Studies in the article have shown that productivity increases with improved structure. This is explained by the fact that in soils with 0.25 mm diameter water-resistant aggregates of 14%, grain yield is 22.2 cents / ha, while water-resistant aggregates are 8%. In soils, this figure decreased to 18.4 cents / ha (3.8 cents / ha). It is also known that alfalfa plays a key role in improving the water-physical properties of the soil, as well as its agrochemical composition. The author's research shows that the amount of water-resistant aggregates under the clover is much higher than in the cotton fields. This can be clearly seen from the following comparison. Thus, the amount of water-resistant aggregates in 0-10 cm of soil in the cotton field is 4.0-18.5; While 0.5 cm is 6.5-11.2 and 20-30 cm is 4.5-18.2, in clover crops this indicator is 35.0; Increased to 24.7 and 27.0. In addition, it revealed the accumulation of more organic and mineral substances under alfalfa cultivation. They proved this by the analysis of soil samples taken from the one-year and two-year plots. It was found that 1.66% humus and 0.112% total nitrogen were accumulated in the topsoil of the annual alfalfa field, while the amount of humus accumulated in the topsoil in the biennial clover field was 1.70% and the total nitrogen content was 0.150%. It should be noted. that the development of irrigation erosion in irrigated arable lands depends on the fact that the surface of the area is covered with a large cover. This was clearly shown by the observations. It was found that both relatively weak (0.4 mm / min) and very (1.4 mm / min) heavy rains protect clover soil from further washing. Thus, 0.4 mm / min. In heavy rains, the depth of the furrow under alfalfa is 14.4 mm, 31 mm at 1.4 mm / min, 50.9 and 64.2 mm between rows of cotton, respectively, and 78.6 and 113 mm along the row. 6 mm

The rapid proliferation of cloud infrastructures has transformed data management, enabling organizations to process, store, and analyse vast amounts of data with unprecedented efficiency. However, the reliability of data pipelines within these infrastructures remains a significant challenge, plagued by issues such as data latency, corruption, system downtime, and scalability constraints. Traditional approaches to ensuring pipeline reliability, including manual monitoring and reactive fault management, often fall short in meeting the demands of modern, high-volume data ecosystems.

This research explores the potential of artificial intelligence (AI)-driven solutions to enhance the reliability of data pipelines in cloud infrastructures. By leveraging machine learning and advanced analytic, AI offers innovative methods for real-time anomaly detection, predictive maintenance, and performance optimization. The study begins with a comprehensive review of the current state of data pipeline management in cloud environments, identifying key challenges and limitations of conventional techniques.

Findings reveal that AI-driven approaches significantly outperform traditional methods, offering proactive and scalable solutions for managing data pipelines. However, the study also addresses critical challenges, such as the computational cost of AI models, data quality issues, and ethical considerations surrounding data privacy. Future research directions include the integration of AI with edge computing and the development of lightweight, cost-effective AI models tailored for cloud infrastructures.

The convergence of Artificial Intelligence (AI), Cloud Computing, and Edge Computing is rapidly reshaping the technology landscape, creating innovative solutions across industries. This paper explores the synergistic relationship between these three technologies, highlighting their individual contributions and the transformative impact of their integration. AI, with its ability to analyze and make decisions from vast amounts of data, benefits from the computational power and scalability offered by Cloud Computing, while Edge Computing enables real-time data processing closer to the source, reducing latency and enhancing efficiency. Together, these technologies are driving advancements in fields such as healthcare, automotive, manufacturing, and smart cities, unlocking new business models, improving operational efficiency, and providing real-time decision-making capabilities. However, challenges related to data security, privacy, network reliability, and ethical considerations remain significant barriers to widespread adoption. Looking ahead, the convergence of AI, Cloud, and Edge is poised to continue evolving, influencing future technology trends and shaping the digital transformation across industries. This paper examines the current state of this convergence, its potential applications, and the implications for businesses and society.