Predicting Suitable Areas for Growing Cassava Using Remote Sensing and Machine Learning Techniques: A Study in Nakhon-Phanom Thailand

Aim/Purpose: Although cassava is one of the crops that can be grown during the dry season in Northeastern Thailand, most farmers in the region do not know whether the crop can grow in their specific areas because the available agriculture planning guideline provides only a generic list of dry-season crops that can be grown in the whole region. The purpose of this research is to develop a predictive model that can be used to predict suitable areas for growing cassava in Northeastern Thailand during the dry season. Background: This paper develops a decision support system that can be used by farmers to assist them determine if cassava can be successfully grown in their specific areas. Methodology: This study uses satellite imagery and data on land characteristics to develop a machine learning model for predicting suitable areas for growing cassava in Thailand’s Nakhon-Phanom province. Contribution: This research contributes to the body of knowledge by developing a novel model for predicting suitable areas for growing cassava. Findings: This study identified elevation and Ferric Acrisols (Af) soil as the two most important features for predicting the best-suited areas for growing cassava in Nakhon-Phanom province, Thailand. The two-class boosted decision tree algorithm performs best when compared with other algorithms. The model achieved an accuracy of .886, and .746 F1-score. Recommendations for Practitioners: Farmers and agricultural extension agents will use the decision support system developed in this study to identify specific areas that are suitable for growing cassava in Nakhon-Phanom province, Thailand Recommendation for Researchers: To improve the predictive accuracy of the model developed in this study, more land and crop characteristics data should be incorporated during model development. The ground truth data for areas growing cassava should also be collected for a longer period to provide a more accurate sample of the areas that are suitable for cassava growing. Impact on Society: The use of machine learning for the development of new farming systems will enable farmers to produce more food throughout the year to feed the world’s growing population. Future Research: Further studies should be carried out to map other suitable areas for growing dry-season crops and to develop decision support systems for those crops.

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Predicting Suitable Areas for Growing Cassava Using Remote Sensing and Machine Learning Techniques: A Study in Nakhon-Phanom Thailand

10.28945/4068 ◽

2018 ◽

Keyword(s):

Machine Learning ◽

Decision Support ◽

Decision Support System ◽

Support System ◽

Dry Season ◽

The Body ◽

Machine Learning Techniques ◽

Future Research ◽

Ground Truth Data ◽

Northeastern Thailand

Aim/Purpose: [This Proceedings paper was revised and published in the 2018 issue of the journal Issues in Informing Science and Information Technology, Volume 15] Although cassava is one of the crops that can be grown during the dry season in Northeastern Thailand, most farmers in the region do not know whether the crop can grow in their specific areas because the available agriculture planning guideline only provides a generic list of dry-season crops that can be grown in the whole region. The purpose of this research is to develop a predictive model that can be used to predict suitable areas for growing cassava in Northeastern Thailand during the dry season. Background: This paper develops a decision support system that can be used by farmers to assist them in determining if cassava can be successfully grown in their specific areas. Methodology: This study uses satellite imagery and data on land characteristics to develop a machine learning model for predicting the suitable areas for growing cassava in Thailand’s Nakhon-Phanom Province. Contribution: This research contributes to the body of knowledge by developing a novel model for predicting suitable areas for growing cassava. Findings: This study identified elevation and Ferric Acrisols (Af) as the two most important features for predicting the best-suited areas for growing cassava in Nakhon-Phanom province, Thailand. Together with other predictors, soil types contributed to the improvement of the overall model based the F-score. The Boosted Decision Tree was the best algorithm for predicting cassava in the area. Recommendations for Practitioners: Farmers and agricultural extension agents will use the decision support system developed in this study to identify specific areas that are suitable for growing cassava in Nakhon-Phanom province, Thailand. Recommendation for Researchers: To improve the predictive accuracy of the model developed in this study, more land and crop characteristics data should be incorporated during model development. The ground truth data for areas growing cassava should also be collected for a longer period to provide a more accurate sample of the areas that are suitable for cassava growing. Impact on Society: The use of machine learning for the development of new farming systems will enable farmers to produce more food throughout the year to feed the world’s growing population. Future Research: Further studies should be carried out to map other suitable areas for growing dry-season crops and to develop decision support systems for those crops.

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Variables Influencing Machine Learning-Based Cardiac Decision Support System: A Systematic Literature Review

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.892.274 ◽

2019 ◽

Vol 892 ◽

pp. 274-283

Author(s):

Mohammed Ashikur Rahman ◽

Afidalina Tumian

Keyword(s):

Machine Learning ◽

Decision Support ◽

Literature Review ◽

Decision Support System ◽

Clinical Decision Support ◽

Support System ◽

Clinical Decision ◽

Machine Learning Techniques ◽

Cardiac Care ◽

Learning Techniques

Now a day, clinical decision support systems (CDSS) are widely used in the cardiac care due to the complexity of the cardiac disease. The objective of this systematic literature review (SLR) is to identify the most common variables and machine learning techniques used to build machine learning-based clinical decision support system for cardiac care. This SLR adopts the Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) format. Out of 530 papers, only 21 papers met the inclusion criteria. Amongst the 22 most common variables are age, gender, heart rate, respiration rate, systolic blood pressure and medical information variables. In addition, our results have shown that Simplified Acute Physiology Score (SAPS), Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) are some of the most common assessment scales used in CDSS for cardiac care. Logistic regression and support vector machine are the most common machine learning techniques applied in CDSS to predict mortality and other cardiac diseases like sepsis, cardiac arrest, heart failure and septic shock. These variables and assessment tools can be used to build a machine learning-based CDSS.

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