ADAPTIVE MODELS FOR DETERMINING CROPLAND SUITABILITY AND TOTAL HARVEST PREDICTION FOR POTATO USING IOT


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Authors

  • rindra yusianto Universitas Dian Nuswantoro

Keywords:

cropland suitability, iot, potato, spatial perspective, total harvest

Abstract

ABSTRACT: Potatoes are Indonesia’s fourth largest food agro-industrial and leading horticultural commodities traded
internationally. In 2021, the needs of the potato industry will only be met by 85.93%. This study aims to provide
recommendations for adaptive models for increasing productivity based on the suitability of agricultural cropland and
predicting total harvest using IoT. We modified the multi-thresholding method by installing an SHT15 sensor to measure
temperature. We also installed rain gauge sensors and analyzed the spatial perspective. Using a drone quadcopter, we
perform image processing and mapping to predict the total harvest. The research sample used a random of 12 grids in an
agricultural area in Wonosobo, Indonesia. The results showed that the most suitable agricultural cropland was 11.05%, suitable
was 22.9%, and cropland with several inhibiting factors was 62.01%. The most convenient location is at the coordinates 7°
15’ 12.1” S latitude, 109° 55’ 27.5” E longitude in Kejajar, Wonosobo District. The results also showed an average harvest
of 13.79 t ha-1. This model can predict an increase in yields with a production prediction of 72,765 t year-1 and an accuracy
rate of 89.35%. In addition, to balance supply and demand, this model recommends an increase in production by 30% and
fulfillment from other regions by 31%.

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Submitted

2023-03-30

Published

2023-08-14

How to Cite

yusianto, rindra. (2023). ADAPTIVE MODELS FOR DETERMINING CROPLAND SUITABILITY AND TOTAL HARVEST PREDICTION FOR POTATO USING IOT. Potato Journal, 50(1). https://epubs.icar.org.in/index.php/PotatoJ/article/view/134816