In agriculture, the volume and quality of the use of modern technologies, including systems for collecting, storing and processing data, is noticeably growing. This increases both the amount of data and the need for high-quality processing and reliable conclusions that you can rely on when making decisions. The lack of information for decision making leads to the fact that in the process of cultivating crops, up to 40 % of the crop is lost. Further automation of processes at all stages of the production cycle represents a higher level of digital integration, which affects the most complex organizational changes in the agricultural business, but their implementation can dramatically affect profit and competitiveness of products. The modernization of the agricultural sector is based on the transition to «intelligent agriculture». The greatest interest to science and practice is the intellectualization of agricultural technology management, where the basis is expert systems in which management decisions are made through knowledge bases (KB), formed through analytical control systems located in data centers. In this paper, we consider expert systems for the state control of spring wheat. To this type of management we attribute the task of preliminary formation of the sequence of technological operations on one growing season. In the cloud information system, the generated knowledge bases are transferred from the data center to local management systems at the request of consumers.
The transition to "intellectual" agriculture is the main vector of modernization of the agricultural sector of the economy. It is based on integrated automation and robotization of production, the use of automated decision-making systems. This is inevitably accompanied by a significant increase in data flow from sensors, monitoring systems, meteorological stations, drones, satellites and other external systems. Farm management has the opportunity to use various online applications for accurate recommendations and making various kinds of management decisions. In this regard, the most effective use of cloud information technologies, allowing implementing the most complex information and technical level of automation systems for management of agricultural technologies. The purpose of this work is to test the approach to creating expert management decision support systems (DSS) through the knowledge base (KB), formed in the cloud information system. For this, we consider an example of constructing a DSS for choosing the optimal date for preparing forage from perennial grasses. A complete theoretical and algorithmic database of the analytical DSS implemented in the data processing center of the cloud information system is given. On its basis, a KB is formed for a variety of different decision-making conditions. This knowledge base is transmitted to the local DSS. To make decisions about the optimal dates for the preparation of the local DSS, two variants of algorithms are used. The first option is based on management models, and the second uses the pattern recognition method. The approbation of the algorithms was carried out according to the BZ from 50 cases. According to the results of testing, the method of pattern recognition proved to be more accurate, which provides a more flexible adjustment of the situation on the local DSS to a similar situation in the KB. The considered technique can be extended to other crops.
Управление параметрами химического состояния почв за счёт внесения минеральных удобрений является важнейшим технологическим компонентом в современных системах точного земледелия. Эта проблема положительно не решена до настоящего времени. Здесь существенным сдерживающим фактором является отсутствие методов и средств оценивания параметров химического состояния на больших площадях сельскохозяйственных полей. Целью настоящего исследования является создание научно-методических основ для решения задачи оценивания параметров химического состояния почв на основе данных дистанционного зондирования Земли (ДЗЗ). В связи с недоступностью для ДЗЗ химического состояния почвы оценивание его параметров осуществляется в два этапа. На первом этапе оцениваются массовые и химические параметры состояния посева сельскохозяйственной культуры, а на втором этапе по этим оценкам строятся оценки параметров химического состояния почвы. Основу предлагаемой методики составляют математические модели: параметров отражения массовых и химических характеристик состояния посева (ДЗЗ), зависимости величины урожая от параметров химического состояния почв для отдельных культур. При этом для построения оценок параметров по данным ДЗЗ использовался алгоритм оптимальной фильтрации. Полученные оценки можно использовать для управления параметрами химического состояния почвы в системах точного земледелия.Ключевые слова: точное земледелие, управление агротехнологиями, дистанционное зондирование Земли, математические модели, алгоритмы оценивания параметров Одобрена к печати: ВведениеСреди компонентов «умного сельского хозяйства» центральное место занимают технологии точного земледелия (ТЗ), включающее в себя комплекс современных информационных технологий и роботизированных технологических машин. По своей сути такой комплекс направлен на решение задач управления агротехнологиями (Михайленко, 2005; Точное…, 2009). При этом основной технологической операцией, посредством которой формируется урожай культур, является внесение минеральных удобрений. В то же время оптимизация доз внесе-
Improving the efficiency of agriculture is impossible without the use of innovative solutions, among which it is necessary to highlight information technology and the use of the capabilities of the digital economy. To the greatest extent, these opportunities are manifested in such a new technological direction as precision farming which has received a new impetus for development in connection with the expanding use of remote sensing of the Earth. However, it should be noted that the use of remote sensing means for more than twenty years has so far had little effect on the effectiveness of precision farming technologies. This is due to the insufficient development of the scientific and methodological base for the use of remote sensing in the monitoring and control of agricultural technologies. Until now, the enormous opportunities of modern remote sensing tools are reduced to the construction and study of various types of vegetation indices, according to which it is impossible to make control decisions in precision farming systems. This problem is especially acute in control systems for the processes of applying mineral fertilizers, which are the main technological operations in crop production. Here, scientific and technological progress concerned only the mechanization of soil sampling at fixed points in the field and subsequent laboratory analysis of the samples. Each sampling point is considered representative for a field plot with an area of 5 to 15 ha. Such a technology for obtaining information is not only very laborious, but also carries big errors in the subsequent justification of fertilizer doses. The aim of this work is the further development of scientific and methodological foundations and software and hardware assessment tools according to remote sensing data of the content of the main nutrients in plants and soil.
The aim of the work is to develop a theoretical basis for solving the problem of managing the state of agrocenoses, which contain crops of the main crop and weeds. The solution to this problem is aimed at eliminating the limitations of the existing paradigm of separate management of the state of crops and weeds. The application of mineral fertilizers simultaneously stimulates the growth and development of plants, crops and weeds, and herbicide treatments simultaneously suppress the growth of both crop plants and weeds. As a result, this leads to significant crop losses and overconsumption of fertilizers and herbicides. The proposed theory and methodology is based on taking into account the relationship between the state of crops and weeds, and their overall effect on the content of nutrients in the soil. For this, a system of mathematical models has been proposed, in which these relationships and yield losses are taken into account when the parameters of the chemical state of the soil deviate from the optimal values for crop rotation and from the effect of herbicide treatments on crop sowing. The result of solving the problem is the optimal strategies for the introduction of mineral fertilizers, ameliorants and herbicides by years of crop rotation. These strategies ensure that yield losses are minimized for all crops, rotation and agrochemical consumption. They are the main tool for planning agricultural technologies and standardizing technological operations carried out for individual years of crop rotation. The results obtained are new, since such tools are currently lacking.
An analysis of modern work in the field of the use of Earth remote sensing data in agriculture has shown that, at present, they have not yet created the information and methodological basis for estimating the parameters of the chemical state of plants and soil. Currently, sample surveys of fields with mechanical sampling at individual points at the end of the growing season are widely used. Such a survey is very time-consuming and expensive, and most importantly, does not have sufficient accuracy required to solve management problems and decision-making in precision farming. The aim of this work is to bridge this gap, which will make it possible to more fully use the modern capabilities of remote sensing of the Earth to solve many problems of agricultural technology management. Since the parameters of the chemical state of the soil are inaccessible to remote sensing, a two-stage iterative scheme for their assessment was developed. It consists in the fact that on the basis of mathematical models and current data of remote sensing of the Earth, the parameters of the crop biomass are estimated, as well as the parameters of the chemical state of the crop biomass, and estimates of the parameters of the chemical state of the soil are constructed from the estimates and the mathematical model. Moreover, the closure of this integration procedure is carried out by comparing the actual reflection parameters relative to the chemical state of the crop and their estimates obtained at the first stage of the assessment procedure.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.