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Corresponding author is with the Department of Information & Communication Engineering, Sunchon National University, Suncheon-si, Republic of Korea,57922. E-Mail address:

Strawberry is one of the most well-liked fruits all over the world, and strawberry productions is rapidly rising as one of the most healthy economies all over the world. Due to the high demand for strawberries, greenhouse strawberry cultivation is increasing rapidly and farmers are using different types of methods for greenhouse cultivation to get high productions. The aims of this study are to find out the high production rate of the strawberry based on nutrient solutions with water flow rate. Farmers use a different amount of water nutrient solutions for strawberry production but mostly they don’t know how much nutrient solutions with water flow is good for getting high production because of giving the high amount or less amount of nutrient solutions with water flow are always not good or bad for productions. Farmers have to know about the amount of nutrient solutions with water flow for getting good productions and now this is the high time to support farmers for increasing their strawberry productions by giving technological support. Therefore, in this study, we analyze the production rate of strawberries based on the nutrient solutions with water flow rate of strawberries in every bed. Finally, through the results and discussion by using the support vector regression model we find out that how much nutrient solutions with water flow should be needed to obtain high yielding of strawberries.

딸기는 전 세계적으로 가장 선호하는 과일 중 하나 이며, 딸기 생산은 전 세계에서 가장 큰 healthy economies 중 하나로 급속히 성장하고 있다. 딸기에 대한 수요가 높기 때문에 온실 딸기 재배가 급속히 증가하고 있으며 농부들은 높은 생산량을 얻기 위해 온실 재배를 위한 여러 유형의 방법을 사용하고 있다. 본 연구의 목적은 영양수의 유속에 따른 딸기의 높은 생산율을 알아내는 것이다. 농부들은 딸기 생산을 위해 다른 양의 영양 용액을 사용하지만 대부분 유속에 따라서 얼마나 많은 양의 영양분이 공급되는지, 공급하는 양의 적고 많음에 따라서 딸기에 긍정적, 부정적 영향을 미치는지 모른다. 농부들은 좋은 생산물을 얻기 위해 물의 흐름에 따른 양분 용액의 양을 알아야 하며, 농민들이 기술 지원을 통해 딸기 생산량을 늘릴 수 있도록 지원해야 할 때가 되었다. 따라서 본 연구에서는 딸기 테스트 베드에 주는 양분 용액의 유속에 따른 딸기 생산율을 분석 하였다. 마지막으로 지원 벡터 회귀 모델로부터 얻어낸 결과와 논의를 통해 딸기의 높은 수확량을 얻는데 필요한 양분용액의 유속을 분석하였다.

Nowadays strawberry is one of the most popular fruits all over the world. Strawberry is so much nutrient fruits also. Strawberry fruits consist of several nutrients such as Potassium, Carbohydrate, Protein, Calcium, Magnesium, Iron and high content of Vitamin C, etc.

Due to the high demand for strawberries, greenhouse strawberry cultivation is growing up rapidly. For greenhouse cultivation, nowadays farmers are using different types of method. Some method is used for growing plants with soil by using mineral nutrient solutions in a water solvent. Nitrogen, Potassium, and Phosphorus (NPK) are essential for basic plant survival. Nitrogen is for cell growth, phosphorus for the roots, flowers, and buds, and potassium is for photosynthesis

These study deals to analysis the nutrient solutions with water flow rate and the productions rate of strawberries. We used support vector regression algorithm to predict the average nutrient solutions with water flow rate and the average productions rate. Then we compare all nutrient solutions flow rate and the productions to find out the best water nutrition’s flow rate for getting high productions. For doing all the steps, we used strawberry greenhouse data, which has three beds and farmers used three different amounts of nutrient solutions with water flow rate for three different beds.

In statistics, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data. Support Vector Machine can also be used as a regression method, maintaining all the main features that characterize the algorithm (maximal margin). The Support Vector Regression (SVR) uses the same principles as the SVM for classification, with only a few minor differences. First of all, because output is a real number it becomes very difficult to predict the information at hand, which has infinite possibilities. In the case of regression, a margin of tolerance (epsilon) is set in approximation to the SVM which would have already requested from the problem. But besides this fact, there is also a more complicated reason, the algorithm is more complicated therefore to be taken in consideration. However, the main idea is always the same: to minimize error, individualizing the hyperplane which maximizes the margin, keeping in mind that part of the error is tolerated. Equation of Support vector regression;

Here, y - is the output variable, x -is the number of input variable, w –is the slope of the line and b –is the error term.

In this study, we use the year 2015 September to 2016 May strawberry data, which is gained from a greenhouse strawberry farm in South Korea named Mebangsuliang. There are two types of parameters are available in the greenhouse data, first one is nutrient solutions with water flow and another one is the production rate of strawberries. Three beds are available in this greenhouse, and beds names are MBsul_A, MBsul_B, and MBsul_C. In every bed, there are six plots and in each plots, twelve plants are available. Total seventy-two plants are available in each bed. Farmers used three different types of nutrient solutions with water flow for three different beds and they got three different types of productions from three individual beds.

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In result and discussion part, we use the support vector regression for predicting average water nutrition’s flow rate for three beds and the average productions rate of three beds. From these results, we find out the best water nutrition’s flow rate from among three for getting high productions.

In this study, we use one greenhouse data, which has three beds Mbsul_A, Mbsul_B&Mbsul_C. From this greenhouse, we got two types of data one is nutrient solutions with water flow rate for every beds and another one is productions rate of every beds.

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Here we find out total amount of nutrient solutions with water flow rate and total amount of strawberry productions. From <

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Beds | Maximum water nutrient solutions(L) | Average water nutrient solutions(L) | Total Water Nutrient Solutions(L) | Average Strawberry Productions(kg) | Total Strawberry Productions(kg) |

Mbsul_A | 1452.096 | 872.94 | 37536.44 | 9.433855 | 405.6558 |

Mbsul_B | 1492.778 | 892.95 | 38396.85 | 10.74421 | 462.0009 |

Mbsul_C | 1431.25 | 969.96 | 41708.63 | 9.478254 | 407.5649 |

From table1 we can easily find out the best productions of strawberry among three beds. From this table, we can know about the total strawberry productions, average productions of strawberry, average water nutrient solutions for per bed, maximum water nutrient solutions for every bed and about the total nutrient solutions with water flow for three beds are Mbsul_A, Mbsul_B, Mbsul_C. Table1 shows that Mbsul_B gives the best strawberry productions among three beds. Mbsul_B bed total strawberry productions are 462.0009(kg), average strawberry productions are 10.74421(kg) and total nutrient solutions flow is 38396.85 (L). Total productions of Mbsul_A and Mbsul_C are 405.6558(kg) & 407.5649(kg). Average strawberry productions and total nutrient solutions flow of two beds accordingly 9.433855(kg)&37536.44(L) for Mbsul_A and 9.478254(kg)& 41708(L) for Mbsul_C.

This paper focused on to find out high productions of strawberries based on water nutrient solutions flow. All results and analyze provided us acuteness between nutrient solutions with water flow and strawberry productions. All results about three different types of nutrient solutions with water flow with three different productions and this work drills to find out high productions of strawberries based on water nutrient solutions flow. We analyze absorbed data with the fitted line of support vector regression. We compare three different types of nutrient solutions with water flow among them and compare three fitted lines of support vector regression. We compare three different productions and the fitted line of support vector regression. After comparing all three beds nutrient solutions flow and productions we find out that bed Mbsul_B gives us the best strawberry productions among three beds also with an optimal water nutrient flow. The total production rate and nutrient solutions of bed Mbsul_B are 462.0009(kg) & 38396.85 (L). On the other hand, total productions of Mbsul_A and Mbsul_C are 405.6558(kg) & 407.5649(kg). Average strawberry productions and total nutrient solutions flow of two beds accordingly 9.433855(kg) &37536.44(L) for Mbsul_A and 9.478254(kg) & 41708(L) for Mbsul_C. So, the production of Mbsul_B is far better than Mbsul_A& Mbsul_C also the nutrient solutions flow of Mbsul_B is an optimum nutrient solution. Mbsul_B nutrient solutions flow is close to nutrient solutions of bed Mbsul_A and less than Mbsul_C water nutrient solutions flow. From <

This work was carried out with the support of "Cooperative Research Program for Agriculture Science <Technology Development (Project No. PJ01188605)" Rural Development Administration, Republic of Korea and, this research was supported by IPET (Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries) through Advanced Production Technology Development Program, funded by MAFRA (Ministry of Agriculture, Food and Rural Affairs) (No. 315001-5)