Application of Datamining Tool to Crop Management System

Raorane, A.A.; Kulkarni, R. V.

doi:10.18551/rjoas.2015-01.01

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…Since the 1980s, linear programming has been used in crop allocation problems in order to increase their economic yield [1,2,9,18]. Various programs/softwares like MATLAB and Lindo have been used for crop management [27]. Many examples of crop allocation with linear programming methodology have been reported in the literature [11].…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Crop management and distribution by linear programming method using the Excel software for planning the agricultural production

Fytilis,

Karaouzas

2023

Preprint

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The need for appropriate crop management/distribution to increase the economic yield of crops is a major challenge for farmers. Linear programming is a mathematical model that solves such problems with great success for the purpose of crop maximization or minimization. In this study the method of linear programming is used in Sperchios river basin to increase crop efficiency and consequently to increase the economic returns of the crops by maximizing crop mass. Several constraints are taken into account, which determines the final result (e.g. fertilization requirements, irrigation, labor, land use). The main objective is to evaluate the distribution of crops in the study area and the use of the historical constraint, a constraint that is being introduced for the first time. Then a comparison is made of the results obtained from the use and non-use of the historical constraint (in combination with the other constraints). The method used is that of linear programming. In particular, by using the software excel uses the solver tool that solves linear problems. Currently, in the Sperchios river basin 26 crops are being cultivated. If the historical constraint is included, the number of crops remain 26. Otherwise, the optimal solution suggests the exploitation of 6 crops. Also, depending on the constraints , the needs for irrigation, fertilization, labor and land use differ. The excel software suggested the optimal solution to obtain the maximum crop yield considering certain constraints. Thus, we conclude that linear programming can be used as a decision support making – a decision support system (DSS) in crop management.

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Crop management and distribution by linear programming method using the Excel software for planning the agricultural production

Fytilis,

Karaouzas

2023

Preprint

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“…Some of these techniques, such as the kmeans, the k nearest neighbor, artificial neural networks and support vector machines, are discussed and an application in agriculture for each of these techniques is presented. In addition, the references included in brackets confirmed the ability and necessity of using data mining technique in agriculture (Raorane & Kulkarni, 2013;Kalpana et al, 2014;Geetha, 2015;Khedr et al, 2015;Raorane & Kulkarni, 2015;Almaliki et al, 2016;Almaliki, 2017;Oliveira et al, 2017;Thomas, 2017). Therefore, the energy used in wheat production was predicted and modelled using data mining techniques (Artificial Neural Network).…”

Section: Introductionmentioning

confidence: 87%

Energy Prediction of Wheat Production Using Data Mining Technique in Iran

Monjezi

2021

Basrah J. Agric. Sci.

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Wheat is considered as one of the most important products in Iran. Concerning high cultivation area of wheat in Khuzestan, an instrument is required to process stored data in order to give information resulted from such processing to managers of agricultural sectors. Data mining technique is able to give essential information and models to producers of wheat for modelling energy consumption. One of the most practical algorithms is an artificial neural network. The main aim of this research is to predict output energy of wheat farms using a multilayer perceptron neural network. This is an analytic research and its database consists of 1240 records. Data required for the research was obtained from wheat farm during 2014-2018. Data analysis was done via IBM SPSS modeller 14.2 and standard CRISP. Concerning the model used in the research, it was found that variables of chemical fertilizers, machinery & diesel fuel with coefficients of 0.2987, 0.2064 and 0.1527 respectively had the highest effect on output variable (productive energy). Amount of prediction precision in neural network algorithm, meaning ratio of correctly predicted records to total records was 93.08%. Also, linear correlation between actual values and predicted values were 0.92 and 0.88 respectively, for training data and testing data suggesting strong correlation. The results obtained can be effective for wheat farmers in direction of evaluation and optimization of energy consumption in process of wheat production and reduction of consumption of energy inputs.

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“…Different data mining techniques such as K-Means, K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN) and Support Vector Machines (SVM) for very recent applications of data mining techniques in agriculture field. In addition, the references included in parentheses confirmed the ability and necessity of using data mining technique in agriculture (Raorane & Kulkarni, 2013;Jeysenthil et al, 2014;Kalpana et al, 2014;Geetha, 2015;Raorane & Kulkarni, 2015).…”

Section: Introductionmentioning

confidence: 88%

An assessment of Energy Consumption for Canola Production System in Iran (A Case Study: Amirkabir Agro-Industry)

Monjezi

Hosseinzadeh

2021

Basrah J. Agric. Sci.

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Canola production increased significantly in Iran due to its high yield in recent years. Khuzestan province is the main centre of canola production in Iran. This paper presents a data mining study of samples of canola obtained from farms in Amirkabir Agro-Industry of Khuzestan province. Data were collected from 48 farms. The farms were chosen by random sampling method. The purpose of this study is to determine energy consumption of input and output used in canola production. And Output energy of canola farms is predicted using data mining and multi-layer perceptron neural network. This is an analytic research and its database consists of 432 records. Data required for this research was obtained during growing seasons in 2017-2018. Data analysis was done by IBM SPSS modeler 14.2. The results showed that the amount of energy consumed in canola production was 28927.43 MJ ha-1. About 40% of this was generated by fertilizers and 37% from electricity and diesel fuel. Concerning the model used in the research, it was found that variables of chemical fertilizer, fuel, electricity energy and irrigation, respectively had the highest effect on output variable (productive energy). Amount of prediction precision in neural network algorithm meaning ratio of correctly predicted records to total records was 86.5%. Also, linear correlation between actual values and predicted values was 0.84 and 0.88 respectively for training data and testing data suggesting strong correlation. Results obtained in this research can be effective for canola farmers in Amirkabir Agro-Industry in direction of evaluation and optimization of energy consumption in process of canola production and reduction of consumption of energy inputs.

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Application of Datamining Tool to Crop Management System

Cited by 8 publications

References 7 publications

WITHDRAWN: Crop management and distribution by linear programming method using the Excel software for planning the agricultural production

WITHDRAWN: Crop management and distribution by linear programming method using the Excel software for planning the agricultural production

Energy Prediction of Wheat Production Using Data Mining Technique in Iran

An assessment of Energy Consumption for Canola Production System in Iran (A Case Study: Amirkabir Agro-Industry)

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