• Atthachai Bunprasert Department of Geography, Faculty of Social Science, Chiang Mai University.
  • Arisara Charoenpanyanet Department of Geography, Faculty of Social Science, Chiang Mai University.


Satellite Imagery, Arabica coffee, Anthracnose disease spread areas, Productivity Prediction


Currently, there are technological surveys that recognize remote applications as diverse, particularly satellite technology that analyzes spatial data on Earth from space. This technology facilitates rational spatial decision analysis, saving time before surveys. The purpose of this study is to investigate and analyze the factors that contribute to the occurrence of Anthracnose disease in Arabica coffee and predict the productivity of Arabica coffee during an outbreak of Anthracnose disease in the Doi Chang area of Wawee sub-district, Mae Sui district, Chiang Rai province. The study found that the factors influencing the occurrence of Anthracnose disease are Land Surface Temperature (LST), Elevation, and Normalized Difference Moisture Index (NDMI), with a model accuracy of 95.4 percent. Factors such as leaf area index (LAI) and Normalized Difference Moisture Index (NDMI) are relevant regarding the Productivity Prediction of Arabica coffee. The R-square value is 0.594, and the Mean Square Error is 76.31 Kg per Rai. This study presents an alternative option for preparing risk management strategies to mitigate the impact of plant diseases on Arabica coffee and evaluating coffee productivity before entering the market in the future.


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How to Cite

Bunprasert, A., & Charoenpanyanet, A. (2024). PREDICTION OF ANTHRACNOSE DISEASE SPREAD AREAS AND PRODUCTIVITY OF ARABICA COFFEE USING SATELLITE IMAGERY. Srinakharinwirot University Journal of Sciences and Technology, 16(31, January-June), 1–17, Article 248175. Retrieved from