Vol. 4 No. 03 (2024)

In the storage of cotton raw materials, the importance of modern tool equipment is considered high. This article is about the modern technologies used in the cotton cleaning plant “Bek cluster”, which was established in Mirzaabad District of Syrdarya region, the impact of moisture on fiber quality in the process of processing cotton.

In this article, the results of the study have been discussed on the cultivation of Uzbekistanskaya (st), Uzbekistan guzali (st), Redgauntlet (st), Zenga-Zengana, Medvey, Velikan, Dana varieties of strawberry that had been planted in experimental plots of strawberries in open field conditions at the scientific research institute of horticulture, viticulture and winemaking named after Academician M. Mirzaev in Tashkent region. The duration of phenological phases and periods of ripening of berries in the varieties Vnuchka, Victoria and Zamposhniya were also monitored.

As a result, the ripening of strawberry varieties lasted from April 6 ("Victoria") to April 13 ("Uzbekistanskaya", "Redgauntlet"). The difference between varieties was 8 days. Fruit ripening was observed from May 3 ("Victoria") to June 24 ("Uzbekistanskaya" (st), "Zenga-Zengana"). It was observed that the duration of ripening was from 26 to 41 days in the section of varieties indicated in the experiment.

The analysis of the conducted experiments showed that when 40 tons of organic fertilizers were used in grape varieties, the best development of the root system was 302 cm in the Toyfi rozovy variety, 201 cm in the Kishmish Batir variety, and 295 cm in the Rkatsiteli variety when 40 tons of organic fertilizers were applied.

This comprehensive review delves into the intricate interplay of chemical and biological processes within anoxic waterlogged soil environments. Through an exploration of current research and methodologies, the review elucidates the complex dynamics governing microbial activities, nutrient cycling, and redox reactions in these unique ecosystems. Understanding these interconnected processes is vital for advancing our knowledge of soil ecology, biogeochemistry, and ecosystem functioning. The review synthesizes key findings, identifies gaps in current understanding, and offers insights into future research directions aimed at unraveling the complexities of anoxic waterlogged soil environments.

The article presents the results of field experiments conducted to study the effect of mineral fertilizers of different standards on the photosynthetic productivity and pure photosynthesis productivity of winter wheat variety "Asr" in the conditions of irrigated light gray soils of Navoi region. It was found that the photosynthesis productivity was determined by the accounting balance of mineral fertilizers and the methods of soil plant diagnostics, and the options used in combination with pesticides with biologically active organic and mineral fertilizer Rokogumin were the highest.

The article offers the findings of a scientific study on the most suitable times to plant early-ripening potato cultivars in the northern districts of the agrobiocenosis of Karakalpakstan.

In this article, the analysis of field leveling and compacting machines used in the preparation of land for planting seeds and the research conducted on them, the improvement of the technological work process of the leveler and the justification of its parameters, the improvement of work quality and productivity in the preparation of land for planting seeds of seeds and other crops are discussed.

Citrus Huanglongbing (HLB) poses a significant threat to citrus production worldwide, necessitating efficient detection methods for early disease identification. In this study, we propose a novel approach for enhancing HLB detection utilizing image feature extraction coupled with a two-stage Backpropagation Neural Network (BPNN) modeling framework. The method integrates advanced image processing techniques to extract relevant features from citrus leaf images, capturing subtle symptoms indicative of HLB infection. Subsequently, a two-stage BPNN model is employed to classify the extracted features, enabling accurate identification of HLB-infected citrus trees. Experimental results demonstrate the effectiveness of the proposed approach in achieving high detection accuracy and robustness across diverse citrus varieties and environmental conditions. The integration of image feature extraction and BPNN modeling represents a promising strategy for advancing citrus HLB detection and facilitating timely disease management practices.