A Survey and Analysis of Various Agricultural Crops Classification Techniques

Chouhan, Surabhi; Singh, Divakar; Singh, Anju

doi:10.5120/ijca2016908575

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…The temperature, rainfall, soil parameters and past year production are used as selected attributes. The classification, clustering and association rule DMTs are analyzed by Chouhan (10) for analyzing the crops production.…”

Section: Literature Reviewmentioning

confidence: 99%

Rice yield prediction and optimization using association rules and neural network methods to enhance agribusiness

Supro¹

2020

IJST

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Objectives: This study aims to implement data analytics and machine learning approaches to rice data and to establish association rules on fixed attributes and their correlations for yield prediction of crops. Methods: The data of rice crop is collected from district Larkana as per defined parameters: area, production, yield, temperature, rainfall, humidity and wind speed. The pre-processing operations are applied on prepared dataset to execute data analytics and machine learning algorithms. The processed data are then input into an Apriori algorithm for generating association rules. Neural network model is used to perform optimization on resulted association rules. Findings: Minimum support and confidence values equal to 3 and 80 respectively were set using Apriori algorithm on prepared rice dataset and obtained 88 association rules. Among them, results of 28 associated rules predicted`High Yield Production'. Neural network model is experimented to optimize the predicted yield of district Larkana through which overall network performance of 97.8% is calculated. Previously, rice yield data of Larkana were not statistically and digitally predicted and investigated. Application : Group of effective and well-built association rules of yield prediction are core outcome of this study which will be helpful for researchers, farmers and government officials to improve the productivity of rice crop.

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Section: Literature Reviewmentioning

confidence: 99%

Rice yield prediction and optimization using association rules and neural network methods to enhance agribusiness

Supro¹

2020

IJST

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“…Random decision forests make up for the choice trees propensity for over fitting to their training set. Surabhi Chouhan, Bhopal Divakar Singh, Bhopal Anju Singh [8], use a different method for mining that is used for imparting and obtaining significant data so that the information can be ordered in an effective and rapid manner. These mining algorithms can be connected in different fields including arrangement of agricultural crops production.…”

Section: Literature Surveymentioning

confidence: 99%

Data Analytics on Agrometeorological Parameters for Building A Utility System for Farmer Community

Sowmya¹,

Mundada²,

Hegde³

et al. 2019

IJRTE

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The day-to-day evolution of the world is throwing many challenges, thereby demanding the humans to be on par with the modern tools and technologies that are on offer. These technologies are contributing to the overall growth across all the domains. One such domain that is highly affected by the modern tools and techniques is Agriculture. Gone are the days, when only a handful methodologies and tools were utilized to understand about agriculture. Although the farmers are sceptical to the modernization, the end result has been encouraging at many a times. With the use of analytics, one can get to know more about the type of soil, crops and fertilizers, amount of water to be utilized depending on the climatic conditions and thereby have an effective yield. In this work, the elementary task of portraying the effect of climatic conditions on the production of different varieties of crops is carried out. Also, the application of Multivariate Linear Regression and Artificial Neural Network (ANN) techniques provides a significance outcome in the prediction of yield with the focus on Ragi and Rice. The work provides an accuracy of 65% with the 3-fold cross validation technique and 68% accuracy with ANN model.

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“…According to many authors (Patel and Patel, 2014;Mistry et al, 2016;Kodeeshwari and Ilakkiya, 2017) DM techniques were used mainly for classification and clustering, but can perform association, regression, and predictive analysis. Outputs of crop yield (Manjula and Narsimha, 2016) performed by DM engines have been used by farmers to improve crop performance (Chouhan, 2016;Jiménez et al, 2016). The book written by Mucherino et al (2009) is a broad overview of recent DM techniques and applications in agriculture and is the first completely dedicated to emerging research fields.…”

Section: Data Miningmentioning

confidence: 99%

Assessment of data fusion oriented on data mining approaches to enhance precision agriculture practices aimed at increase of Durum Wheat (Triticum turgidum L. var. durum) yield

D’Accolti¹,

Maggio²,

Massaro³

et al. 2018

J Food Nut Agri

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In 2050, world population will reach a total of 9 billion inhabitants and their food demand have to be satisfied. Durum wheat (Triticum turgidum L. var. durum) is one of the most important food crop and its consumption is increasing worldwide. Productivity growth in agriculture and profitable returns are strongly influenced by investment in research and development, where Precision Agriculture (PA) represents an innovative way to manage farms by introducing the Information and Communication Technology (ICT) into the production process. It is known that farms activities produce large amounts of data. Today ICT allows, with electronic and software systems, to collect and transfer automatically these data, thus increasing yields and profits. In this direction significant data are processed from agricultural production, and retrieved to extract useful information important to increase the knowledge base. Data from multiple data sources can be processed by a Data Fusion (DF) approach able to combine multiple data sources into an unique database system. Raw data are transformed into useful information, thus DF improves pattern recognition, analysis of growth factors, and relationship between crops and environments. Data Fusion is synonym of Data Integration, Sensor Fusion, and Image Fusion. By means of Data Mining (DM) it is possible to extract useful information from data of the production processes thus providing new outputs concerning product quality and product “health status”. The following literature take into account the DF and DM techniques applied to Precision Agriculture (PA) and to cultivation inputs (water, nitrogen, etc.) management. We report also last advances of DF and DM in modern agriculture and in precision durum wheat production.

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A Survey and Analysis of Various Agricultural Crops Classification Techniques

Cited by 5 publications

References 17 publications

Rice yield prediction and optimization using association rules and neural network methods to enhance agribusiness

Rice yield prediction and optimization using association rules and neural network methods to enhance agribusiness

Data Analytics on Agrometeorological Parameters for Building A Utility System for Farmer Community

Assessment of data fusion oriented on data mining approaches to enhance precision agriculture practices aimed at increase of Durum Wheat (Triticum turgidum L. var. durum) yield

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