Prediction of leaf number by linear regression models in cassava

Fakir, M. S. A.; Mostafa, Mohamed M.; Karim, Masudul; ., A.K.M. Azad-ud-doula Prodhan

doi:10.3329/jbau.v9i1.8743

Cited by 3 publications

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“…This result can be related to the amount of carbohydrate which made by chlorophyll. Fakir et al (2011) also used regression to estimate leaf number currently held on the plant and degree of leaf sheding occurred was carried out in two Cassava (Manihot esculenta) morphotypes (Philippine and Nagra) at Mymensingh (24°75´N 90°50´E). Four linear regression Models were developed for estimating leaf number from length of main stem and primary branch.…”

Section: Discussionmentioning

confidence: 99%

The analysis of agricultural data with regression data mining technique

Fetanat¹,

Mortazavifarr²,

Zarshenas³

2015

CeN

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Data mining is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. This paper discusses data mining technique such as regression and clustering which is a process model for analyzing data and describes the support that SPSS provides for this model. SPSS-based analysis and application construction process is illustrated through a case study in the agricultural domain-ornamental plants. Cluster analysis or clustering was used is the task of assigning a set of objects into groups so that the objects in the same cluster are more similar to each other than to those in other clusters. In this survey clustering technique divides growth factors into several independent categories. Also, regression technique which was used includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. In this research, analyzed data with regression technique showed the effect of chlorophyll content on the number of flowers.

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Section: Discussionmentioning

confidence: 99%

The analysis of agricultural data with regression data mining technique

Fetanat¹,

Mortazavifarr²,

Zarshenas³

2015

CeN

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Exposure to various abscission-promoting treatments suggests substantial ERF subfamily transcription factors involvement in the regulation of cassava leaf abscission

Liao

Yang

et al. 2016

BMC Genomics

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BackgroundCassava plants (Manihot esculenta Crantz) have obvious abscission zone (AZ) structures in their leaf pulvinus-petioles. Cassava leaf abscission can be triggered by either 17 days of water-deficit stress or 4 days of ethylene treatment. To date, little is known about cassava AP2/ERF factors, and less is known regarding their roles in regulating abscission zone development.ResultsHere, the cassava and Arabidopsis AP2/ERF genes were compared, finding that the cassava genome contains approximately 1.54-fold more ERF subfamily than the Arabidopsis genome. Microarray analysis was used to identify the AP2/ERF genes that are expressed in cassava leaf pulvinus-petiole abscission zones by comparing the AP2/ERF gene expression profiles of ethylene- and water-deficit stress-induced leaf abscission. In total, 99 AP2/ERF genes were identified as expressed in AZs across six time points during both ethylene- and water-deficit stress-induced leaf abscission. Comparative expression profile analysis of similar SOTA (Self Organizing Tree Algorithm) clusters at six time points during ethylene- and water-deficit stress-induced leaf abscission demonstrated that 20 ERF subfamily genes had similar expression patterns in response to both treatments. GO (Gene Ontology) annotation confirmed that all 20 ERF subfamily genes participate in ethylene-mediated signalling. Analysis of the putative ERF promoter regions shown that the genes contained primarily ethylene- and stress-related cis-elements. Further analysis of ACC oxidase activity in AZs across six time points during abscission shown increased ethylene production in response to both ethylene and water-deficit stress; however, the difference was more dramatic for water-deficit stress. Finally, the expression ratios of 20 ERF subfamily genes were analysed in two cassava cultivars, ‘KU50’ and ‘SC5’, that exhibit different levels of leaf abscission when challenged with the same water-deficit stress. The analysis indicated that most of the ERF genes were expressed at higher levels in the precocious abscission ‘KU50’ cultivar than in the delayed abscission ‘SC5’ cultivar.ConclusionCcomparative analysis of both ethylene- and water-deficit stress-induced leaf abscission shown that the ERF subfamily functions in the regulation of cassava abscission zone development.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2845-5) contains supplementary material, which is available to authorized users.

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The Application of Data Mining Techniques in Agricultural Science

Fetanat¹,

Mortazavifar²,

Zarshenas³

2015

CeN

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Information Technology has a positive impact on other disciplines. Using today's technology, precision agriculture and Information Technology are mixed together. Use of Information Technology in agriculture will lead to improvements in productivity. For this purpose, the raw data is transformed into useful information through data mining. This research determined whether data mining techniques can also be used to improve pattern recognition and analysis of large growth factors of ornamental plants experimental datasets. Furth ermore, the research aimed to establish data mining techniques can be used to assist in the classification and regression methods by determining whether meaningful patterns exist various growth factors of ornamental plants characterized at various research sites across Kish Island. Different data mining techniques were used analyze a large data base of ornamental plants properties attributes. The data base has been collected from different plants of Kish Island in various areas in order to determine, classify and predict effective growth factors on bloo ming. In this research, analyzed data with regression technique showed the effect of chlorophyll content on the number of flowers. The analy sis of these agricultural data base with different data mining methods may have some advantages in agriculture

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Prediction of leaf number by linear regression models in cassava

Cited by 3 publications

References 1 publication

The analysis of agricultural data with regression data mining technique

The analysis of agricultural data with regression data mining technique

Exposure to various abscission-promoting treatments suggests substantial ERF subfamily transcription factors involvement in the regulation of cassava leaf abscission

The Application of Data Mining Techniques in Agricultural Science

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