Model predictive control and its application in agriculture: A review

Ding, Yi; Wang, Liang; Li, Yongwei; Li, Daoliang

doi:10.1016/j.compag.2018.06.004

Cited by 184 publications

(81 citation statements)

References 128 publications

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“…Additionally, the tool proposed in this study allows for integrating information obtained from different satellites; improving the reliability and consistency of vegetation state variables; and, consequently, enhancing the estimations of crop water requirements [51][52][53][54]. Furthermore, the inclusion of the tool into a geographic information system (GIS) allows for task automation, which reduces computational time and permits modelling large areas at once, which is an important limitation in other models [11,12].…”

Section: Discussionmentioning

confidence: 99%

“…Most of these models are too complex for non-specialized users, as in the case of many farmers, and even by more qualified users such as technicians from irrigation communities or agricultural cooperatives. This low level of practical applicability is caused by the fact that these models are usually composed of a large number of variables and parameters, requiring, in many cases, long execution times [11,12]. In addition, ET c is usually computed on a punctual location that may not be representative of the entire irrigated area.…”

Section: Introductionmentioning

confidence: 99%

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A Novel ArcGIS Toolbox for Estimating Crop Water Demands by Integrating the Dual Crop Coefficient Approach with Multi-Satellite Imagery

Ramírez-Cuesta

Mirás-Avalos

Rubio-Asensio

et al. 2018

Water

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Advances in information and communication technologies facilitate the application of complex models for optimizing agricultural water management. This paper presents an easy-to-use tool for determining crop water demands using the dual crop coefficient approach and remote sensing imagery. The model was developed using Python as a programming language and integrated into an ArcGIS (geographic information system) toolbox. Inputs consist of images from satellites Landsat 7 and 8, and Sentinel 2A, along with data for defining crop, weather, soil type, and irrigation system. The tool produces a spatial distribution map of the crop evapotranspiration estimates, assuming no water stress, which allows quantifying the water demand and its variability within an agricultural field with a spatial resolution of either 10 m (for Sentinel) or 30 m (for Landsat). The model was validated by comparing the estimated basal crop coefficients (Kcb) of lettuce and peach during an irrigation season with those tabulated as a reference for these crops. Good agreements between Kcb derived from both methods were obtained with a root mean squared error ranging from 0.01 to 0.02 for both crops, although certain underestimations were observed resulting from the uneven crop development in the field (percent bias of −4.74% and −1.80% for lettuce and peach, respectively). The developed tool can be incorporated into commercial decision support systems for irrigation scheduling and other applications that account for the water balance in agro-ecosystems. This tool is freely available upon request to the corresponding author.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel ArcGIS Toolbox for Estimating Crop Water Demands by Integrating the Dual Crop Coefficient Approach with Multi-Satellite Imagery

Ramírez-Cuesta

Mirás-Avalos

Rubio-Asensio

et al. 2018

Water

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“…Ying Ding et al [7] used Model Predictive Control (MPC) methods. According to him Model Predictive Control method is more suitable than Process Control Model which is originated in industries.…”

Section: Literature Surveymentioning

confidence: 99%

Crop Yield Prediction using XG Boost Algorithm

Ravi*,

Baranidharan

2020

IJRTE

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The main objective of this research is to predict crop yields based on cultivation area, Rainfall and maximum and minimum temperature data. It will help our Indian farmers to predict crop yielding according to the environment conditions. Nowadays, Machine learning based crop yield prediction is very popular than the traditional models because of its accuracy. In this paper, linear regression, Support Vector Regression, Decision Tree and Random forest is compared with XG Boost algorithm. The above mentioned algorithms are compared based on R2 , Minimum Square Error and Minimum Absolute Error. The dataset is prepared from the data.gov.in site for the year from 2000 to 2014. The data for 4 south Indian states Andhra Pradesh, Karnataka, Tamil Nadu and Kerala data alone is taken since all these states has same climatic conditions. The proposed model in this paper based on XG Boost is showing much better results than other models. In XG Boost R2 is 0.9391 which is the best when compared with other models.

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“…№ 719 не стимулируется, к сожале нию, выпуск сельхозтехники, которая в настоящее время отсутствует, что обусловливает целесообраз Управление в промышленности С ) ность его доработки и принятия в новой редакции . Законодательного механизма по стимулированию запусков производств сельхозмашиностроительной продукции, связанной с импортозамещением, с вне дрением новейших технологий в сельское хозяйство (спутниковой системы рулевого управления, интел лектуальной системы управления оборудованием [21], системы контроля за уплотнением почв и др .…”

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The Russian industry: the problems and the further development (ending)

Rumyantseva

2018

RJIE

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This article describes the various approaches to the analysis of the current situation and the justification of the further development prospects of the Russian industry represented at the round table on June 21,2018 inthe State Duma of Federal Assembly of the Russian Federation on a subject: «Legislative providing industrial policy of the Russian Federation: technologies, innovations, investments». Attention had also been drawn to issues requiring the globalization, the industrial development inequality of the countries of the world and the Russian regions, the dependences of economic growth on the state of the knowledge economy, the industrial and monetary and credit policy interrelations, the changes in the system of the state regulation of the Russian industry, the business moods, the technological updating results of the industrial enterprises, the features of the investment projects financing in Russia and in industrialized countries, the prospects of the introduction in Russia of the digital technologies of the industrial production management. Among offers of the situation significant improvement there are: the providing for the economic relations participants of the equal competitive opportunities; the completion of the Federal law «About Industrial Policy in the Russian Federation», the involvement in the economy of free money, the updating on the basis of the problems settlement with the financing of the enterprises of the fixed assets, the selective development of the import substitution, the distribution of the positive experience of the successful industrial companies. The author points to a need of the involvement in the discussion and the other important questions concerning the comprehensive analysis of the reasons of the serious lag of the Russian enterprises from the enterprises of the industrialized countries generating the negative consequences; the labor productivity at the industrial enterprises of the different countries of the world and factors of its growth; the influences of the developed bureaucracy, the administrative barriers, the corruption on a situation in the various industries, the applications of the concept of the economical production, the energy productivity and a resource conservation, etc.

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Model predictive control and its application in agriculture: A review

Cited by 184 publications

References 128 publications

A Novel ArcGIS Toolbox for Estimating Crop Water Demands by Integrating the Dual Crop Coefficient Approach with Multi-Satellite Imagery

A Novel ArcGIS Toolbox for Estimating Crop Water Demands by Integrating the Dual Crop Coefficient Approach with Multi-Satellite Imagery

Crop Yield Prediction using XG Boost Algorithm

The Russian industry: the problems and the further development (ending)

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