Iterative Sharpening of Digital Images

Sravankumar, B.; Anilkumar, Chunduru; Easwaramoorthy, Sathishkumar; Ramasubbareddy, Somula; Govinda, K.

doi:10.1007/978-981-13-3329-3_6

Cited by 6 publications

(1 citation statement)

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“…Future work is to add security features in supplying nutrient water supply and find any attack happened using digital forensics methods [22] [23]. Further applying image processing techniques to detect diseases in strawberry growth [24][25] [26].…”

Section: Discussionmentioning

confidence: 99%

Identifying High Significance Input Factors in Strawberry Production using Linear Model

Shin¹,

Rahman²,

Lee³

et al. 2019

IJITEE

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This study is focused on identifying the high significance of input factors in strawberry growth and production using a linear regression model. Greenhouse strawberry cultivation is increasing so fast due to the high demand for strawberry and farmers are also taking different types of technics for greenhouse cultivation to get high productions of strawberry. This study aims to increase the production of strawberries in order to maximize the profits from the cultivation of strawberries and also to fulfill the demand for strawberries. The strawberry data consist of average strawberry productions (AvgSP), electric conductivity (EC), potential of Hydrogen (PH) value, greenhouse inside temperature (Temp), greenhouse inside humidity, CO2 , nutrient solution with water, and supply of water nutrient solution. To find out the relationship among each input factor we use the correlation method and after that based on the correlation we make different types of combination of input factors. In this study, we use the linear regression method to find out the R2 value and significance factors of different combinations of input factors. For the linear regression model we take average strawberry production as output and different combination of input factors as input. In result and discussion, we concluded the high significance input factors in strawberry growth production.

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