Agriculture encompasses the study, practice, and discipline of plant cultivation. Agriculture has an extensive history dating back thousands of years. Depending on climate and terrain, it began independently in various locations on the planet. In comparison to what could be sustained by foraging and gathering, agriculture has the potential to significantly increase the human population. Throughout the twenty-first century, precision farming (PF) has increased the agricultural output. precision agriculture (PA) is a technology-enabled method of agriculture that assesses, monitors, and evaluates the needs of specific fields and commodities. The primary objective of this farming method, as opposed to conventional farming, is to increase crop yields and profitability through the precise application of inputs. This work describes in depth the development and function of artificial intelligence (AI) and the internet of things (IoT) in contemporary agriculture. Modern day-to-day applications rely extensively on AI and the IoT. Modern agriculture leverages AI and IoT for technological advancement. This improves the accuracy and profitability of modern agriculture. The use of AI and IoT in modern smart precision agricultural applications is highlighted in this work and the method proposed incorporates specific steps in PF and demonstrates superior performance compared to existing classification methods. It achieves a remarkable accuracy of 98.65%, precision of 98.32%, and recall rate of 97.65% while retaining competitive execution time of 0.23 s, when analysing PF using the FAOSTAT benchmark dataset. Additionally, crucial equipment and methods used in PF are described and the vital advantages and real-time tools utilised in PA are covered in detail.

Response surface methodology (RSM) is a widely used tool for modeling and optimization for food processes. The objective of this review is to evaluate recent findings on the use of RSM in the extraction of compounds from agri-food products. First, the steps for the application of RSM were briefly detailed. According to the analysis performed, RSM is suitable because it evaluates the effects of the independent variables and their interactions on the responses, which is ideal for the optimization of different techniques for the extraction of multiple bioactive compounds and therefore, in the various studies, has allowed to significantly increase the yield and even the biological activities of the extracts; however, RSM has limitations and considering the complexity and dynamics of foods, the challenge is much greater. In this sense, it was determined that simultaneous use with other techniques is necessary in order to optimally describe the process and obtain more accurate results.

Recent technological developments indicate possible advancements in supply chain management (SCM). These innovations have attracted a lot of interest from industries including logistics, manufacturing, packaging, and transportation. The conventional systems, however, use centralised servers to control all operations, including the exchange of raw materials, making orders, dealing with buyers and sellers, and updating orders. The networks supply chain may thus be insecure as a result of every activity being routed via centralised servers. The danger is additionally increased by a number of difficulties, including scalability, data integrity, security, and availability. Block chain technology may be used in these circumstances to decentralise transaction processing and eliminate the need for a centralised controller. In this approach, the performance of the resource-constrained supply chain network is improved by the effective use of edge computing and priority data access. The Intelligent K-Means (IKM) clustering algorithm is suggested across the edge nodes in the current research to categorise the priority level of each piece of data. This classifier determines if the edge node has received data that is high priority or low priority. Low priority data is recorded in the log files for future data analysis. Then, to allow safe data flow in the open block chain while excluding outside parties, the High Priority Access based Smart Contract (HPASC) technique is deployed. The whole experiment was conducted in a Python environment, and variables including scalability, reaction time, throughput, and accuracy were studied. Current systems constrained block sizes and fork creation lengthen the time transactions must wait before being processed. The suggested methodology is quicker and uses less storage space than current block chain systems. The results show that the suggested approach works better than current blockchain technology to raise the standard of supply chain management.

The current investigation explains the chemical reaction and bioconvection process for an inclined magnetized Cross nanofluid over an inclined cylinder using a spectral relaxation approach. Additionally, the facts concerning swimming gyrotactic microorganisms, non-uniform thermal conductivity, and variable decrease or increase in heat sources are taken together. Each profile is checked for inclined and orthogonal magnetic impact. Appropriate transformations made for conversion of nonlinear PDEs into systems of ODEs. For obtaining numerical results, a spectral relaxation approach is utilized, and graphs are plotted with each physical parameter attached. It is well established that the temperature field intensifies owing to an amplification of thermal conduction and Brownian diffusivity phenomena. The heat transfer rate amplifies owing to a magnification in magnetic parameter and thermal conductivity, but the velocity field diminishes as a result of magnification in the Weissenberg number and power law index. Amplification in the reaction rate constant parameter diminishes the concentration field. Activation energy is the key factor responsible for magnification in the concentration field. Furthermore, smooth agreement is found during comparison with the existing literature. Statistical analysis is also conducted for physical quantities.