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

Joseph K Mbugua, WATANYOO Suksa-ngiam
Issues in Informing Science and Information Technology  •  Volume 15  •  2018  •  pp. 043-056

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.

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.

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.

This research contributes to the body of knowledge by developing a novel model for predicting suitable areas for growing cassava.

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.

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

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.

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.

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.

geospatial, remote sensing, machine learning, suitability, cassava prediction
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