As wheat is one of the major crops worldwide, therefore, accurate disease detection in wheat plants is critical for mitigating effects and halting disease spread. Nowadays, the detection of diseases through images using machine learning and deep learning has achieved state-of-art results. Yet these models suffer from two shortcomings, one is the data-hungry models of deep learning, and the second is the adaption of new classes with previous ones, i.e., if a new disease arrived, one must retrain the entire model with a new dataset which has led to the origination of few-shot learning that solves both problems. A wheat disease classification network is proposed based on a few-shot learning method for the classification of 18 wheat diseases using EfficientNet as a backbone. Also, the attention mechanism is incorporated to facilitate efficient feature selection. Our proposed network has achieved 93.19% accuracy on the 40 images of 18 disease classes manually collected from the internet while that of our backbone network EfficientNet has gained an accuracy of about 98.5% accuracy on CGIAR Dataset. The evaluation demonstrates that despite the presence of only a few images in both the training and query sets, the proposed model has achieved superior performance in terms of accuracy and computation cost. Furthermore, the evaluation also highlights the effectiveness of our model in terms of data and computation cost. The proposed method can be used for disease detection in wheat crops requiring less amount of data.

Sustainable soil management with the appropriate understanding of soil characteristics is vital in maintaining and improving agriculture soil management. The objectives of the present study are to characterize the spatial variability of soil using the GIS technique and used agglomerative hierarchical clustering (AHC) for the delineation of management zones (MZs) for precision agriculture. A total of 111 soil samples were collected from 37 soil profiles in systematic depths (0–50, 50–100, and 100–150 cm) from the South Hail region, KSA. Samples were analyzed for pH, ECe, CaCO₃, available macro and micronutrients, and hydrological properties. The best fit models, using ArcGIS software, were J-Bessel for pH, Clay, bulk density (BD), and available water (AW); K-Bessel for EC and available N; Stable for CaCO₃, P, K, Fe, Zn, Sand, field capacity (FC) and saturated hydraulic conductivity (Ks); Spherical for Mn and Cu; Gaussian for saturation percentage (SP); whereas exponential for permanent wilting point (PWP). The principal component analysis (PCA) resulted in six principal components (PCs) explaining 79.75% of the total variance of soil properties. The PC1 was strongly influenced by soil BD, FC, clay, PWP, Ks, and sand. PC2 was dominated by N, ECe, and CaCO₃; PC3 was dominated by pH; PC4 was dominated primarily by K and P, PC5 was mainly dominated by Fe; Mn, and Cu, and PC6 was mainly dominated by SP and Zn. Based on AHC, four soil management zones (MZs) cover 77.94, 14.10, 7.11 and 0.85% of the studied area. Management zone 1 (MZ1) and Management zone 3 (MZ3) are classified as moderately saline while Management zone 2 (MZ2) is classified as highly saline soils, greater than the limiting critical value for the sensitive crops. The potential solutions to reduce salinization in the area include: reducing irrigation, moving to salt-tolerant crops or applying humic acids to fix anions and cations and eliminate them from the root zone of the plants. Treating the area with diluted sulfuric acid to remove salts and reduce ECe to less than 2 dSm⁻¹, to get maximum productivity. This finding is diagnostic for determining the amount of fertilizer and irrigation water to be applied to soils in different management zones. Its emphasis’s the importance of site-specific management for long-term crop productivity and, as a result, reducing environmental hazards caused by uneven fertilizers and water applications.

Even though water is a renewable resource, the majority of the available water on the planet is unfit for human use. Moreover, the drinkable water demand is ever-increasing as a result of rising population, urbanization, and life standards, which makes the needs for sustainable, economic, and environment-friendly treatment alternatives of utmost importance. Seawater desalination using solar stills has been proposed as a promising alternative that may help to solve drinkable water scarcity issues. In the past decades, many studies have been conducted to assess the performance of different types of solar stills aiming to enhance their productivity. Computational fluid dynamic (CFD) numerical simulation is one of the approaches that have been used recently to assess the performance of solar stills. The present study performed a systematic review and bibliometric analysis to provide a comprehensive overview of CFD numerical simulation uses as a tool to assess solar stills performance. A total of 486 publications were collected initially from different databases for the period between 2012 and 2022. The collected publications were filtered through several stages reaching 43 publications of highest significance. The collected data were analyzed descriptively, and the bibliometric mapping was presented. Furthermore, the basics and principles of CFD numerical simulation of solar stills efficiency were described and discussed. Later, the previous studies were analyzed to understand the algorithms, methods, and still types used. Finally, future research scopes and conclusions were stated. The presented knowledge in this study can help to provide a deep overview of using CFD in studying the efficiency of solar stills and inspire researchers to identify future research ways and gaps.

Small communities and most rural settlements in the Kingdom of Saudi Arabia (KSA) store domestic wastewater in residential septic tanks and transport it to the nearest centralized wastewater treatment plant. Without a sanitary sewerage system, the residents encounter various socioeconomic and environmental challenges related to sewage collection vehicles, the production of objectionable gases, and leaking septic tanks. The present study developed a resident perception-based methodology to appraise the sustainability of a low-cost ceramic filter bioreactor-type decentralized wastewater treatment system (DWWTS) for a small community of 1300 residents (160 households) in Qassim (KSA). In addition to six demographic factors, nine indicators assessed residents’ perceptions about existing and proposed wastewater management systems. A hierarchical-based system of sub-indices evaluated the three dimensions of sustainability using four environmental, nine social, and three economic indicators. The indicators translated into dichotomous questions posed to 34 respondents in the study area. The statistical analysis assessed the association of responses with the willingness to accept (WTA) the proposed DWWTS. A subjective rating scheme translated the responses into performance scores, and a fuzzy-based method aggregated the scores into sub- and top-level indices. The top of the hierarchy showed a close agreement between the resident’s perception and DWWTS’ sustainability. The study found that residents’ knowledge about environment and resource conservation resulted in a moderately high willingness to reuse treated effluent and WTA the decentralized system. The study also showed that the economic viability of a DWWTS remained at a moderate performance level due to a low monthly waste disposal cost. The study’s findings present a high potential for sustainable community-maintained DWWTS initially supported by the government. The proposed approach facilitates decision-makers working in ministries concerning water resources, environmental protection, and agricultural production in evaluating the sustainability of DWWTS for small communities in arid regions.

This study aimed to evaluate the environmental benefits of utilizing by-products from olive farms and olive oil mills within the framework of sustainable resource management and the reduction in agricultural waste, through an integrated circular approach that involves composting and bioenergy recovery. A total of 10.7–11.2 t/ha of biomass, including pruning residues and olive pomace, was generated, with a utilization efficiency of 63.5–67.5%. The energy potential of olive biomass was highlighted through assessments that revealed a theoretical generation potential of approximately 96 GJ/ha (25–28 MW·h/ha), primarily from repurposed woody biomass and pomace. The environmental analysis showed a 50–60% reduction in greenhouse gas emissions compared to conventional disposal, due to avoided open burning, carbon stabilization via compost, and the displacement of fossil fuels. Economically, the circular strategy yielded a net benefit of ~70 $/ha, with revenues from bioenergy and compost exceeding processing costs. Soil organic matter increased from 1.3% to 1.5% after compost application, improving fertility and water retention. The waste reduction percentage reached ~65%, significantly decreasing the volume of unutilized biomass. These outcomes, confirmed through statistical and correlation analyses, demonstrate a robust model for circular agriculture that enhances energy self-sufficiency, mitigates the environmental impact, and supports economic and agronomic sustainability. The findings offer a replicable framework for transforming olive farming waste into valuable bioresources.

This study evaluates the physical, chemical, and biological properties of wastewater—comprising domestic sewage and agricultural drainage water—both before and after treatment to assess the efficiency of the applied processes. The physical properties, including total suspended solids (TSS) and color, demonstrated significant improvements post-treatment, with TSS reduction reaching 91.4% and color removal at 99.5%, indicating the effectiveness of ultrafiltration and coagulation techniques. Chemically, the total dissolved solids (TDS) concentration decreased from 838.2 to 375.5 mg·L⁻¹, aligning with environmental standards and ensuring suitability for irrigation. Additional reductions were observed in biochemical oxygen demand (BOD) and chemical oxygen demand (COD), with removal efficiencies of 86.5% and 83.7%, respectively, highlighting the system’s capability in reducing organic pollutants. Biologically, the treatment process achieved 99.9% removal efficiencies for both Total Coliform and E. coli, meeting world health organization (WHO) guidelines for microbial safety. The water quality index (WQI) analysis classified the treated water in the “Excellent” category, demonstrating an overall enhancement in water quality. Beyond these performance evaluations, this study introduces a novel approach by employing conventional treatment techniques on a blended wastewater—comprising domestic sewage and agricultural drainage water—operated under real operational conditions to achieve safe and sustainable irrigation reuse. This study hypothesizes that the synergistic integration of ultrafiltration with sodium hypochlorite disinfection—without relying on biological treatment—can significantly enhance water quality for sustainable irrigation.