Urban Agrotechnologies (City-Farming) as a Perspective Branch of Development of World Agribusiness and the Way to Improve the Cities Food Security

Rutkin, Natan Mikhailovich; Lagutkin, Oleg Yurievich; Lagutkina, Lina Yurievna

doi:10.24143/2073-5529-2017-4-95-108

Vestnik of Astrakhan State Technical University. Series: Fishing Industry

2017

DOI: 10.24143/2073-5529-2017-4-95-108

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Urban Agrotechnologies (City-Farming) as a Perspective Branch of Development of World Agribusiness and the Way to Improve the Cities Food Security

Natan Mikhailovich Rutkin¹,

Oleg Yurievich Lagutkin

Lina Yurievna Lagutkina

Abstract: Исследуются перспективы развития мирового урбанизированного агропроизводства (сити-фермерства) на основе анализа ключевых инновационных технологических и рыноч-ных трендов. Отмечается, что тенденции к сокращению объема плодородных земель, исчер-панию ресурсов экосистем, в том числе ресурсов Мирового океана, негативные последствия изменений климата являются основными лимитирующими факторами при развитии тради-ционного сельскохозяйственного производства и перспективного обеспечения растущего населения планеты. О… Show more

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Cited by 4 publications

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Сравнение И Отбор Ситуаций В Системах Вывода Решений На Прецедентах Для «умной» Фермы

Glukhikh,

Prokhoshin,

Glukhikh

2023

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The trend of development of smart farms is aimed at their becoming fully autonomous, robotic enterprises. The prospects for the intellectualization of agricultural production and smart farms, in particular, today are associated with the development of technology systems used to detect, recognize complex production situations and search for effective solutions in these situations. The article presents the concept of such a decision support system on smart farms using the method of decision support based on case-based reasoning - CBR system. Its implementation requires a number of non-trivial tasks, which include, first of all, the tasks of formalizing the presentation of situations and creating methods for comparing and retrieving situations from the KB on this basis. In this study, a smart farm is presented as a complex technological object consisting of interrelated components, which are the technological subsystems of a smart farm, the products produced, the objects of the operational environment, as well as the relationships between them. To implement algorithms for situational decision-making based on precedents, a formalized representation of the situation in the form of a multivector is proposed. This allowed us to develop a number of models of the trained similarity function between situations. The conducted experiments have shown the operability of the proposed models, on the basis of which ensemble architecture of a neural network has been developed for comparing situations and selecting them from the knowledge base in decision-making processes. Of practical interest is monitoring the condition of plants by their video and photo images, which allows detecting undesirable plant conditions (diseases), which can serve as a signal to activate the process of searching for solutions in the knowledge base.

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Сравнение И Отбор Ситуаций В Системах Вывода Решений На Прецедентах Для «умной» Фермы

Glukhikh,

Prokhoshin,

Glukhikh

2023

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Neural Network and Home Hydroponics

Бородулин

Shafrai

Максименко

2023

Food Processing: Techniques and Technology

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Hydroponics is a method of soilless cultivation of plants. It shortens the vegetation period, reduces the risk of disease and insect infestation, and provides a year-round growing cycle. Hydroponics depends on efficient water management. It is associated with a complex design, operation, and maintenance. Neural networks can control complex technological processes in agriculture. The research objective was to use a neural network to increase the efficiency of a home hydroponics system. The study involved a nutrient bed hydroponics setup with ten Lactuca sativa plants. Sensors collected information about the temperature and humidity of air, illumination, and the temperature of the leaf surface. Data processing, neural network training, and microcontroller programming relied on Python 3, PyTorch, and MicroPython. The four-layer perceptron, which is a popular control mechanism, turned out to be the most effective neural network architecture. Fewer layers resulted in a high error rate (≥ 5%). When the number of layers was > 4, the error level remained at that of the four-layer experiment (0.2%). Further practical tests showed an increase in energy efficiency by 32.3%, compared to the classical control algorithm at close values of plant transpiration. Neural net technology could be integrated into energy-saving residential premises and smart home systems in order to increase the self-sufficiency of hydroponics installations.

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Methods improving practical implementation of technology of combined recirculating fish farming and intensive crop production

Evgrafova

Nevalennyy

Lagutkina

et al. 2021

Vestnik of Astrakhan State Technical University. Series: Fishing Industry

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The article focuses on the problem of adopting the effective methods for combined cultivation of food products in cities, which requires the rapidly deployable and scalable aquaponic urban farms for year-round cultivating the aquaculture and crop products. Experimental work on cultivating the agricultural products in a small-sized system of combined recirculating fish farming and intensive crop production (aquaponics) has been carried out. A well-grounded idea of the qualitative and quantitative requirements for feed formulations, the possibilities of their optimization due to including the ingredients of natural origin was obtained. With combined recirculating fish farming and intensive crop production the control over the physiological state of the aquatic organisms grown on feed on their own formulation is carried out on the analysis of special biochemical parameters, which complement the general characteristics of the limits of reference values of homeostasis constants and determine the physiological status of fish. Feeding rates (% of body weight) of fish determine the amount of organic load for nutrition of plant growing objects, in particular leafy greens. In the course of research it was found out that when feeding sterlet (100 g) with the developed feed with protein content 45% the feeding rate is 4% of the body weight. The equivalent area 1 m2 with leafy greenery accounts for 40–50 g of feed with a mass of water bodies up to 5.0 kg/m3.

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Urban Agrotechnologies (City-Farming) as a Perspective Branch of Development of World Agribusiness and the Way to Improve the Cities Food Security

Cited by 4 publications

References 3 publications

Сравнение И Отбор Ситуаций В Системах Вывода Решений На Прецедентах Для «умной» Фермы

Сравнение И Отбор Ситуаций В Системах Вывода Решений На Прецедентах Для «умной» Фермы

Neural Network and Home Hydroponics

Methods improving practical implementation of technology of combined recirculating fish farming and intensive crop production

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