promise, AI applications in food quality, safety, and authenticity often remain separated across disciplinary and technological domains. Sensing studies focus on detection accuracy, prediction studies focus on model performance, traceability studies focus on chain-of-custody infrastructure, and explainable AI studies focus on interpretability. This fragmentation limits the field’s ability to understand how these components could function together as an integrated assurance system. This integrative review synthesises 31 peer-reviewed articles published between 2017 and 2025 across food science, analytical chemistry, sensing technology, food informatics, and artificial intelligence. The review examines four interconnected domains: sensing technologies and data sources, prediction models for quality and safety, traceability and authenticity verification, and explainable decision systems. Its purpose is not to report new empirical data, but to integrate diverse research streams into a coherent conceptual understanding of AI-enabled food integrity assurance. The review shows that AI has already demonstrated value in optical sensing, spectroscopy, imaging, authenticity testing, shelf-life prediction, risk assessment, fraud detection, and traceability analysis. However, most applications are still designed as isolated models rather than as connected decision systems. Three tables classify sensing technologies, categorise prediction models, and present an integrative framework linking detection, prediction, provenance, and explainable decisions. The central conclusion is that the technical components of AI-enabled food integrity assurance already exist, but they remain insufficiently integrated. Future research should move beyond stand-alone classification or prediction models and toward interoperable, explainable, and operationally validated systems. Such systems should connect sensing, prediction, traceability, and human decision-making across food supply chains.

Cold chains are essential food-system infrastructures because they preserve safety, quality, nutritional value, and marketability from production to consumption. Despite their importance, many cold-chain operations still depend on reactive checks, delayed audits, and fixed temperature protocols that are poorly aligned with the biological variability of food products. This reactive orientation allows quality deterioration to accumulate invisibly until products arrive at inspection points, retailers, or consumers. The central problem addressed in this article is the absence of an integrated architecture that connects continuous monitoring, predictive analytics, and automated decision support across the full cold-chain pathway. Sensors may detect temperature excursions, models may estimate shelf life, and logistics systems may manage routing, yet these capabilities often remain fragmented. Without architectural integration, cold chains cannot become truly intelligent, adaptive, or self-correcting. The objective of this article is to propose an Intelligent Cold Chain conceptual architecture for real-time quality preservation, spoilage prediction, and loss prevention. The architecture is designed to transform environmental and logistical data into predictive quality intelligence and operational decisions. It positions the cold chain as a cyber-physical system in which sensing, prediction, decision-making, and coordination interact continuously. The article develops its architecture through a conceptual synthesis of peer-reviewed research published between 2017 and 2025. The synthesis draws from cold-chain logistics, food quality engineering, IoT monitoring, machine learning, digital twins, and supply-chain systems design. It does not report new empirical data, but instead integrates existing technological and theoretical insights into an original systems framework. The proposed architecture defines four functional layers: sensing, prediction, decision, and coordination. These layers specify how sensor streams are acquired, how spoilage risk is inferred, how corrective actions are recommended, and how information is shared across actors. The architecture provides a blueprint for moving cold chains from passive refrigerated corridors toward proactive, data-driven systems that preserve quality and reduce food losses.

Digital twins are increasingly proposed as cyber-physical representations that may support precision agriculture through monitoring, simulation, prediction, optimization, and decision support. However, agricultural systems described as digital twins vary substantially in data quality, interoperability, synchronization, modelling depth, predictive reliability, decision functionality, validation, and operational governance. This article proposes a conceptual maturity framework that evaluates agricultural digital twins through capability progression rather than technology adoption alone. The framework begins with descriptive monitoring and advances through real-time farm representation, simulation and scenario analysis, prediction and optimization, and supervised autonomous decision support. Monitoring maturity depends on reliable sensing, remote sensing, Internet of Things integration, data fusion, spatial and temporal adequacy, traceability, and human-interpretable representation. Simulation and prediction maturity require explicit farm-system boundaries, calibrated models, uncertainty estimation, local validation, and evidence that analytical outputs remain valid for their intended context. Optimization maturity additionally requires transparent objectives, constraints, robustness assessment, and distinction between predictive accuracy and decision value. The highest proposed level permits only narrowly bounded and supervised decision initiation supported by human authority, explainability, safety mechanisms, governance, cybersecurity where relevant, and continuous feedback. The framework does not constitute a validated maturity standard or establish agronomic, environmental, economic, or autonomous decision benefits. Its principal contribution is an integrated structure for assessing capability readiness, identifying progression barriers, and preventing monitoring, simulation, prediction, or optimization functions from being overinterpreted as evidence of deployment-ready autonomy.