Support to decision-making

Naud, Olivier; Taylor, James A.; Colizzi, Lucio; Giroudeau, Rodolphe; Guillaume, Serge; Bourreau, Eric; Crestey, Thomas; Tisseyre, Bruno

doi:10.1016/b978-0-12-818373-1.00004-4

Cited by 12 publications

(9 citation statements)

References 33 publications

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“…In the papers [13,20,[26][27][28], farm management information systems are considered. Such systems are evolved from simple record-keeping software to complex cloud-based systems that can manipulate large amounts of data and provide decision support capabilities.…”

Section: Farm Management Information Systems (Fmis)mentioning

confidence: 99%

Digital Technologies, Internet of Things and Cloud Computations Used in Agriculture: Surveys and Literature in Russian

Matsveichuk¹,

Sotskov²

2023

Preprint

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Development of agriculture in Russia and Belarus is based on the practical implementation of "smart" systems in agriculture based on the use of modern wireless, intelligent technologies and Internet of Things. This review presents research articles (mainly, in Russian) published in the period of 2013 – 2022 on the use of cloud technologies and Internet of Things for the development of agriculture in Russia and Belarus. An analysis of the use of cloud technologies and Internet of Things in the modern world is given on the basis of research articles and reviews published in English in the period of 2017 – 2022. The main directions of digitalization of modern agriculture are listed. The uses of cloud technologies and Internet of Things in agriculture are described along with promising directions for further research and applications.

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Section: Farm Management Information Systems (Fmis)mentioning

confidence: 99%

Digital Technologies, Internet of Things and Cloud Computations Used in Agriculture: Surveys and Literature in Russian

Matsveichuk¹,

Sotskov²

2023

Preprint

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“…(3) Target spikes per m 2 Based on the ideal yield structure, an optimal crop stand density (in spikes per m 2 (Sm 2 )), can be derived for each site, depending on the variety and start of shooting (daylength response). This assignment was obtained from the breeder or corresponding trial observations.…”

Section: Conflicts Of Interestmentioning

confidence: 99%

“…These interactions and biological processes create the need for decision making throughout the growing season, but most critically during the vegetation period. Decisions are made on the basis of available information and practical knowledge but, increasingly, also through using decision support systems (DSS) [1][2][3]. To realize DSS, computer science methods and techniques are used in goal-directed action in environments that are not completely controllable, dynamic, and/or imprecisely known in advance.…”

Section: Introduction 1scientific Challengesmentioning

confidence: 99%

A Digital Advisor Twin for Crop Nitrogen Management

et al. 2022

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Farmers and consultants face an unmanageable amount of diverse knowledge and information for crop management decisions. To determine optimal actions, decision makers require knowledge-based support. In this way, decisions can be improved and heuristics can be replaced over time. The study presents a digital knowledge base with an integrated decision support system (DSS), using the example of nutrient supply, specifically nitrogen (N), fertilization. Therefore, the requirements of farmers and crop consultants for DSS to inform fertilization decisions for winter wheat (Triticum aestivum L.) were elaborated using surveys, expert interviews, and a prototype test. Semantic knowledge was enriched by expert knowledge and combined in a web application, the Crop Portal. To map regional and personal decision making patterns and experiences, the tacit knowledge on the complex advisory problem of N fertilization is made digitally usable. For this purpose, 16 fuzzy variables were specified and formalized. Individual decision trees and their interactions with an integrative knowledge base were used to multiply the consulting reach of experts. Using three consultants and nine model farms from different soil–climate areas in Germany, the Crop Portal was tested under practical conditions and the perceived pragmatic and hedonic quality of the system was evaluated using a standardized questionnaire. The field test showed that the variation in fertilizer recommendations from the ‘digital advisor twin’ ranged from 5 kg N ha−1 to 16 kg N ha−1 when compared with the decisions of the experts in the field. The study presents the participatory development and evaluation of a rule-based DSS prototype in agricultural practice.

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“…Precision pest management is based on making decisions based on precise subfieldscale information about pest infestations instead of pest density averages at the field scale (Naud et al, 2020). The most significant advantages of precision pest management for crop sustainability are the reduction of pesticide use and the increased use of non-chemical tools targeted to populations in restricted parts of the field.…”

Section: Precision Pest Managementmentioning

confidence: 99%

“…The integration of multilayer information can promote more accurate control decisions. A general multilayer architecture includes the soil and crop layer (the physical component of the agricultural land and the crop present on it) interacting with the rest of the architecture, the actuators layer (with the distribution layer representing the tools to deliver the solutions to specific areas within the field), the sensors layer (representing the tools available to collect data from the soil and plants), a networking layer (connecting all the layers), and, finally, the application layer (with cloud services to process all data and transform them into useful information) (Naud et al, 2020). The integration of data is especially important in the context of pest management because of the dynamics of pest populations in space and time (Venette et al, 2010), as well as the high dynamism of their environmental drivers, such as temperature, radiation, and humidity (Méndez-Vázquez, et al, 2019).…”

Section: Precision Pest Managementmentioning

confidence: 99%

Monitoring soybean pests using remote sensing

Filho¹

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Dedico esse trabalho a todos que me auxiliaram nessa trajetória, em especial à minha família, amigos e todos professores que colaboraram com minha formação. AGRADECIMENTOSAgradeço primeiramente a Deus, por me guiar nessa longa jornada, desde os primeiros passos, até essa conquista.Ao meu orientador Prof. Pedro Yamamoto, por sempre oferecer as melhores condições de desenvolvimento pessoal e profissional, e pela disponibilidade em todos os momentos.Agradeço à minha família, meus pais, minha irmã, meu cunhado e meus sobrinhos, por me apoiar durante todos esses anos. Em especial, agradeço à minha esposa Ana Clara, que sempre esteve ao meu lado nessa caminhada de pós-graduação, me ajudando nos ensaios, incentivando diariamente, compartilhando sucessos e fracassos, enfim, tornando possível a conclusão de mais essa etapa. Ainda, agradeço pelo presente que recebemos no final desse ano, na reta final do doutorado, entregue por Deus, com nome de Maria, nossa filha.

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Support to decision-making

Cited by 12 publications

References 33 publications

Digital Technologies, Internet of Things and Cloud Computations Used in Agriculture: Surveys and Literature in Russian

Digital Technologies, Internet of Things and Cloud Computations Used in Agriculture: Surveys and Literature in Russian

A Digital Advisor Twin for Crop Nitrogen Management

Monitoring soybean pests using remote sensing

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