Yun-Yang Chao, YingTzy Jou, P. Skobelev, V. Ermakov, E. Simonova. Developing digital platform and eco-system of smart services for rice cultivation // Proceedings of the 4th International Agriculture Innovation Conference (IAIC 2019), August 08-09, 2019, Oulu, Finland. – P. 57-59.

Rice is not only the main food in Asia but also is one of the top five foods in the world. According to United Nations statistics, the current world population has exceeded 7 billion people, and the demand for rice has also increased year by year. The Food and Agriculture Organization (FAO) of the United Nations notes that world rice consumption increased by 1.1% to 503.9 million tons in 2017/18. By 2018/19, world rice utilization will increase by another 520 tons to reach 509.1 billion tons (FAO, 2018).
But growing demand for rice can meet in near future a number of issues with production and supply because many countries in the world are currently facing a number of new challenges including global climate changes, growing demand for soil carbon sequestration, etc. It requires that agriculture need to become more smart, flexible and adaptive — to provide high quality of products, better efficiency and productivity, and finally the competitiveness and sustainability of countries development.
In this paper we present first vision of international Taiwan-Russia project on the development digital platform and eco-system of smart services for rice cultivation.
The project is oriented on new coming era of Industry 5.0 which is associated with the next step from automation of physical processes, data integration and visualization (as it is currently considered in Industry 4.0) – to Artificial Intelligence (AI) for supporting cooperation of humans and robots in managing organizations (so called “Augmented
Intelligence”).
More specifically the objective of the project will be the developing of new models, methods and tools for the digitalization of domain-specific knowledge and the automation of coordinated decision making between smart services designed as a potentially autonomous cyber-physical systems with the use of ontologies and multi-agent technology.
There are now a number of digital platforms and services on the market which are already well-developed and used in practice, but they have a number of limitations. For example, some of them are closed for end-users and developers or dictate their rules to farmers. Some other not allow users to have access to all resources in one mobile phone. Third are focused on accounting and routine automation and not provide farmers with smart services supporting knowledge-based decision making in problem situations and every day operations, etc.
In the proposed project the new concept and prototype of open digital inter-cloud AI platform and eco-system of smart services for rice cultivation will be developed.
The functionality of the platform and eco-system will help farmers:

  • select varieties for planting, depending on the type and composition of the soil;
  • determine the patterns of good, normal and weak rice growth;
  • analyze the state of the rice, discover problems, find a solution and adjust work plans;
  • make recommendations on the use of fertilizers or pesticides for rice, etc.

To solve a problem we propose to develop inter-cloud platform which will provide and combine traditional and AI services with ability to discover problem situations, find possible solutions and make negotiations between services for taking coordinated decision — collecting required data from existing platforms by specified protocols and integrating knowledge on fields and crops, machines, agro-technologies, etc.
The first prototype of digital eco-system of smart services will be focused on forming knowledge base for farm management, collecting data from sensors, hyper-spectrum analysis of images of fields, forming crop rotation plans and scheduling humans and machines with economic estimates. The digital platform will be designed as an open system — and smart services developed in one country will have chance to enter eco-system for interest of concrete farmer working in any other country.
As a first services for rice cultivations we consider the following services:
1) Knowledge Base on rice growth and physiological prediction for assessing the state of soil
and plants.
At present, different rice cultivation technologies significantly affect the quality and yield of rice as well as climate events, water shortage and other factors. Agriculture must be oriented to a scientific, intelligent, and value-based approach to effectively manage and reduce costs and labor expenditures. Collect the growth parameters of the plant cultivation process, establish a digital database, and then analyze the crop growth pattern by the agricultural big
data model to effectively predict the crop production capacity. In this development rice will be treated with silicon fertilizer, and the growth, physiology, disease and microclimate data of rice development will be collected, and a database of rice growth and climate will be established to provide a reference for subsequent growth pattern analysis.
2) Smart service based on spectroscopy for rapid analysis of soil.
With the development of refined agriculture, people know that in order to maintain the fertility of the soil and improve the soil structure, the soil must have large amount of organic matter. Therefore, the demand for soil and organic material in cultivated land is increasing, but the traditional physical and chemical analysis of soil is time-consuming and tedious. To solve this problem, the research hopes to develop a method for rapid analysis of soil. The study will divide a field in Guanshan, Taitung, into three sections and assigned one agricultural cultivation treatment with different fertilizers and probiotics to each section. Different spectra of soil samples from each treatment will be collected and compared for supporting decision making processes.
Expected results for the project:

  • The concept of an open digital inter-cloud platform and eco-system of smart services for rice cultivation.
  • The basic ontology and knowledge base on the most advanced techniques and tools of rice cultivation for generating recommendations to farmers.
  • The distributed architecture of the platform and eco-system of smart services to provide openness, flexibility and efficiency, high performance, reliability and security.
  • Models, methods and tools to support collective decision-making and negotiations between agents of smart services in digital eco-system.
  • Prototypes of smart services of crop cultivation to the level of each field.
  • The inter-cloud integration of smart services into a digital eco-system for rice cultivating
    and the study of its applicability on selected farms.
    In future the number of such services could be extended involving governmental organizations, universities, commercial companies, start-ups, etc
    The first prototype will be focused on the domain of rice production but the main part of the platform and eco-system will be also applicable for wheat production, tropical fruits, etc.

Petr Skobelev, Vladimir Laryukhin, Igor Mayorov, Elena Simonova, Olga Yalovenko. Smart Farming – Open Multi-Agent Platform and Eco-System of Smart Services for Precise Farming // Y. Demazeau et al. (Eds.): Proceedings of the 17th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2019), Ávila, Spain, 26-28 June, 2019.  – PAAMS 2019, LNAI 11523, pp. 212–224, 2019. https://doi.org/10.1007/978-3-030-24209-1_18

The paper is addressing new challenges in agriculture, which are becoming nowadays critical for many countries, including climate changes, exhausted soils, aged farmers, etc. One of the new trends is associated with a step from Agriculture–4.0 focused on automation of physical processes for precision farming – to Agriculture–5.0 based on Artificial Intelligence (AI) for digitalization of domain knowledge and automation of farmer decision-making processes. A brief overview of existing IT systems for precision farming is given, key limitations are discussed and business requirements for developing AI solutions are formulated. The concept of digital eco-system of smart services for precision farming is proposed based on AI-technologies. The paper presents functionality and architecture of multi-agent platform and eco-system and identifies vitally important smart services for everyday operations of farmers. The structure and content of ontology-driven knowledge base for precision agriculture is considered, aimed at formalizing specifications of modern types of crops, agro- and bio-technologies, etc. The virtual “round table” is proposed as a generic framework for forming well-balanced recommendations for farmers with the use of ontology-based model of agricultural enterprise, which forms a specification of situation for automatic decision-making. Finally, the first case studies of the industrial prototype of the solution development are discussed.

E.Pantelej, N.Gusev, G.Voshchuk and A.Zhelonkin. Automated Field Monitoring by a Group of Light Aircraft-Type UAVs // Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (Eds.). Proceedings of the 3rd International Scientific Conference «Intelligent Information Technologies for Industry», September 17-21, 2018, Sochi, Russia. Volume 2. – Advances in Intelligent Systems and Computing, Series Volume 875.  Springer, Switzerland. – P. 350-358. ISBN 978-3-030-01821-4. DOI: 10.1007/978-3-030-01821-4.

This paper provides an overview of some existing methods of Earth remote sensing (ERS) using for agricultural needs. Special emphasis is placed on sensing with the help of UAVs. The paper describes the developed software and hardware complex for an aircraft-type UAV group. The proposed solution significantly increases the operating time and automates the process of monitoring agricultural areas. In addition, legislative restrictions on the use of UAVs are considered.

Budaev, A. Lada, E. Simonova,  P. Skobelev, V. Travin, O.Yalovenko, G. Voshchuk, A. Zhilyaev. Conceptual design of smart farming solution for precise agriculture // International Journal of Design & Nature and Ecodynamics. – WIT Press, vol. 13(2018), is. 3. – pp. 307-314. DOI: 10.2495/DNE-V13-N3-307-314.

The paper presents precise agriculture as a complex adaptive system with high level of uncertainty and dynamics, in which knowledge is forming experimentally and in a very enterprise-specific way. Is there any opportunity to learn the best practices from advanced precise farmers, transfer their knowledge and support everyday decision making for regular farmers? The concept of Smart Farming as an augmented AI solution for precise agriculture is proposed. The solution is designed as a digital eco-system (system of systems) of smart services, where each service, in its turn, is an autonomous AI system. The paper also discusses functionality of smart services for precise agriculture and the service-oriented architecture of the solution with p2p interaction of services. Ontology-driven knowledge base and multi-agent technology are considered as the key technologies of the solution. The virtual “round table” for coordinated decision making of smart services is introduced. Finally, the paper presents results of the first applications, as well as the future steps and expected results.