CC BY
5
УДК 004.8
06.01.01 - Общее земледелие, растениеводство (сельскохозяйственные науки)
Автоматизированный системно-когнитивный анализ влияния сроков посева и ширины междурядий на урожайность и качество зерна озимой пшеницы сорта Дон 95
Луценко Евгений Вениаминович д.э.н., к.т.н., профессор Web of Science ResearcherID S-8667-2018 Scopus Author ID: 57188763047 РИНЦ SPIN-код: 9523-7101
prof.lutsenko@gmail.com http ://lc. kubagro .ru
https://www.researchgate.net/profile/Eugene Lutsenko
Лукьяненко Татьяна Викторовна, К.т.н., доцент
Кубанский Государственный Аграрный университет имени И.Т.Трубилина, Краснодар, Россия
Данная работа является продолжением серии работ автора по применению Автоматизированного системно-когнитивного анализа (АСК-анализ) для решения широкого спектра задач в области агрономии, т.е. по когнитивной агрономии. В работе изучается влияние сроков посева и ширины междурядий на урожайность и качество зерна озимой пшеницы сорта Дон 95. Работа может быть основой для лабораторных работ по применению систем искусственного интеллекта, в частности лингвистического АСК-анализа для решения задач в области когнитивной агрономии.
Ключевые слова: ЛИНГАВИСТИЧЕСКИЙ АСК-АНАЛИЗ, ЛИНГВИСТИЧЕСКИЙ АВТОМАТИЗИРОВАННЫЙ СИСТЕМНО-КОГНИТИВНЫЙ АНАЛИЗ, КОГНИТИВНАЯ АГРОНОМИЯ, ИНТЕЛЛЕКТУАЛЬНАЯ СИСТЕМА «ЭЙДОС»,
http://dx.doi.org/10.21515/1990-4665-182-014
UDK 004.8
01/06/01 - General farming, crop production (agricultural sciences)
Automated system-cognitive analysis of the effect of sowing time and row spacing on the yield and grain quality of winter wheat variety Don 95
Lutsenko Evgeniy Veniaminovich
Doctor of Economics, Ph.D., professor
Web of Science ResearcherlD S-8667-2018
Scopus Author ID: 57188763047
RSCI SPIN code: 9523-7101
prof. lutsenko@gmail. com http://lc.kubagro.ru
https://www.researchgate.net/profile/Eugene Lutsenko
Lukyanenko Tatiana Viktorovna,
Candidate of Technical Sciences, Associate Professor
Kuban State Agrarian University named after I.T. Trubilin, Krasnodar, Russia
This work is a continuation of a series of works by the author on the use of Automated System Cognitive Analysis (ASC-analysis) for solving a wide range of problems in the field of agronomy, i.e. in cognitive agronomy. The paper studies the effect of sowing time and row spacing on the yield and grain quality of winter wheat variety Don 95. The work can be the basis for laboratory work on the use of artificial intelligence systems, in particular, linguistic ASC analysis for solving problems in the field of cognitive agronomy.
Keywords: LINGUISTIC ASK-ANALYSIS, LINGUISTIC AUTOMATED SYSTEMIC COGNITIVE ANALYSIS, COGNITIVE AGRONOMY, INTELLIGENT SYSTEM "EIDOS",
CONTENT
1. INTRODUCTION.......................................................................................................................................2
2. METHODS..................................................................................................................................................2
3. RESULTS...................................................................................................................................................3
3.1. Cognitive structuring of the subject area...................................................................................3
3.2. Formalization of the subject area..................................................................................................4
3.3. Synthesis and verification of statistical and system-cognitive models................................7
3.8. Examining the object of modeling by examining its model..........................................................9
3.8.1. SWOT-analysis of the system for determining the future states of the modeling object by the factors acting on it.......................................................................................................................................9
3.8.2. cognitive functions...........................................................................................................................11
4. DISCUSSION...........................................................................................................................................15
5. CONCLUSIONS......................................................................................................................................16
LITERATURE...............................................................................................................................................16
1. INTRODUCTION
This work is a continuation of a series of works by the author on the use of Automated System Cognitive Analysis (ASC-analysis) for solving a wide range of problems in the field of agronomy, i.e. on cognitive agronomy [1, 2, 3]. This paper studies the effect of sowing time and row spacing on the yield and grain quality of winter wheat variety Don 95. The work can be the basis for laboratory work on the use of artificial intelligence systems, in particular, linguistic ASC analysis for solving problems in the field of cognitive agronomy.
2. METHODS
Automated system-cognitive analysis (ASC-analysis) was proposed by Prof. E.V. Lutsenko in 2002 in a number of articles published in 1997-20011and the fundamental monograph [2].
The term itself: "Automated system-cognitive analysis (ASC-analysis)" was proposed by Prof. E.V. Lutsenko. At that time, he did not meet on the Internet at all. Today, according to the corresponding request, Yandex has 9 million sites with this combination of words. . ASC analysis includes:
- theoretical foundations, in particular the basic formalizable cognitive concept;
- a mathematical model based on a systemic generalization of information theory (STI);
- method of numerical calculations (database structures and algorithms for their processing);
- software tools, which is currently the universal cognitive analytical system "Eidos" (intellectual system "Eidos").
"5
In [4], a rather detailed standard (in the IMRAD system) ) description of the application of ASC-analysis and its software tools of the intellectual system "Eidos" for solving a number of problems in the field of cognitive agronomy. Below is the content of the work [4]:
1. Introduction (introduction)
1.1. Description of the researched subject area
1.2. Object and subject of research
1.3. The problem solved in the work and its relevance
1.4. Objective
1 http://lc.kubagro.ru/aidos/Sprab0802.pdf(See from Publication No. 48).
2 https://yandex.ru/search/?text=Automated%2Bsystem-cognitive%2Banalysis%2B(ASC-analysis)&lr=35&clid=2327117-18&win=360
3
Since 1972, first for publications included in the most authoritative international bibliographic databases Scopus and Web of Science (WoS), and then for everyone else, the IMRAD system became the generally accepted international standard for designing research. IMRAD is an English abbreviation that stands for: Introductoin (Introduction), Materials and Methods (Materials and Methods), Results (Results) and Discussion (Discussion):https://disshelp.ru/blos/model-structury-nauchnyh-statei-imrad/).
2. Methods (methods)
2.1. Justification of the requirements for the method of solving the problem
2.2. Literature review of methods for solving the problem, their characteristics and assessment of the degree of compliance with reasonable requirements
2.3. Automated system-cognitive analysis (ASC-analysis) as a method of problem solving
2.4. "Eidos" system - ASC-analysis toolkit
2.5. Purpose and tasks of the work
3. Results (results)
3.1. Task-1. Cognitive structuring of the subject area. Two interpretations of classification and descriptive scales and gradations
3.2. Task-2. Formalization of the subject area
3.3. Task-3. Synthesis of statistical and system-cognitive models. Multiparameter typing and partial knowledge criteria
3.4. Task-4. Model Verification
3.5. Task-5. Choosing the Most Reliable Model
3.6. Task-6. System identification and forecasting
3.6.1. Integral criterion "sum of knowledge"
3.6.2. Integral criterion "semantic resonance of knowledge"
3.6.3. Important Mathematical Properties of Integral Criteria
3.6.4. Solving the problem of identification and forecasting in the Eidos system
3.7. Task-7. Decision Support
3.7.1. Simplified decision-making as an inverse forecasting problem, positive and negative information portraits of classes, SWOT analysis
3.7.2. Developed decision-making algorithm in adaptive intelligent control systems based on ASC analysis and the Eidos system
3.8. Task-8. Examining the object of modeling by examining its model
3.8.1. Inverted SWOT Diagrams of Descriptive Scale Values (Semantic Potentials)
3.8.2. Cluster-constructive analysis of classes
3.8.3. Cluster-constructive analysis of the values of descriptive scales
3.8.4. Knowledge Model of the Eidos System and Nonlocal Neurons
3.8.5. Non-local neural network
3.8.6. 3d integrated cognitive maps
3.8.7. 2d-integral cognitive maps of meaningful class comparison (mediated fuzzy plausible reasoning)
3.8.8. 2d-integrated cognitive maps of meaningful comparison of factor values (mediated fuzzy plausible reasoning)
3.8.9. cognitive functions
3.8.10. Significance of descriptive scales and their gradations
3.8.11. The degree of determinism of classes and classification scales
4. Discussion (discussion)
5. Conclusions (conclusions) References (literature)
However, in this paper, due to limitations on its scope, out of all the diverse possibilities for studying the object of modeling by studying its model supported by the Eidos system, we will consider only SWOT analysis and cognitive functions.
3. RESULTS
3.1. Cognitive structuring of the subject area.
In this work, winter wheat of the Don 95 variety acts as an object of modeling, as factors of sowing time and row spacing (Table 1), and as the results of these factors, grain yield and quality (Table 2):
Table1- Descriptive scales (factors)
KOD OPSC NAME OPSC
1 НАЧАЛО СЕВА
2 ШИРИНА МЕЖДУРЯДИЙ, СМ
Source: c:\Aidos-X\AID DATA\A0000002\System\Opis Sc.dbf
"able2- Classification scales (results of factors)
KOD CLSC NAME CLSC
1 КУСТИСТОСТЬ, %
2 КОЛИЧЕСТВО ВЫПАДОВ ЗА ОСЕННЕ-ЗИМНИЙ ПЕРИОД, %
3 КОЛИЧЕСТВО СТЕБЛЕЙ В ПЕРИОД УБОРКИ, ШТУК/М
4 УРОЖАЙНОСТЬ, Т/ГА
5 СОДЕРЖАНИЕ БЕЛКА, %
6 КЛЕЙКОВИНА, %
Source: c:\Aidos-X\AID DATA\A0000002\System\Class Sc.dbf 3.2. Formalization of the subject area
As a source of initial data in this work, we use Table 4 from [5] (Table 3):
Table3- Initial data for studying the effect of sowing time and row spacing on the yield and grain quality of winter wheat variety Don 95
Таблица 4
Влияние начал сроков посева на урожайность и качество зерна озимой пшеницы сорта Дон 95 (норма высева 3,0 х 106.штук/га ГТК - 1,3)_
Начало сева Ширина междурядий, см Кустистость, % Количество выпадов за осенне-зимний период, % Количество стеблей в период уборки, штук/мг Урожайность, т/га Содержание белка, % Клейковина, %
01 09 2001 г. 15 2,6 15,14 452 2,65 16,8 37,1
02.09.2001 г. 2,8 13,77 492 2,97 16,4 36,5
03.09.2001 г. 2,9 12,18 512 3,02 16,1 34,2
04.09,2001 г. 3,4 10,65 543 3,11 15,9 33,7
05.09 2001 г 4,1 7,31 572 3,27 15,8 32,7
06 09.2001 г. 3,8 9,13 560 3,31 15,9 33,2
07.09.2001 г. 3,6 11,15 542 3,40 16,2 34,1
08,09 2001 г. 3,4 13,14 513 3,37 16,3 34,3
09.09.2001 г. 3,1 17,16 482 3,26 15.4 33,6
10.09.2001 г. 3,0 21,15 476 2,96 15,1 30,2
01.09.2001 г. 22,5 3.1 15,6 542 2,96 14,7 29,4
02.09 2001 г. 3,4 13,14 567 3,02 15?3 30,8
03.09.2001 г. 3.7 11,26 580 3,34 15,8 33,2
04.09.2001 г. 4.1 10,81 617 3,76 16,4 34,3
05.09.2001 г. 4,2 9,21 630 3,87 16,7 35,1
06 09 2001 г. 3,8 8,63 627 3,99 16,4 35,3
07.09.2001 г. 3,6 10,15 613 4,02 16,7 36,1
08.09.2001 г. 3,4 11,13 594 4,13 16,9 37,1
09 09.2001 г. 3,3 14,17 582 3,87 15,4 36,4
10.09.2001 г. 3.2 16,23 517 3.62 15,2 34,2
Using the standard features of MS Excel, we will bring table 3 to the form standard for the Eidos system (table 4):
Table4- Table of initial data in the standard of the Eidos system
A В С D E F G Н I
Количеств Количеств
о выпадов о стеблей Содерж
Начало сева и Ширина за осенне- в период ание
ширина междурядий междуря зимний уборки, Урожайн белка. Кпейков
1 (см) Начало сева дий, см Кустистость. % период. % штук/м ость, т/га % ина. %
2 01.09.2001 T.-W-15 01.09.2001 г. W-15 2,60 15,14 452,00 2,65 16,80 37,10
3 02.09.2001 T.-W-15 02.09.2001 г. W-15 2,80 13,77 492,00 2,97 16,40 36,50
4 03.09.2001 T.-W-15 03.09.2001 г. W-15 2,90 12,18 512,00 3,02 16,10 34,20
5 04.09.2001 r-w-15 04.09.2001 г. w-15 3,40 10,65 543,00 3,11 15,90 33,70
6 05.09.2001 T.-W-15 05.09.2001 г. W-15 4,10 7,31 572,00 3,27 15,80 32,70
7 06.09.2001 T.-W-15 06.09.2001 г. w-15 3,80 9,13 560,00 3,31 15,90 33,20
8 07.09.2001 T.-W-15 07.09.2001 г. w-15 3,60 11,15 542,00 3,40 16,20 34,10
9 08.09.2001 r-w-15 08.09.2001 г. w-15 3,40 13,14 513,00 3,37 16,30 34,30
10 09.09.2001 T.-W-15 09.09.2001 г. w-15 3,10 17,16 482,00 3,26 15,40 33,60
11 10.09.2001 T.-W-15 10.09.2001 г. w-15 3,00 21,15 476,00 2,96 15,10 30,20
12 01.09.2001 г.-w-22,5 01.09.2001 г. w-22,5 3,10 15,60 542,00 2,96 14,70 29,40
13 02.09.2001 r-w-22,5 02.09.2001 г. w-22,5 3,40 13,14 567,00 3,02 15,30 30,80
14 03.09.2001 r-w-22,5 03.09.2001 г. w-22,5 3,70 11,26 580,00 3,34 15,80 33,20
15 04.09.2001 r.-w-22,5 04.09.2001 г. w-22,5 4,10 10,81 617,00 3,76 16,40 34,30
16 05.09.2001 r-w-22,5 05.09.2001 г. w-22,5 4,20 9,21 630,00 3,87 16,70 35,10
17 06.09.2001 r-w-22,5 06.09.2001 г. w-22,5 3,80 8,63 627,00 3,99 16,40 35,30
18 07.09.2001 r-w-22,5 07.09.2001 г. w-22,5 3,60 10,15 613,00 4,02 16,70 36,10
19 08.09.2001 r-w-22,5 08.09.2001 г. w-22,5 3,40 11,13 594,00 4,13 16,90 37,10
20 09.09.2001 r-w-22,5 09.09.2001 г. w-22,5 3,30 14,17 582,00 3,87 15,40 36,40
21 10.09.2001 r-w-22,5 10.09.2001 г. w-22,5 3,20 16,23 517,00 3,62 15,20 34,20
Note:In MS Excel format, table 4 can be downloaded directly from the link:http://aidos.byethost5.com/Source data applications/Applications-000336/Inp data.xls.
The input of initial data from Excel-table 4 into the Eidos system is carried out using API-2.3.2.2 (Figure 1).
Picturel. Screen form of control API-2.3.2.2 of the Eidos system
As a result, classification and descriptive scales and gradations are first formed (tables 5 and 6), and then the initial data (table 4) are encoded with their help, as a result of which a training sample is formed (table 7).
Table5- Classification scales and gradations (numerical scales)
KOD CLS NAME CLS
1 КУСТИСТОСТЬ, %-1/3-{2.6000000, 3.1333333}
2 КУСТИСТОСТЬ, %-2/3-{3.1333333, 3.6666667}
3 КУСТИСТОСТЬ, %-3/3-{3.6666667, 4.2000000}
4 КОЛИЧЕСТВО ВЫПАДОВ ЗА ОСЕННЕ-ЗИМНИИ ПЕРИОД, %-1/3-{7.3100000, 11.9233333}
5 КОЛИЧЕСТВО ВЫПАДОВ ЗА ОСЕННЕ-ЗИМНИЙ ПЕРИОД, %-2/3-{11.9233333, 16.5366667}
6 КОЛИЧЕСТВО ВЫПАДОВ ЗА ОСЕННЕ-ЗИМНИЙ ПЕРИОД, %-3/3-{16.5366667, 21.1500000}
7 КОЛИЧЕСТВО СТЕБЛЕЙ В ПЕРИОД УБОРКИ, ШТУК/М-1/3-{452.0000000, 511.3333333}
8 КОЛИЧЕСТВО СТЕБЛЕЙ В ПЕРИОД УБОРКИ, ШТУК/М-2/3-{511.3333333, 570.6666667}
9 КОЛИЧЕСТВО СТЕБЛЕЙ В ПЕРИОД УБОРКИ, ШТУК/М-3/3-{570.6666667, 630.0000000}
10 УРОЖАЙНОСТЬ, Т/ГА-1/3-{2.6500000, 3.1433333}
11 УРОЖАЙНОСТЬ, Т/ГА-2/3-{3.1433333, 3.6366667}
12 УРОЖАЙНОСТЬ, Т/ГА-3/3-{3.6366667, 4.1300000}
13 СОДЕРЖАНИЕ БЕЛКА, %-1/3-{14.7000000, 15.4333333}
14 СОДЕРЖАНИЕ БЕЛКА, %-2/3-{15.4333333, 16.1666667}
15 СОДЕРЖАНИЕ БЕЛКА, %-3/3-{16.1666667, 16.9000000}
16 КЛЕЙКОВИНА, %-1 /3-{29.4000000, 31.9666667}
17 КЛЕЙКОВИНА, %-2/3-{31.9666667, 34.5333333}
18 КЛЕЙКОВИНА, %-3/3-{34.5333333, 37.1000000}
Source: c:\Aidos-X\AID DATA\A0000001\System\Classes.dbf
KOD ATR NAME ATR
1 НАЧАЛО СЕВА-01.09.2001 г.
2 НАЧАЛО СЕВА-02.09.2001 г.
3 НАЧАЛО СЕВА-03.09.2001 г.
4 НАЧАЛО СЕВА-04.09.2001 г.
5 НАЧАЛО СЕВА-05.09.2001 г.
6 НАЧАЛО СЕВА-06.09.2001 г.
7 НАЧАЛО СЕВА-07.09.2001 г.
8 НАЧАЛО СЕВА-08.09.2001 г.
9 НАЧАЛО СЕВА-09.09.2001 г.
10 НАЧАЛО СЕВА-10.09.2001 г.
11 ШИРИНА МЕЖДУРЯДИЙ, СМ^-15
12 ШИРИНА МЕЖДУРЯДИЙ, СМ-w-22,5
Source: c:\Aidos-X\AID DATA\A0000001\System\Attributes.dbf
Table7- Training set (in full)
NAME OBJ N2 N3 N4 N5 N6 N7 N8 N9
01.09.2001 г.-w-15 1 11 1 5 7 10 15 18
02.09.2001 r-w-15 2 11 1 5 7 10 15 18
03.09.2001 г.-w-15 3 11 1 5 8 10 14 17
04.09.2001 г.-w-15 4 11 2 4 8 10 14 17
05.09.2001 г.-w-15 5 11 3 4 9 11 14 17
06.09.2001 г.-w-15 6 11 3 4 8 11 14 17
07.09.2001 r-w-15 7 11 2 4 8 11 15 17
08.09.2001 r-w-15 8 11 2 5 8 11 15 17
09.09.2001 r-w-15 9 11 1 6 7 11 13 17
10.09.2001 r-w-15 10 11 1 6 7 10 13 16
01.09.2001 r-w-22,5 1 12 1 5 8 10 13 16
02.09.2001 r-w-22,5 2 12 2 5 8 10 13 16
03.09.2001 r-w-22,5 3 12 3 4 9 11 14 17
04.09.2001 r-w-22,5 4 12 3 4 9 12 15 17
05.09.2001 r-w-22,5 5 12 3 4 9 12 15 18
06.09.2001 r-w-22,5 6 12 3 4 9 12 15 18
07.09.2001 r-w-22,5 7 12 2 4 9 12 15 18
08.09.2001 r-w-22,5 8 12 2 4 9 12 15 18
09.09.2001 r-w-22,5 9 12 2 5 9 12 13 18
10.09.2001 r-w-22,5 10 12 2 5 8 11 13 17
Source: c:\Aidos-X\AID DATA\A0000001\System\EventsK0.dbf
Note that the Eidos system usually uses databases with the dbf extension. They open in MS Excel or can be converted to xls, xlsx files using online services.
3.3. Synthesis and verification of statistical and system-cognitive models
In the Eidos system, the synthesis of models is carried out in mode 3.5 (Figure 2):
Picture2. Screen form of the mode of synthesis and verification of models
As a result of the operation of mode 3.5, 3 statistical and 7 system-cognitive models were created, of which only the INF3 model is shown in Figure 3.
From Figure 4, we see that at almost all levels of similarity, the proportion of true positive solutions is greater than false ones, and at similarity levels above 30%, false solutions do not occur at all. For negative decisions at difference levels above 40%, the proportion of true decisions is greater than false ones. Therefore, it is correct to use the obtained INF3 model for solving problems of identification, forecasting, decision making and research of the modeled subject area by studying its model, since this model correctly (reliably, adequately) reflects the modeled subject area.
A В С □ Е F G Н I J К L М N О Р Q R 3 т и V W
1 KOD PR NAME КУСТИСТОСТЬ, %-1/3-{2.6000000, 3.1333333} Г--cd cd cd со cd cd cñ СП СП со СИ со со Й п Sí I— о о 1— о - КУСТИСТОСТЬ, %-3/3-[3.6666667, 4.2000000} КОЛИЧЕСТВО ВЫПАДОВ ЗА ОСЕННЕ-ЗИМНИЙ ПЕРИОД, %-1ß-{7.3100000, 11.9233333 КОЛИЧЕСТВО ВЫПАДОВ ЗА ОСЕННЕ-ЗИМНИЙ ПЕРИОД, %-2/3-{11 S233333, 16.536666 КОЛИЧЕСТВО ВЫПАДОВ ЗА ОСЕННЕ-ЗИМНИЙ ПЕРИОД, %-ЗВ-{16.5366667, 21.150000 КОЛИЧЕСТВО СТЕБЛЕЙ В ПЕРИОД УБОРКИ, ШТУК/М-1 Д-{452.0000000, 511.3333333} КОЛИЧЕСТВО СТЕБЛЕЙ В ПЕРИОД УБОРКИ, ШТУК/М-2/3-{511.3333333, 570.6666667} КОЛИЧЕСТВО СТЕБЛЕЙ В ПЕРИОД УБОРКИ, ШТУК/М-3/3-{570 6666667, 630 0000000} УРОЖАЙНОСТЬ, Т/ГА-1/3-{2 6500000 3 1433333} УРОЖАЙНОСТЬ, Т/ГА-2/3-{3.1433333 3.6366667} УРОЖАЙНОСТЬ, Т/ГА-3/3-{3.6366607 4.1300000} СОДЕРЖАНИЕ БЕЛКА, %-1/3-{14.7000000, 15.4333333} СОДЕРЖАНИЕ БЕЛКА, %-2/3-{15.4333333, 16.1666667} СОДЕРЖАНИЕ БЕЛКА, %-3/3-{16.1666667, 16.9000000} Г-- со cd cd CD cd ™ о" О о о о о ^г сч < X ш о ш КЛЕЙКОВИНА, %-2/3-{31.9666667, 34.5333333} КЛЕЙКОВИНА, %-3/3-{34.5333333, 37.1000000} SUMMA а I_I а: сл DISP
2 1 НАЧАЛО CEBA-01.09.2001 г. 1.4 -0,8 -0.6 1,0 1.2 -0,2 0,6 0,2 -0.8 1,3 -0.7 -0.6 0.4 -0,6 0.1 0,7 -1,0 0,3 0.0 0.0 0.8
3 2 НАЧАЛО CEBA-02.09.2001 г. 0.4 0.2 ■0.6 -1,0 1,2 -0,2 0,5 0,2 -0.8 1,3 -0.7 -0.6 0.4 -0.6 0,1 0,7 -1,0 0,3 0.0 0.0 0.7
4 3 НАЧАЛО СЕВА-0 3.0 9.20 01 г. 0.4 -0.8 0.4 0.0 0.2 -0.2 -0.4 0.2 0.2 0,3 0,3 -0,6 -0.6 1.5 -0.9 ■0.3 1.0 -0.7 0.0 0.0 0,6
5 4 НАЧАЛО СЕВА-04.09.2001 г. -0.6 0.2 0.4 1.0 -0.8 -0.2 -0.4 0.2 0.2 0,3 -0.7 0.4 -0.6 0,5 0.1 ■0,3 1.0 -0.7 0.0 0.0 0,6
6 5 НАЧАЛО СЕВА-0 5.09.20 01 г -0.6 -0.8 1.4 1.0 -0.8 -0.2 -0.4 -0.8 1.2 -0.7 0.3 0.4 -0.6 0.5 0.1 ■0.3 0.0 0.3 0.0 0.0 0.7
7 Б НАЧАЛО СЕВА-06.09.2001 г. -0.6 -0.8 1.4 1.0 -0.8 -0.2 -0.4 0.2 0.2 -0.7 0.3 0.4 -0.6 0.5 0.1 ■0.3 0.0 0.3 0.0 0.0 0.6
S 7 НАЧАЛО СЕВА-07.09.2001 г. -0 6 1,2 -0.6 1.0 -0.8 -0.2 -0.4 0.2 0.2 -0.7 0,3 0,4 -0 6 -0.5 1.1 -0.3 0.0 0.3 0.0 0.0 0.6
9 S НАЧАЛО СЕВА-08.09.2001 г. -0.6 1,2 -0.6 0,0 0.2 -0.2 -0,4 0,2 0,2 -0,7 0,3 0,4 -0.6 -0,5 1,1 -0.3 0,0 0,3 0.0 0.0 0,6
10 9 НАЧАЛО СЕВА-09.09.2001 г. 0.4 0,2 -0.6 -1,0 0,2 0,8 0,5 -0,3 0,2 -0,7 0,3 0,4 1.4 -0,5 -0.9 -0.3 0,0 0,3 0.0 0.0 0.7
11 10 НАЧАЛО СЕВА-10.09.2001 г. 0.4 0,2 -0.6 -1,0 0,2 0,8 0,5 0,2 -0,8 0,3 0,3 -0.6 1.4 -0,5 -0.9 0,7 0,0 -0,7 0.0 0.0 0.7
12 11 ШИРИНА МЕЖДУРЯДИИ. CM-w-15 2.0 -1.0 -1,0 -1,0 0,0 1,0 2,0 1,0 -3.0 1,5 1,5 -3.0 -1.0 1,5 -0,5 ■0.5 2,0 -1,5 0.0 0.0 1.6
13 12 ШИРИНА МЕЖДУРЯДИИ, CM-w-22,5 -2.0 1.0 1.0 1.0 0.0 -1,0 -2,0 -1.0 3.0 -1.5 -1.5 3.0 1.0 -1.5 0.5 0,5 -2.0 1.5 0.0 0.0 1,6
14 Сумма 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
15 Среднее 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
16 Среднеквадратичное отклонение 1.1 0.8 0.9 1.0 0.7 0,5 1.0 0.6 1.4 1.0 0.8 14 0,9 0.9 0.7 0,5 1.0 0.8 0.0 0.0 0.9
Picture3. System-cognitive model "INF3" of the "Eidos" system, Chi-square matrix (according to Karl Pearson)
Picture4. Frequency distributions of the number of true and false, positive and negative
decisions in the INF3 system-cognitive model
3.8. Examining the object of modeling by examining its model
Of all the diverse possibilities for studying the object of modeling by studying its model, supported by the Eidos system [4], in this paper, due to limitations on its volume, we will consider only SWOT analysis and cognitive functions.
3.8.1. SWOT-analysis of the system for determining the future states of the modeling object by the factors acting on it
Figure 5 shows examples of some of the output forms of an automated SWOT analysis. These forms are intuitive to experts in the subject area under study and do not require special comments.
We only note that the SWOT diagrams clearly show the sign and strength of the influence of each factor value on the transition of the simulation object to the state corresponding to the class selected in the upper window. The sign is shown in color, and the strength of influence is shown in the thickness of the line. On the left side of the SWOT-diagram there are values of the factors contributing to the transition of the simulation object to the state corresponding to the class selected in the upper window, and on the right - preventing this transition.
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Picture5. Examples of screen forms of the automated mode SWOT analysis (mode 4.4.8 of the Eidos system)
3.8.2. cognitive functions
It should be noted that the models of the Eidos system are phenomenological models that reflect empirical patterns in the facts of the training sample, i.e. they reflect causal relationships, but do not reflect the mechanism of determination, but only the very fact and nature of determination.
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1. MAM Art o CEBA
Визуализация когнитивных функций системы «Эйд«», ф Е-ЬДщша [Росси»), Д.К.Блндыи (Беларусь), Потаит W 2Q116120S6 Р» ОТ 09.03.2011
2.ШИРИНА МЕЖДУРЯДИЙ, СМ
Визуализация когнитивных функций системы «Эйд«», ф Е.В.луценка (Россия) д.к.Бандыы (Беларусь) патент РФ 2D11G12056 РФ от 09.O3.20ll
Picture6. Examples of cognitive functions in the INF3 SC model
A meaningful explanation of cognitive functions at the theoretical level of knowledge, i.e. in the form of meaningful scientific laws - this is the business of specialists in the subject area to which the subject of modeling belongs.
4. DISCUSSION
The obtained results can be assessed as successfully continuing and developing works [1, 4, 5]. These results were obtained by using the Automated System Cognitive Analysis (linguistic ASC-analysis) and its software tools - the intellectual system "Eidos".
Those who wish have every opportunity to study this work and for further research using ASC analysis and the Eidos system on their computer. To do this, you need to download the system from the developer's website using the link on
the page:http://lc.kubagro.ru/aidos/ Aidos-X.htm, and then in the application manager (mode 1.3) install the intelligent cloud Eidos application No.336.
There are a large number of video lessons (about 300) on various aspects of the application of this technology, which can be found at the links on the page: http://lc.kubagro.ru/aidos/How_to_make_your_own_cloud_Eidos-application.pdf.
Those who wish to get acquainted with this work in Russian can do this at the link: https://www.researchgate.net/publication/364320152.
5. CONCLUSIONS
The paper studies the influence of sowing time and row spacing on the yield and grain quality of winter wheat variety Don 95. The work can be the basis for laboratory work on the use of artificial intelligence systems, in particular, ASC analysis for solving problems in the field of cognitive agronomy. Based on the knowledge of these dependencies, the problems of forecasting, decision making and research of the modeled subject area can be solved by studying its system-cognitive model. The solution of some of these problems is given in this work.
The work can be the basis for laboratory work and scientific research on the use of artificial intelligence systems, in particular, linguistic ASC analysis for solving problems in the field of cognitive agronomy.
LITERATURE
1. Works of Prof. E.V. Lutsenko & C° on topics related to the agro-industrial complex, in particular with cognitive agronomy:http://lc.kubagro.ru/aidos/Work with agricultural.htm
2. Lutsenko, E. V. Automated system-cognitive analysis in the management of active objects: (system information theory and its application in the study of economic, socio-psychological, technological and organizational-technical systems) / E. V. Lutsenko. -Krasnodar: Kuban State Agrarian University named after I.T. Trubilina, 2002. - 605 p. -ISBN 5-94672-020-1. - EDN OCZFHC.
3. Orlov, A. I. System fuzzy interval mathematics / A. I. Orlov, E. V. Lutsenko. -Krasnodar: Kuban State Agrarian University, 2014. - 600 p. - ISBN 978-5-94672-757-0. -EDN RZJXZZ.
4. Lutsenko EV Automated system-cognitive analysis of the dependence of agrophysical indicators of the soil on its processing, fertilizers and the phase of wheat vegetation // July 2022, D0I:10.13140/RG.2.2.32110.69446/2, LicenseCCBY 4.0,https://www.researchgate.net/publication/362211691
5. Rogachev A.F., Saldaev A.M., Rogachev D.A. A method for controlling production processes in the cultivation of winter crops in arid climates /https://patents.google.com/patent/RU2228607C1/ru
