The integration of Internet of Things (IoT) sensors and Machine Learning (ML) technologies has transformed precision agriculture by enabling data-driven, adaptive, and efficient farming practices. IoT sensors provide continuous, high-resolution monitoring of critical agricultural parameters, including soil health, crop growth, and environmental conditions. Coupled with advanced ML algorithms, this data facilitates predictive analytics and real-time decision-making, optimizing resource utilization for irrigation, pest control, and yield prediction. Recent innovations, such as edge computing, Reinforcement Learning (RL), and Transfer Learning, have further enhanced the scalability and adaptability of IoT-ML systems, enabling dynamic responses to complex agricultural challenges. This review synthesizes evidence from case studies and emerging applications to illustrate the potential of IoT-ML integration in driving sustainable agricultural development. Highlighted examples include IoT-enabled irrigation systems achieving over 30% water savings and RL-driven automation for efficient pest and disease management. Despite significant progress, barriers such as high initial investment costs, connectivity limitations, and data integration challenges hinder widespread adoption, particularly in resource-constrained regions. The study provides a comprehensive evaluation of IoT-ML applications, explores strategies to address implementation hurdles, and outlines a roadmap for scaling these technologies globally. By advancing precision agriculture through technological innovation, this integration represents a critical pathway toward enhancing food security, reducing environmental footprints, and promoting resilient agricultural systems.

Numerous scientific studies have been conducted, and a wide range of new inorganic geopolymer composite applications have emerged as a result. This innovative material can be made from agricultural waste sources rich in aluminosilicate, which provides best substitute for traditional cement in terms of both quality and cost. This paper presents the state-of-the-art review of deploying agro-wastes as precursor for geopolymer concrete with the view to aggregating the research studies of using agro waste ash-based geopolymer as the most auspicious, durable, inexpensive, user-friendly, and eco-beneficial construction materials for the long-term use. This paper thoroughly examines the rheological, workability, chemistry, and mechanical characteristics of agro-waste ash-based geopolymer concrete with emphasis on sugarcane bagasse and banana peels ash. Sugarcane and green bananas (matooke) play vital roles in East African agriculture, economy and food security. The effects micro-structures, curing conditions, molarity of metallic alkalis, proportioning of alkaline-binder ratio, aggregates content and size fractions effects on the behavior of geopolymer concrete were critically assessed. The study highlighted the abundance of these agro products which serve as staple food in East African region, therefore, it becomes imperative to recycle its waste to produce durable concrete by way of waste management strategy. As a vital component of the readily available building materials, they have a crucial role to play in providing the construction sector with a sustainable outlook while concluding that agro-wastes are potential material that could be a game changer for geopolymer concrete, a number of research gaps that worthy for further study were identified. The findings of this review further demonstrates that agro-waste derivatives when used as binder materials in geopolymer concrete can be a viable and sustainable alternative to conventional cementitious materials.

The use of aluminium waste (AW) and sawdust ash (SDA) in concrete was evaluated in this study where the cement ratio was partially replaced by fractions of AW and SDA introduced as a supplementary cementitious material. Artificial neural network (ANN) was adapted as the modelling tool for this study and was developed with a two-layer feed-forward network, hidden neurons with sigmoid activation function and linear output neurons for the simulation of the network. The setting time and concrete compressive strength at varying curing days were predicted using the neural network model with variations of constituents of the cement content consisting of OPC, SDA and AW as the input of the network. Three input and seven output data set were used for the model development using the following algorithms; Data Division: Random, Training: Levenberg–Marquardt and Calculation: MATLAB. The data sets are set aside for validation, training and testing; 70% of the samples are used for training, 15% for validation and 15% are also used for testing. The performance of the networks was evaluated using linear regression, RMSE and R-values. The model performance scored 0.91 and 0.07 for R² and RMSE, respectively, and performed better than the linear regression model, the results indicate the efficiency, reliability and usefulness of ANN for predicting concrete mechanical properties where AW and SDA are used to replace cement ratio accurately.

Ecofriendly, ecoefficient and sustainable civil engineering work has been research with emphasis on adapting the byproducts of solid waste recycling and reuse to achieving infrastructural activities with low or zero carbon emission. The direction combustion model, the solid waste incinerator caustic soda oxides of carbon entrapment model (SWI-NaOH-OCEM) developed by this research has achieved a zero carbon release. This research adopted the literature search method to put together research results of previous works relevant to the aim of this present work. It has been shown that CO and CO2 emissions can be contained during the derivation of alternative or supplementary cementing materials used in the replacement of ordinary Portland cement in civil engineering works. In the overall assessment of the present review work has left the environment free of the hazards of CO and CO2 emissions. It was shown that these supplementary cementing materials derived from solid wastes improve the engineering properties of treated soft clay and expansive soils, concrete, and asphalt. Bio-peels, another form solid waste has been established as a good detoxificant used in treating wastewater. It has been shown that solid waste recycling and reuse is a hub to achieving ecofriendly, ecoefficient and sustainable infrastructural development on the global scale.

This research explores the application of Artificial Intelligence (AI) techniques to assess the mechanical properties of geopolymer concrete made from a blend of Banana Peel-Ash (BPA) and Sugarcane Bagasse Ash (SCBA), using a sodium silicate (Na₂SiO₃) to sodium hydroxide (NaOH) ratio ranging from 1.5 to 3. Utilizing three AI methodologies—Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Gene Expression Programming (GEP)—the study aims to enhance prediction accuracy for the mechanical properties of geopolymer concrete based on 104 datasets. By optimizing mix designs through varying proportions of BPA and SCBA, alkaline activator molarity, and aggregate-to-binder ratios, the research identified combinations that significantly enhance mechanical properties, demonstrating notable international relevance as it contributes to global efforts in sustainable construction by effectively utilizing industrial by-products. The experimental results demonstrated that increasing the molarity of the alkaline activator from 4 to 10 M significantly enhanced both the compressive and flexural strengths of the geopolymer concrete. Specifically, a mixture containing 52.5% SCBA and 47.5% BPA at a 10 M molarity achieved a maximum compressive strength of 33.17 MPa after 20 h of curing. In contrast, a mixture composed of 95% SCBA and 5% BPA at a 4 M molarity exhibited a substantially lower compressive strength of only 21.27 MPa. Additionally, the highest recorded flexural strength of 9.95 MPa (77.25% SCBA and 22.5 BPA) was observed at the 10 M molarity, while the flexural strength at 4 M was lowest, at 4.12 MPa (95% SCBA and 5% BPA). Microstructural analysis through Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (ED-SEM) revealed insights into the pore structure and elemental composition of the concrete, while Thermogravimetric Analysis (TGA) provided data on the material’s thermal stability and decomposition characteristics. Performance analysis of the AI models showed that the ANN model had an average MSE of 1.338, RMSE of 1.157, MAE of 3.104, and R² of 0.989, while the ANFIS model outperformed with an MSE of 0.345, RMSE of 0.587, MAE of 1.409, and R² of 0.998. The GEP model demonstrated an MSE of 1.233, RMSE of 1.110, MAE of 1.828, and R² of 0.992, confirming that ANFIS is the most accurate model for predicting the mechanical and rheological properties of geopolymer concrete. This study highlights the potential of integrating AI with experimental data to optimize the formulation and performance of geopolymer concrete, advancing sustainable construction practices by effectively utilizing industrial by-products.