scholarly journals Crop Yield Forecasting using Data Mining

2021 ◽  
Author(s):  
Pallavi Kamath ◽  
Pallavi Patil ◽  
Shrilatha S ◽  
Sushma ◽  
Sowmya S
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Yield Forecasting ◽  
Using Data ◽  
Crop Yield Forecasting

scholarly journals Crop yield forecasting of paddy and sugarcane through modified Hendrick and Scholl technique for south Gujarat

MAUSAM ◽  
2021 ◽  
Vol 67 (2) ◽  
pp. 405-410
Author(s):  
NEERAJ KUMAR ◽  
R. R. PISAL ◽  
S. P. SHUKLA ◽  
K. K. PANDEY
Keyword(s):  
Crop Yield ◽  
Range Analysis ◽  
Data Set ◽  
Regression Techniques ◽  
Weather Parameters ◽  
Yield Forecasting ◽  
Trial And Error Method ◽  
Using Data ◽  
Crop Yield Forecasting ◽  
Error Method

Regression models by modified Hendrick and Scholl technique were developed on paddy and sugarcane for six districts of south Gujarat. The data on the yield and weather parameters were analyzed for 27 years. The 25 year data was used for development of the model. The validation of model was done using data set of 2010 and 2011. The stepwise regression analysis was executed by trial and error method to obtain the finest combination of predictors, significant at 5% level. The multiple regression techniques was used for fitting of the model and decided best by highest R2 and lowest percent error. All crop yield forecasting models gave good estimates and produced error percent within acceptable range. Analysis revealed that the model error percent of paddy and sugarcane for respective crop growing districts were -10.0 to 8.1% and -12.2 to 1.5% respectively. Crop yield forecasting for year 2012 based on validated model was made for the districts of Navsari, Surat, Bharuch, Valsad, Narmada and Tapi.  

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Integrating the impact of wheat fungal diseases in the Belgian crop yield forecasting system (B-CYFS)

2012 ◽  
Vol 40 ◽  
pp. 8-17 ◽  
Author(s):  
Moussa EL Jarroudi ◽  
Louis Kouadio ◽  
Martin Bertrand ◽  
Yannick Curnel ◽  
Frédéric Giraud ◽  
...  
Keyword(s):  
Crop Yield ◽  
Fungal Diseases ◽  
Forecasting System ◽  
Yield Forecasting ◽  
Crop Yield Forecasting ◽  
The Impact
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