ADAPTIVE MODELS FOR DETERMINING CROPLAND SUITABILITY AND TOTAL HARVEST PREDICTION FOR POTATO USING IOT - yusianto, rindra (Potato Journal, 2023)
ABSTRACT: Potatoes are Indonesia’s fourth largest food agro-industrial and leading horticultural commodities tradedinternationally. In 2021, the needs of the potato industry will only be met by 85.93%. This study aims to providerecommendations for adaptive models for increasing productivity based on the suitability of agricultural cropland andpredicting total harvest using IoT. We modified the multi-thresholding method by installing an SHT15 sensor to measuretemperature. We also installed rain gauge sensors and analyzed the spatial perspective. Using a drone quadcopter, weperform image processing and mapping to predict the total harvest. The research sample used a random of 12 grids in anagricultural area in Wonosobo, Indonesia. The results showed that the most suitable agricultural cropland was 11.05%, suitablewas 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 harvestof 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 accuracyrate of 89.35%. In addition, to balance supply and demand, this model recommends an increase in production by 30% andfulfillment from other regions by 31%.