Antibacterial, antifungal, and phytochemical properties of ethanolic extract.

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 against potato pathogenic fungal and bacterial pathogens. The isolated fungal isolates were unequivocally identified as and based on morphological characteristics and internal transcribed spacer genetic sequencing data.

AI-Assisted Cotton Grading: Active and Semi-Supervised Learning to Reduce the Image-Labelling Burden.

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.

Recycling rice straw ash to produce low thermal conductivity and moisture-resistant geopolymer adobe bricks.

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.