Agricultural productivity remains a critical determinant of economic stability, food security, and sustainable development in both developing and developed economies. The increasing demand for high-quality agricultural products, coupled with the need for efficient resource utilization, necessitates the integration of advanced biological engineering systems and optimization techniques. This study presents an analytical modeling framework that leverages biological engineering principles, machine learning-based classification, and non-destructive sensing technologies to enhance agricultural productivity. The research synthesizes existing methodologies such as image-based fruit grading, near-infrared spectroscopy, and automated sorting systems into a unified optimization model. Empirical insights derived from existing literature are integrated to demonstrate the effectiveness of system-level optimization in improving yield quality, reducing post-harvest losses, and enhancing export competitiveness. The proposed model also incorporates economic data trends to align productivity improvements with market demands. The findings suggest that a systems-based analytical approach significantly improves efficiency across agricultural value chains. However, implementation challenges such as technological accessibility, cost constraints, and data integration limitations remain critical considerations. The study contributes to the advancement of agricultural engineering by providing a scalable and adaptable framework for optimizing biological systems in agriculture.

Milk and dairy products are of great importance from the perspective of food safety, and the presence of antibiotic residues in their composition may pose a potential risk to human health. The widespread therapeutic use of antibiotics in livestock practice can lead to the occurrence of these residues in milk when withdrawal periods before milking are not properly observed. Therefore, the detection and quantitative assessment of antibiotic residues in milk is considered a relevant scientific problem.

This study was designed as an experimental laboratory investigation in which 30 raw milk samples obtained from different sources were analyzed. High-performance liquid chromatography (HPLC) was applied as a confirmatory method for the detection and verification of antibiotic residues. Within the study, antibiotics belonging to the β-lactam, tetracycline, and sulfonamide groups were identified on the basis of retention time agreement and chromatographic peak characteristics, and their concentrations were calculated using calibration curves. The obtained results were compared with internationally accepted maximum residue limits (MRLs).

According to the analytical results, antibiotic residues were detected in 60% of the milk samples (18 samples), while in 4 samples (13.3%) the antibiotic concentrations were close to or exceeded the MRL values. The HPLC method demonstrated high sensitivity, selectivity, and repeatability, confirming its reliability for quantitative determination of antibiotic residues in complex matrices such as milk.

The novelty of this study lies in the quantitative assessment of antibiotic residues in milk under local conditions using a modern confirmatory instrumental method and in the evaluation of milk safety by comparing the obtained data with international MRL requirements. The results scientifically justify the need to strengthen veterinary control and introduce regular laboratory monitoring throughout the milk production chain.

In this article, the issue of creating a differential equation of fiber movement in determining the microneural index of cotton fibers using a universal tool is considered. In the study, the effects of electrostatic force, gravity and acoustic pressure affecting the movement of cotton fibers in the acoustic chamber were theoretically analyzed. Differential equations representing the movement of fibers were created, and based on their solutions, the laws of vertical movement of fibers were determined. Also, the effect of acoustic impact of different frequencies on fibers on their movement, amplitude and energy was analyzed through graphs. The obtained results allow to increase the efficiency of using the acoustic method in determining the microneural index of cotton fiber and to improve the fiber sorting process.

The growth-stimulating potential of four salt-tolerant Bacillus strains (B. subtilis 7А, 9А, 10А and B. proteolyticus 33S) on cotton (Gossypium hirsutum L.) was studied in vitro. Seed inoculation with the strains significantly increased germination, shoot and root length, as well as fresh and dry weight compared to the control. The most pronounced growth-promoting effect was observed with the B. proteolyticus 33S strain, which increased shoot length by 4.9 cm, root length by 2.72 cm, fresh weight by 0.20 g, and dry weight by 0.03 g relative to the control. The obtained results allow us to recommend the studied strains for further research with the aim of their future use in microbial biopreparations aimed at stimulating plant growth and improving the properties of saline soils.

The article gives a botanical description, biology, growth and development, flowering, reproduction, chemical composition, use in everyday life in medicine, in the elimination of rodents, as well as harvesting and storage, diseases and pests of green-flowered black corn - Cynoglossum viridiflorum Pall.ex. Lehm. In the Republic of Karakalpakstan.

This article examines the biological characteristics, morphology, karyotype, and legal status of Cannabis sativa L. in Uzbekistan. The structural and functional features of the root system, stem, leaves, flowers, fruits, and seeds are comprehensively described. Particular attention is given to cytogenetic characteristics, including the organization of metacentric and submetacentric chromosomes, nucleolar organizer regions, and the chromosomal mechanism of sex determination. Biological and morphological distinctions among Cannabis sativa, Cannabis indica, and Cannabis ruderalis are comparatively analyzed. In addition, the regulatory framework governing the cultivation and utilization of industrial cannabis in the Republic of Uzbekistan, together with international practices, is reviewed. The study emphasizes that modern genomic and biotechnological approaches considerably expand the potential applications of cannabis in medicine, industry, and agriculture.