Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images

Meyer, George E.; Neto, J. Camargo; Jones, David D.; Hindman, Timothy W.

doi:10.1016/j.compag.2003.08.002

Cited by 133 publications

(57 citation statements)

References 20 publications

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“…The k-means algorithm is used for soil classifications using GPS-based technologies [27], classification of plant, soil and residue regions of interest by color images [28]. Decision tree approach technique is also used in the prediction of soil fertility [29].…”

Section: Classification Of Soils and Prediction Of Soil Fertilitymentioning

confidence: 99%

Role of Data Mining Techniques in Agriculture Improvement

Sindhu¹,

Sindhu²

2017

International Journal of Advanced Research in Computer and Comm

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Data mining is the process of discovering and extracting of interesting patterns and knowledge from large amounts of data. The field of agriculture has to deal with large amounts of data and processing and retrieval of significant data from this abundance of agricultural information is necessary to help the farmers. Therefore, appropriate methods and techniques are required for managing and organizing this data to increase the efficiency and agricultural productivity. The application of data mining methods and techniques to discover new insights or knowledge is a relatively a novel approach in agriculture. Data mining can help to process and convert this raw data into useful information for improving agriculture. In this paper, various data mining techniques used for processing of agricultural information/data such as k-means clustering, k-nearest neighbour, artificial neural networks, support vector machine, naive Bayesian classifier and fuzzy c-means are described. With the advancement of novel and appropriate data mining techniques, different types of agricultural problems will be addressed to improve crop productivity.

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Section: Classification Of Soils and Prediction Of Soil Fertilitymentioning

confidence: 99%

Role of Data Mining Techniques in Agriculture Improvement

Sindhu¹,

Sindhu²

2017

International Journal of Advanced Research in Computer and Comm

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“…[17] , Classification of plant, soil, and residue regions of interest by color images [18], Grading apples before marketing [19], Monitoring water quality changes [20] , Detecting weeds in precision agriculture [21], The prediction of wine fermentation problems can be performed by using a k-means approach. Knowing in advance that the wine fermentation process could get stuck or be slow can help the enologist to correct it and ensure a good fermentation process.…”

Section: The Application Of K-means Algorithm In the Field Of Agriculmentioning

confidence: 99%

A Brief survey of Data Mining Techniques Applied to Agricultural Data

Patel¹,

Patel²

2014

IJCA

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As with many other sectors the amount of agriculture data based are increasing on a daily basis. However, the application of data mining methods and techniques to discover new insights or knowledge is a relatively a novel research area. In this paper we provide a brief review of a variety of Data Mining techniques that have been applied to model data from or about the agricultural domain. The Data Mining techniques applied on Agricultural data include k-means, bi clustering, k nearest neighbor, Neural Networks (NN) Support Vector Machine (SVM), Naive Bayes Classifier and Fuzzy cmeans. As can be seen the appropriateness of data mining techniques is to a certain extent determined by the different types of agricultural data or the problems being addressed. This survey summarize the application of data mining techniques and predictive modeling application in the agriculture field.

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“…First, a coarse vegetation map is generated from lower resolution images using the excess green index (ExG) which is highly effective in masking out the green objects from the bare soil background (Meyer et al (2004)). A close relation exists between the mean ExG for a certain area and its vegetation density, and comparisons can be made within the same image.…”

Section: Aerial Image Processingmentioning

confidence: 99%