Rice straw ash (RSA) geopolymer adobe bricks were produced using the geopolymerization reaction among the RSA, soil, and alkaline activator at the Biosystem Engineering Department, Faculty of Agriculture, Alexandria University, Egypt, to optimize adobe brick advantages. The bulk density, water absorption, compressive strength, and thermal conductivity of the new composite were measured at RSA contents of 0%, 5%, 10%, and 20% and sodium hydroxide contents of 2.5%, 5%, 7.5%, and 10% after curing the composite for 28 days. Results indicated that increasing RSA from 0% to 20% increased the compressive strength and decreased the bulk density, water absorption, and thermal conductivity. Further, increasing sodium hydroxide from 2.5% to 10% increased the bulk density and compressive strength and decreased the water absorption. Significant effects of RSA and sodium hydroxide percentages and their interaction on all the studied characters were reported. The best conditions to minimize bulk density, water absorption, thermal conductivity, and optimize compressive strength of the composite were at 10% sodium hydroxide and 20% RSA. The minimum bulk density, water absorption, and thermal conductivity were 1.463 g/cm³, 8.3%, and 0.46 W/(m·K), respectively, while the maximum CS was 2.1 MPa after 28 days. Using RSA geopolymer adobe bricks on building interior walls is recommended to decrease bricks' thermal conductivity, water absorption, and weight.

Fossil fuels are the main energy sources responsible for harmful emissions and global warming. Using biodiesel made from waste deep-frying oil as an alternative fuel source in diesel engines has drawn great attention. This biodiesel is produced using the transesterification process and blends with mineral diesel at Faculty of Agriculture Saba Basha, Alexandria University, Egypt. The turbocharged diesel engine of a Kubota M-90 tractor was tested. The objectives of this work are to test tractor as a source of power in the farm using waste deep-frying oil biodiesel to utilize waste frying oils (WFO) in clean energy production on the farm and determine the best engine loading stages to maximize engine efficiencies for different fuel blends and reduce the environmental impact of gas emissions from tractor diesel engines in the farms. The experiment design was factorial, with two factors, where the first was the engine load (0%, 25%, 50%, 75%, and 100%) and the second was fuel blend (0%, 5%, 20%, and 100% biodiesel), and the effects of loading stages and biodiesel percentage on engine performance indicators of engine speed, power take off torque, power take off power, brake power, brake mean effective pressure, brake thermal efficiency, brake specific fuel consumption, and gas emissions were studied. The experimental results indicated that engine load percentage and fuel blend percentage significantly affected all studied characters, and the best engine loading stages were between 25 and 75% to maximize engine efficiency and minimize the specific fuel consumption and gas emissions. Increasing the biodiesel percentage at all loading stages resulted decreasing in Engine brake power (BP), brake thermal efficiency, Power take-off (PTO) torque, and brake mean effective pressure and increases in brake specific fuel consumption. Increasing the engine load resulted in decreases in O₂ emissions and increases in CO₂, CO, NO, and SO₂ emissions. Increasing the biodiesel percentage in the blended fuel samples resulted in increases in O₂ and NO emissions and decreases in CO₂, CO, and SO₂ emissions. The use of biodiesel with diesel fuel reduces the environmental impact of gas emissions and decreases engine efficiency.

The assessment of food and industrial crops during harvesting is important to determine the quality and downstream processing requirements, which in turn affect their market value. While machine learning models have been developed for this purpose, their deployment is hindered by the high cost of labelling the crop images to provide data for model training. This study examines the capabilities of semi-supervised and active learning to minimise effort when labelling cotton lint samples while maintaining high classification accuracy. Random forest classification models were developed using supervised learning, semi-supervised learning, and active learning to determine Egyptian cotton grade. Compared to supervised learning (80.20–82.66%) and semi-supervised learning (81.39–85.26%), active learning models were able to achieve higher accuracy (82.85–85.33%) with up to 46.4% reduction in the volume of labelled data required. The primary obstacle when using machine learning for Egyptian cotton grading is the time required for labelling cotton lint samples. However, by applying active learning, this study successfully decreased the time needed from 422.5 to 177.5 min. The findings of this study demonstrate that active learning is a promising approach for developing accurate and efficient machine learning models for grading food and industrial crops.

The research into the use of plants as plentiful reservoirs of bioactive chemicals shows significant potential for agricultural uses. This study focused on analyzing the chemical composition and potency of an ethanolic extract obtained from the aerial parts (leaves and stems) of Salsola kali against potato pathogenic fungal and bacterial pathogens. The isolated fungal isolates were unequivocally identified as Fusarium oxysporum and Rhizoctonia solani based on morphological characteristics and internal transcribed spacer genetic sequencing data. The antifungal activity of the extract revealed good inhibition efficacy against R. solani (60.4%) and weak activity against F. oxysporum (11.1%) at a concentration of 5,000 µg/mL. The S. kali extract exhibited strong antibacterial activity, as evidenced by the significant inhibition zone diameter (mm) observed in all three strains of bacteria that were tested: Pectobacterium carotovorum (13.33), Pectobacterium atrosepticum (9.00), and Ralstonia solanacearum (9.33), at a concentration of 10,000 µg/mL. High-performance liquid chromatography analysis revealed the presence of several polyphenolic compounds (μg/g), with gallic acid (2942.8), caffeic acid (2110.2), cinnamic acid (1943.1), and chlorogenic acid (858.4) being the predominant ones. Quercetin and hesperetin were the predominant flavonoid components, with concentrations of 1110.3 and 1059.3 μg/g, respectively. Gas chromatography-mass spectrometry analysis revealed the presence of many bioactive compounds, such as saturated and unsaturated fatty acids, diterpenes, and phytosterols. The most abundant compound detected was n-hexadecanoic acid, which accounted for 28.1%. The results emphasize the potential of S. kali extract as a valuable source of bioactive substances that possess good antifungal and antibacterial effects, which highlights its potential for many agricultural uses.

The easiest method for making biodiesel is by using, soy oil, waste vegetable oil, canola oil, or animal fats. The best biodiesel type can be made from waste frying oil, thus it has to be done right. Waste vegetable oil can be provided for free or at a low cost by restaurants and food processors...

Egyptian cotton fibres have worldwide recognition due to their distinct quality and luxurious textile products known by the “Egyptian Cotton“ label. However, cotton fibre trading in Egypt still depends on human grading of cotton quality, which is resource-intensive and faces challenges in terms of subjectivity and expertise requirements. This study investigates colour vision and transfer learning to classify the grade of five long (Giza 86, Giza 90, and Giza 94) and extra-long (Giza 87 and Giza 96) staple cotton cultivars. Five Convolutional Neural networks (CNNs)—AlexNet, GoogleNet, SqueezeNet, VGG16, and VGG19—were fine-tuned, optimised, and tested on independent datasets. The highest classifications were 75.7%, 85.0%, 80.0%, 77.1%, and 90.0% for Giza 86, Giza 87, Giza 90, Giza 94, and Giza 96, respectively, with F1-Scores ranging from 51.9–100%, 66.7–100%, 42.9–100%, 40.0–100%, and 80.0–100%. Among the CNNs, AlexNet, GoogleNet, and VGG19 outperformed the others. Fused CNN models further improved classification accuracy by up to 7.2% for all cultivars except Giza 87. These results demonstrate the feasibility of developing a fast, low-cost, and low-skilled vision system that overcomes the inconsistencies and limitations of manual grading in the early stages of cotton fibre trading in Egypt.