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• DEVELOPMENT OF INTELLIGENT INTEGRATED SYSTEM FOR "SMART" AGRICULTURAL PRODUCTION

  Z.V. Nagoev, V. М. Shuganov, А.U. Zammoev, К. C. Bzhikhatlov, Z.Z. Ivanov

  2022-04-21

  Abstract ▼

  The production of agricultural goods is currently associated with the use of digital technologies,
  elements of precision farming, automation and robotization of agriculture. These technologies
  make it possible to carry out continuous monitoring, carry out timely processing, improve the
  efficiency of production and use of resources. The need for the integrated use of digital technologies
  and artificial intelligence and the creation of intelligent integrated systems for agricultural
  production is noted. Studies show that IT-technologies are actively used in field farming when
  growing grain crops. The main crop in the production of breeding, seed and commercial grain in
  the Kabardino-Balkarian Republic is corn, so it is assumed that the intelligent system of the "smart
  field" should be developed initially for this particular crop, and then, with some modifications,
  used for the production of any crop products – other types of grain, vegetables, fruits, grapes and
  gourds. It allows you to reduce human participation at some stages of production by automating
  the process and controlling it through various "smart" devices. The operation of the "smart field"
  system is based on the use of a variety of sensors, including those installed on mobile equipment
  (ground and air manned and unmanned vehicles, space satellites) and portable portable devices to
  obtain operational data on the state of fields and crops. This allows: – analyze the readiness of
  agricultural land for sowing, monitor the progress of plant vegetation in order to effectively and
  efficiently plan agrotechnical measures (chemical protection against pests and diseases, fertilizing,
  irrigation, etc.); – predict production efficiency indicators (total gross harvest, yield per hectare), as well as timely identify production risks (appearance of pests, plant diseases, soil salinity,
  etc.). – make effective decisions on managing the use of resources of agricultural enterprises. With
  the use of "smart" devices, it became possible to introduce the so-called. "precision farming" to
  manage crop productivity, taking into account changes in the plant habitat. Ultimately, this makes
  it possible to solve two main tasks of agricultural producers - increasing yields and reducing
  costs. The authors have developed the concept of an intelligent integrated system "Smart Field" for
  the production of corn grain using advanced robotic systems and complexes. The architecture of
  the "Smart Field" system for the production of seed and commercial corn is presented, which can
  be adapted with minor modifications for the production of other crop products.

• DEVELOPMENT OF AN INTELLIGENT ROBOTIC HARVESTING SYSTEM

  Z.V. Nagoev, О.Z. Zagazezheva, К.C. Brzhikhatlov, I.А. Mambetov

  2025-04-27

  Abstract ▼

  In the context of the need to ensure food security, the tasks of optimizing production processes in the
  agricultural sector are becoming relevant. For example, given the shortage of labor in agriculture, it is
  necessary to develop and implement robotic systems to automate the processes of plant care, harvesting
  and processing. The article presents the results of the development of an autonomous robot for picking
  apples, created on the basis of a universal anthropomorphic robot developed at the Kabardino-BalkarianScientific Center of the Russian Academy of Sciences. The robot is equipped with two multi-link manipulators
  similar to human hands, which allows it to perform complex harvesting tasks. To ensure intelligent
  control of the entire system, a multi-agent neurocognitive architecture is used, which imitates the work of
  the human brain and allows the robot to adapt to changing environmental conditions. The robot is
  equipped with a set of sensors, including video cameras, ultrasonic and infrared rangefinders, lidar and
  encoders on the manipulator drives. This allows it to accurately determine the location of apples, assess
  their ripeness and plan the trajectory of the manipulators. Particular attention is paid to the development
  of a gripper that imitates a human hand and allows you to adjust the squeezing force, which minimizes the
  risk of damage to the fruit. A multi-agent neurocognitive architecture is used to control the robot, which
  provides autonomous decision-making based on sensor data. The system is able to build a map of the area,
  determine the position of the robot and plan a route, as well as recognize apples and assess their condition.
  The article also considers the problems associated with the automation of harvesting in agriculture,
  including a lack of labor and crop losses due to improper operation of equipment. The authors emphasize
  that automation and robotization of harvesting processes have great potential, especially for crops
  that require an individual approach, such as fruits and vegetables. The presented robot demonstrates high
  efficiency in solving these problems, which is confirmed by the results of field tests. The developed system
  can be adapted to work with other crops, which makes it a universal solution for the agricultural industry

• NEUROCOGNITIVE METHODS AND ALGORITHMS OF FEDERATED LEARNING OF INTELLIGENT INTEGRATED INFORMATION MANAGEMENT SYSTEMS IN A REAL COMMUNICATIVE ENVIRONMENT

  Z.V. Nagoev, K.C. Bzhikhatlov, O.Z. Zagazezheva

  2024-04-15

  Abstract ▼

  Unlike existing methods of teaching artificial intelligence systems, approaches based on federated
  learning will not require a long and expensive procedure for preparing a training sample when
  creating and mass practical application of "smart" agricultural systems, autonomous unmanned
  agricultural machines and robots, and the knowledge obtained by the decision-making system will be
  updated on an ongoing basis. The aim of the research is to develop and implement end-to-end artificial
  intelligence technology, the lack of which today prevents the creation of integrated information
  management systems for crop and livestock production ("smart" agricultural systems) based on the
  group application of unmanned ground and aerial agricultural machines and robots. The introduction
  of such intelligent systems is necessary to preserve and improve the products produced and ensure
  the sustainable development of agriculture. The article describes neurocognitive methods and
  algorithms of federated learning of intelligent agricultural process management systems in a real environment. The structure of data and knowledge exchange in the smart field system based on a
  distributed network of intelligent agents managing smart field systems on various agricultural lands,
  based on federated learning, is also proposed. Each intelligent agent is a software model of the neurocognitive
  processes of reasoning and decision-making within the framework of solving a specific
  task. The proposed structure will facilitate the joint accumulation of a knowledge base in the field of
  agriculture and will be able to become the basis for many different intelligent agents that effectively
  perform specific tasks within a distributed network of smart field management systems. There is also
  a description of intelligent agents performing various tasks in a real environment. Examples of autonomous
  robotic and software complexes being developed are given, on the basis of which it is
  planned to test the proposed concept of federated training of "smart" field systems. At the same time,
  the article describes the expected effects of the introduction of technologies based on the developed
  methods and algorithms of federated training of intelligent agents controlling smart field systems.