from for The COVID-19 pandemic and the associated policy responses to it were highly disruptive of supply chains throughout the economy in 2020 – disruptive on an unprecedented scale. Massive shocks on both the de-mand and supply sides of the market presented par-ticular challenges in agricultural product markets. These were arguably most pronounced in the protein sector. This paper will examine these protein sector supply chain shocks, exploring the nature and causes of the market disruptions during the height of the pandemic, evaluating the effects of these shocks from an economic perspective, and identifying potential market and policy responses that could mitigate the adverse effects of similar events in the future. feedstuffs or by calculating programmed rates of gain using the net en-ergy system. Instead, many producers chose to attempt maximal rates of gain hoping persistent growth and feeding margins would offset discounts due to heavy carcass weights and excess fatness when the supply chain began moving again. Regarding new placements, the structure of the beef industry is uniquely devel-oped to absorb cattle in stocker and backgrounding operations. This presentation will review the factors impacting cattle production and provide case-studies related to feeding at maintenance and growth rates, ef-ficiencies, and carcass outcomes of held cattle from an operation and industry level.
Abstract Antimicrobial resistance (AMR) has developed as one of the major urgent threats to public health causing serious issues to successful prevention and treatment of persistent diseases. In spite of different actions taken in recent decades to tackle this issue, the trends of global AMR demonstrate no signs of slowing down. Misusing and overusing different antibacterial agents in the health care setting as well as in the agricultural industry are considered the major reasons behind the emergence of antimicrobial resistance. In addition, the spontaneous evolution, mutation of bacteria, and passing the resistant genes through horizontal gene transfer are significant contributors to antimicrobial resistance. Many studies have demonstrated the disastrous financial consequences of AMR including extremely high healthcare costs due to an increase in hospital admissions and drug usage. The literature review, which included articles published after the year 2012, was performed using Scopus, PubMed and Google Scholar with the utilization of keyword searches. Results indicated that the multifactorial threat of antimicrobial resistance has resulted in different complex issues affecting countries across the globe. These impacts found in the sources are categorized into three different levels: patient, healthcare, and economic. Although gaps in knowledge about AMR and areas for improvement are obvious, there is not any clearly understood progress to put an end to the persistent trends of antimicrobial resistance.
Lignocelluloses are often a major or sometimes the sole components of different waste streams from various industries, forestry, agriculture and municipalities. Hydrolysis of these materials is the first step for either digestion to biogas (methane) or fermentation to ethanol. However, enzymatic hydrolysis of lignocelluloses with no pretreatment is usually not so effective because of high stability of the materials to enzymatic or bacterial attacks. The present work is dedicated to reviewing the methods that have been studied for pretreatment of lignocellulosic wastes for conversion to ethanol or biogas. Effective parameters in pretreatment of lignocelluloses, such as crystallinity, accessible surface area, and protection by lignin and hemicellulose are described first. Then, several pretreatment methods are discussed and their effects on improvement in ethanol and/or biogas production are described. They include milling, irradiation, microwave, steam explosion, ammonia fiber explosion (AFEX), supercritical CO2 and its explosion, alkaline hydrolysis, liquid hot-water pretreatment, organosolv processes, wet oxidation, ozonolysis, dilute-and concentrated-acid hydrolyses, and biological pretreatments.
Biocatalytic potential of microorganisms have been employed for centuries to produce bread, wine, vinegar and other common products without understanding the biochemical basis of their ingredients. Microbial enzymes have gained interest for their widespread uses in industries and medicine owing to their stability, catalytic activity, and ease of production and optimization than plant and animal enzymes. The use of enzymes in various industries (e.g., food, agriculture, chemicals, and pharmaceuticals) is increasing rapidly due to reduced processing time, low energy input, cost effectiveness, nontoxic and eco-friendly characteristics. Microbial enzymes are capable of degrading toxic chemical compounds of industrial and domestic wastes (phenolic compounds, nitriles, amines etc.) either via degradation or conversion. Here in this review, we highlight and discuss current technical and scientific involvement of microorganisms in enzyme production and their present status in worldwide enzyme market.Graphical abstract
Abstract Digital twins are being adopted by increasingly more industries, transforming them and bringing new opportunities. Digital twins provide previously unheard levels of control over physical entities and help to manage complex systems by integrating an array of technologies. Recently, agriculture has seen several technological advancements, but it is still unclear if this community is making an effort to adopt digital twins in its operations. In this work, we employ a mixed-method approach to investigate the added-value of digital twins for agriculture. We examine the extent of digital twin adoption in agriculture, shed light on the concept and the benefits it brings, and provide an application-based roadmap for a more extended adoption. We report a literature review of digital twins in agriculture, covering years 2017-2020. We identify 28 use cases, and compare them with use cases in other disciplines. We compare reported benefits, service categories, and technology readiness levels to assess the level of digital twin adoption in agriculture. We distill the digital twin characteristics that can provide added-value to agriculture from the examined digital twin applications in agriculture and in other disciplines. Then, inspired by digital twin applications in other disciplines, we propose a roadmap for digital twins in agriculture, consisting of examples of growing complexity. We conclude this paper by identifying the distinctive characteristics of agricultural digital twins.
Abstract Industrial Internet of Things (IIoT) is a convincing stage by interfacing different sensors around us to the Internet, giving incredible chances for the acknowledgment of brilliant living. It is a fast growing technology in the present scenario. IIoT has its effect on almost every advanced field in the society. It has impact not only on work, but also on the living style of individual and organization. Due to high availability of internet, the connecting cost is decreasing and more advanced systems has been developed with Wi-Fi capabilities. The concept of connecting any device with internet is “IIoT”, which is becoming new rule for the future. This manuscript discusses about the applications of Internet of Things in different areas like- automotive industries, embedded devices, environment monitoring, agriculture, construction, smart grid, health care, etc. A regressive review of the existing systems of the automotive industry, emergency response, and chain management on IIoT has been carried out, and it is observed that IIoT found its place almost in every field of technology.
Akriti Taneja, Gayathri Nair, Manisha Joshi
et al.
Artificial intelligence (AI) involves the development of algorithms and computational models that enable machines to process and analyze large amounts of data, identify patterns and relationships, and make predictions or decisions based on that analysis. AI has become increasingly pervasive across a wide range of industries and sectors, with healthcare, finance, transportation, manufacturing, retail, education, and agriculture are a few examples to mention. As AI technology continues to advance, it is expected to have an even greater impact on industries in the future. For instance, AI is being increasingly used in the agri-food sector to improve productivity, efficiency, and sustainability. It has the potential to revolutionize the agri-food sector in several ways, including but not limited to precision agriculture, crop monitoring, predictive analytics, supply chain optimization, food processing, quality control, personalized nutrition, and food safety. This review emphasizes how recent developments in AI technology have transformed the agri-food sector by improving efficiency, reducing waste, and enhancing food safety and quality, providing particular examples. Furthermore, the challenges, limitations, and future prospects of AI in the field of food and agriculture are summarized.
We investigate the impact of domain-specific self-supervised pre-training on agricultural disease classification using hierarchical vision transformers. Our key finding is that SimCLR pre-training on just 3,000 unlabeled agricultural images provides a +4.57% accuracy improvement--exceeding the +3.70% gain from hierarchical architecture design. Critically, we show this SSL benefit is architecture-agnostic: applying the same pre-training to Swin-Base yields +4.08%, to ViT-Base +4.20%, confirming practitioners should prioritize domain data collection over architectural choices. Using HierarchicalViT (HVT), a Swin-style hierarchical transformer, we evaluate on three datasets: Cotton Leaf Disease (7 classes, 90.24%), PlantVillage (38 classes, 96.3%), and PlantDoc (27 classes, 87.1%). At matched parameter counts, HVT-Base (78M) achieves 88.91% vs. Swin-Base (88M) at 87.23%, a +1.68% improvement. For deployment reliability, we report calibration analysis showing HVT achieves 3.56% ECE (1.52% after temperature scaling). Code: https://github.com/w2sg-arnav/HierarchicalViT
Tashreef Muhammad, Tahsin Ahmed, Meherun Farzana
et al.
Accurate short-term forecasting of agricultural commodity prices is critical for food security planning and smallholder income stabilisation in developing economies, yet machine-learning-ready datasets for this purpose remain scarce in South Asia. This paper makes two contributions. First, we introduce AgriPriceBD, a benchmark dataset of 1,779 daily retail mid-prices for five Bangladeshi commodities - garlic, chickpea, green chilli, cucumber, and sweet pumpkin - spanning July 2020 to June 2025, extracted from government reports via an LLM-assisted digitisation pipeline. Second, we evaluate seven forecasting approaches spanning classical models - naïve persistence, SARIMA, and Prophet - and deep learning architectures - BiLSTM, Transformer, Time2Vec-enhanced Transformer, and Informer - with Diebold-Mariano statistical significance tests. Commodity price forecastability is fundamentally heterogeneous: naïve persistence dominates on near-random-walk commodities. Time2Vec temporal encoding provides no statistically significant advantage over fixed sinusoidal encoding and causes catastrophic degradation on green chilli (+146.1% MAE, p<0.001). Prophet fails systematically, attributable to discrete step-function price dynamics incompatible with its smooth decomposition assumptions. Informer produces erratic predictions (variance up to 50x ground-truth), confirming sparse-attention Transformers require substantially larger training sets than small agricultural datasets provide. All code, models, and data are released publicly to support replication and future forecasting research on agricultural commodity markets in Bangladesh and similar developing economies.
Nikhil Raj Deep, Ebrahim Babaeian, Lakesh Sharma
et al.
Accurate field-scale estimation and mapping of root-zone soil moisture is critical for precision irrigation management and optimizing crop yield, especially in Florida’s sandy agroecosystems where low water-holding capacity and high nutrient leaching increase irrigation challenges. While microwave satellites provide soil moisture at large scale, their coarse resolution (km scale) and surface (0–5 cm) estimates limit their application for within-field irrigation decisions. In this study, we developed and evaluated a field-scale mapping framework that integrates a Utility Terrain Vehicle (UTV)-mounted dual-polarized L-band (1.4 GHz) radiometer with i) the tau-omega radiative transfer model, and ii) a hybrid (τ–ω–XGBoost) approach that integrates tau-omega outputs with extreme gradient boosting to estimate and map soil moisture at 10, 20, 30, and 40 cm depths. The framework combines temporal brightness temperature observations with ancillary variables (e.g., vegetation water content, effective soil temperature) to parameterize tau-omega model at the field-scale. The resulting estimates were then assimilated into extreme gradient boosting (XGBoost) as additional predictors and physical constraints to improve retrieval accuracy. Results indicated that tau-omega and hybrid approaches produced mean RMSE of ∼ 0.02–0.04 cm3 cm-3, with best performance at upper depths and vertical polarization outperforming horizontal polarization. Allowing spatial variability in surface roughness improved tau-omega model parameterization and retrieval accuracy. The hybrid model slightly outperformed the tau-omega model, especially at deeper depths (mean unbiased RMSE of 0.020 cm3 cm−3). Overall, the proposed framework not only provides a mesoscale bridge between point sensors and satellite pixels for field-scale mapping of root-zone soil moisture to support irrigation management in sandy agroecosystems, but it can also benefit airborne- and satellite-based soil moisture retrievals.
Nancy Victor, Praveen Kumar Reddy Maddikunta, Delphin Raj Kesari Mary
et al.
Agriculture can be regarded as the backbone of human civilization. As technology evolved, the synergy between agriculture and remote sensing has brought about a paradigm shift, thereby entirely revolutionizing the traditional agricultural practices. Nevertheless, the adoption of remote sensing technologies in agriculture faces various challenges in terms of limited spatial and temporal coverage, high cloud cover, low data quality, etc. Industry 5.0 (I5.0) marks a new era in the industrial revolution, where humans and machines collaborate closely, leveraging their distinct capabilities, thereby enhancing the decision-making capabilities, sustainability, and resilience. This article provides a comprehensive survey of remote sensing technologies and related aspects in dealing with the various agricultural practices in the I5.0 era. We also elaborately discuss the various applications pertaining to I5.0-enabled remote sensing for agriculture. Finally, we discuss several challenges and issues related to the integration of I5.0 technologies in agricultural remote sensing. This comprehensive survey on remote sensing for agriculture in the I5.0 era offers valuable insights into the current state, challenges, and potential advancements in the integration of remote sensing technologies and I5.0 principles in agriculture, thus paving the way for future research, development, and implementation strategies in this domain.
Morteza Saberikamarposhti, Kok-Why Ng, Mehdi Yadollahi
et al.
Agriculture is a leading sector in international initiatives to mitigate climate change and promote sustainability. This article exhaustively examines the removals and emissions of greenhouse gases (GHGs) in the agriculture industry. It also investigates an extensive range of GHG sources, including rice cultivation, enteric fermentation in livestock, and synthetic fertilisers and manure management. This research reveals the complex array of obstacles that are faced in the pursuit of reducing emissions and also investigates novel approaches to tackling them. This encompasses the implementation of monitoring systems powered by artificial intelligence, which have the capacity to fundamentally transform initiatives aimed at reducing emissions. Carbon capture technologies, another area investigated in this study, exhibit potential in further reducing GHGs. Sophisticated technologies, such as precision agriculture and the integration of renewable energy sources, can concurrently mitigate emissions and augment agricultural output. Conservation agriculture and agroforestry, among other sustainable agricultural practices, have the potential to facilitate emission reduction and enhance environmental stewardship. The paper emphasises the significance of financial incentives and policy frameworks that are conducive to the adoption of sustainable technologies and practices. This exhaustive evaluation provides a strategic plan for the agriculture industry to become more environmentally conscious and sustainable. Agriculture can significantly contribute to climate change mitigation and the promotion of a sustainable future by adopting a comprehensive approach that incorporates policy changes, technological advancements, and technological innovations.
Leaf coloration, a critical trait in ornamental foliage plant breeding, is influenced by chlorophyll, carotenoids, and flavonoids, which dictate plant aesthetic and economic value. The regulatory role of MYB transcription factors in leaf pigmentation is well recognized. However, their specific influence on Cymbidium leaf coloration remains obscure despite the genus’s global economic importance. This study utilized a novel orchid mutant with leaf variegation as the experimental material to investigate the role of CcR2R3-MYB genes. This research has successfully identified and cloned a novel MYB transcription factor, namely CcR2R3-MYB, from a leaf variegation mutant of Cymbidium. The expression level of CcR2R3-MYB was significantly higher in the mutant plants, with the protein predominantly localized in the nucleus. Phylogenetic analysis indicates that the gene is closely related to AtMYB106 and DhMYB1 and regulates leaf cell morphogenesis and color variation in Cymbidium. Overexpression of CcR2R3-MYB resulted in a yellowish-green and a reduction in photosynthetic pigment content in the Dendrobium. These findings not only lay a foundation for unraveling the mechanism by which CcR2R3-MYB regulates the development of orchid foliage art but also hold significant implications for creating new orchid germplasm and the enhancement of varietal traits.
Continued adoption of agricultural robots postulates the farmer's trust in the reliability, robustness and safety of the new technology. This motivates our work on safety assurance of agricultural robots, particularly their ability to detect, track and avoid obstacles and humans. This paper considers a probabilistic modelling and risk analysis framework for use in the early development phases. Starting off with hazard identification and a risk assessment matrix, the behaviour of the mobile robot platform, sensor and perception system, and any humans present are captured using three state machines. An auto-generated probabilistic model is then solved and analysed using the probabilistic model checker PRISM. The result provides unique insight into fundamental development and engineering aspects by quantifying the effect of the risk mitigation actions and risk reduction associated with distinct design concepts. These include implications of adopting a higher performance and more expensive Object Detection System or opting for a more elaborate warning system to increase human awareness. Although this paper mainly focuses on the initial concept-development phase, the proposed safety assurance framework can also be used during implementation, and subsequent deployment and operation phases.
This article exemplifies the design of a fruit detection and classification system using Convolutional Neural Networks (CNN). The goal is to develop a system that automatically assesses fruit quality for farm inventory management. Specifically, a method for mango fruit classification was developed using image processing, ensuring both accuracy and efficiency. Resnet-18 was selected as the preliminary architecture for classification, while a cascade detector was used for detection, balancing execution speed and computational resource consumption. Detection and classification results were displayed through a graphical interface developed in MatLab App Designer, streamlining system interaction. The integration of convolutional neural networks and cascade detectors proffers a reliable solution for fruit classification and detection, with potential applications in agricultural quality control.