scholarly journals A Potential Solution for Crop Yield Prediction by using Data Mining Techniques

2021 ◽  
Vol 9 (VI) ◽  
pp. 1211-1214
Author(s):  
Mrs. S.S.N.L. Priyanka
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Ph Value ◽  
Machine Learning Algorithms ◽  
Potential Solution ◽  
Factors Affecting ◽  
Data Mining Techniques ◽  
Managerial Decisions ◽  
Using Data

Agriculture is undoubtedly the largest livelihood provider in India and also contributes a significant figure to the economy of our Country. The technological factors affecting the crop production includes practices used and also managerial decisions. So, predicting the crop yield prior to its harvest would help farmers to take appropriate steps. We attempt to resolve the issue by building a user-friendly prediction system. The results of the prediction are suggested to the farmer such that suitable changes can be made in order to improve the produce. There are different techniques or algorithms which help to predict crop yield. By analyzing all the parameters like location, soil nutrients, pH value, rainfall, moisture a potential solution can be obtained to overcome the situation faced by farmers. This paper focuses on the analysis of the agriculture data and finding optimal yield to provide an insight before the actual crop production using data mining techniques and Machine Learning algorithms.

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. 

scholarly journals An Integrated  Approach for Cancer Survival Prediction Using Data Mining Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ishleen Kaur ◽  
M. N. Doja ◽  
Tanvir Ahmad ◽  
Musheer Ahmad ◽  
Amir Hussain ◽  
...  
Keyword(s):  
Data Mining ◽  
Ovarian Cancer ◽  
Cancer Patients ◽  
Missing Values ◽  
Life Quality ◽  
Advanced Ovarian Cancer ◽  
Machine Learning Algorithms ◽  
Survival Prediction ◽  
Data Mining Techniques ◽  
Using Data

Ovarian cancer is the third most common gynecologic cancers worldwide. Advanced ovarian cancer patients bear a significant mortality rate. Survival estimation is essential for clinicians and patients to understand better and tolerate future outcomes. The present study intends to investigate different survival predictors available for cancer prognosis using data mining techniques. Dataset of 140 advanced ovarian cancer patients containing data from different data profiles (clinical, treatment, and overall life quality) has been collected and used to foresee cancer patients’ survival. Attributes from each data profile have been processed accordingly. Clinical data has been prepared corresponding to missing values and outliers. Treatment data including varying time periods were created using sequence mining techniques to identify the treatments given to the patients. And lastly, different comorbidities were combined into a single factor by computing Charlson Comorbidity Index for each patient. After appropriate preprocessing, the integrated dataset is classified using appropriate machine learning algorithms. The proposed integrated model approach gave the highest accuracy of 76.4% using ensemble technique with sequential pattern mining including time intervals of 2 months between treatments. Thus, the treatment sequences and, most importantly, life quality attributes significantly contribute to the survival prediction of cancer patients.

scholarly journals Knn based Crop and Fertilizer Prediction

2020 ◽  
Vol 9 (4) ◽  
pp. 1453-1456
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Crop Yield ◽  
Accurate Result ◽  
Machine Learning Algorithms ◽  
Yield Prediction ◽  
Prediction Problem ◽  
Environmental Statistics ◽  
Classification And Regression ◽  
Using Data

India has always been active in agriculture, in fact even in this age of industrialization agriculture and agriculturebased industries continue to be a main source of income for a large percentage of the population. Machine learning and data mining have become, in the present day, are very important mediums when it comes to research in the crop yielding domain. Many a times we come across news on the paper about farmers committing suicide because of crop failures and increase in loans. In preventing such situations, crop yield prediction software can play a very important role. This research is an attempt in proposing a method to predict the success of crop for a particular area by using data on amounts and ratios of different components of soil like nitrogen, potassium, phosphorus and environmental statistics on temperature and weather. Various machine learning algorithms are used to get an accurate result. KNN is used for classification and regression prediction problem. It also attempts in providing a precise output on what fertilizers can be used to better the yield. Through this, therefore, farmers will also be able to predict their profits and final revenues.

Prognosis for Crop Yield Production by Data Mining Techniques in Agriculture

2019 ◽  
pp. 145-158 ◽  
Author(s):  
Divya Singh ◽  
Dinesh Sharma
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Agricultural Research ◽  
Rapid Development ◽  
Agricultural System ◽  
Yield Prediction ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
Yield Production

In agriculture, data mining technique is used for extracting information from a large dataset. The techniques for data mining are used in yield prediction for crop at broader spectrum. Agricultural system is very complex and vast therefore to deal with large data situation is a great factor. Different consultancy, industrial production department, organization related to crops is taking keen interest towards crop yield prediction. Here the focus is on the applicability of data mining techniques in agricultural field. The classification and clustering techniques of data mining are used recently in agriculture field. Data mining technology merged with the rapid development of computer science. This chapter focuses on collecting information and overcome the short comes of manual data handling and prediction of yield results of crop production. Data mining is a prominent agricultural research area for analysis of crop yield. These predictions are a very important in solving agricultural problems for crops.

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