Modern-day agriculture supply chains have evolved from sovereign and autonomous local stakeholders to a worldwide interconnected system of multiple participants linked by complicated interactions, impacting the production, processing, transportation, and delivery of food to end consumers. Regular instances of fraudulent acts reveal a lack of openness in agriculture supply chains, raising worries about financial losses, eroding customer trust, and lowering corporate brand value. To develop an efficient and reliable trading environment, several fundamental modifications in the present supply chain architecture are required. There is broad consensus that blockchain can improve transparency in agriculture-food supply chains (agri-food SCs). Consumers now demand safe, sustainable, and equitable food production processes, and businesses are using blockchains and the internet of things to meet these needs. For enhanced responsiveness in agri-food SCs, new concepts have evolved that combine blockchains with various Industry 5.0 technologies (e.g., blockchain technology, big data, internet of things (IoT), radio frequency identification (RFID), near field communication (NFC), etc.). It is critical to cut through the hype and examine the technology’s limits, which might stymie its acceptance, implementation, and scalability in agri-food supply chains. This study presents Agri-SCM-BIoT (Agriculture Supply Chain Management using Blockchain and Internet of things) architecture to address the storage and scalability optimization, interoperability, security and privacy issues security, and privacy of personal data along with storage concerns with present single-chain agriculture supply chain systems. We also discussed the classification of security threats with IoT infrastructure and possible available blockchain-based defense mechanisms. Finally, we discussed the features of the proposed supply chain architecture, followed by a conclusion and future work.

The amount of surface soil moisture (SSM) is a crucial ecohydrological natural resource that regulates important land surface processes. It affects critical land–atmospheric phenomena, including the division of energy and water (infiltration, runoff, and evaporation), that impacts the effectiveness of agricultural output (sensible and latent heat fluxes and surface air temperature). Despite its significance, there are several difficulties in making precise measurements, monitoring, and interpreting SSM at high spatial and temporal resolutions. The current study critically reviews the methods and procedures for calculating SSM and the variables influencing measurement accuracy and applicability under different fields, climates, and operational conditions. For laboratory and field measurements, this study divides SSM estimate strategies into (i) direct and (ii) indirect procedures. The accuracy and applicability of a technique depends on the environment and the resources at hand. Comparative research is geographically restricted, although precise and economical—direct measuring techniques like the gravimetric method are time-consuming and destructive. In contrast, indirect methods are more expensive and do not produce measurements at the spatial scale but produce precise data on a temporal scale. While measuring SSM across more significant regions, ground-penetrating radar and remote sensing methods are susceptible to errors caused by overlapping data and atmospheric factors. On the other hand, soft computing techniques like machine/deep learning are quite handy for estimating SSM without any technical or laborious procedures. We determine that factors, e.g., topography, soil type, vegetation, climate change, groundwater level, depth of soil, etc., primarily influence the SSM measurements. Different techniques have been put into practice for various practical situations, although comparisons between them are not available frequently in publications. Each method offers a unique set of potential advantages and disadvantages. The most accurate way of identifying the best soil moisture technique is the value selection method (VSM). The neutron probe is preferable to the FDR or TDR sensor for measuring soil moisture. Remote sensing techniques have filled the need for large-scale, highly spatiotemporal soil moisture monitoring. Through self-learning capabilities in data-scarce areas, machine/deep learning approaches facilitate soil moisture measurement and prediction.

As the world human population and industrialization keep growing, the water availability issue has forced scientists, engineers, and legislators of water supply industries to better manage water resources. Pollutant removals from wastewaters are crucial to ensure qualities of available water resources (including natural water bodies or reclaimed waters). Diverse techniques have been developed to deal with water quality concerns. Carbon based nanomaterials, especially carbon nanotubes (CNTs) with their high specific surface area and associated adsorption sites, have drawn a special focus in environmental applications, especially water and wastewater treatment. This critical review summarizes recent developments and adsorption behaviors of CNTs used to remove organics or heavy metal ions from contaminated waters via adsorption and inactivation of biological species associated with CNTs. Foci include CNTs synthesis, purification, and surface modifications or functionalization, followed by their characterization methods and the effect of water chemistry on adsorption capacities and removal mechanisms. Functionalized CNTs have been proven to be promising nanomaterials for the decontamination of waters due to their high adsorption capacity. However, most of the functional CNT applications are limited to lab-scale experiments only. Feasibility of their large-scale/industrial applications with cost-effective ways of synthesis and assessments of their toxicity with better simulating adsorption mechanisms still need to be studied.