Abstract:
Earlier estimation of sugar cane yield is a vital element for sugar industry. Many operations of sugar production are based on yield estimation, such as planning for the season management including manpower, transportation, storage, marketing ...etc. The direct ground field estimation has low precision and feasibility due to the wide areas of the sugarcane fields (50-100 hectares), difficult accessibility especially during the rainy season, the height of the plants (1.5-2.00 m), high cost of transportation together with the limitations of human`s eyes. The proposed method in this study is based on the applications of remote sensing and Geographical Information System; Normalized Different Vegetation Index (NDVI) had been used to estimate the sugar cane yield the results were compared to the yield at the end of the season which was also compared to the estimation from the direct ground field method. The precision of estimation of the direct ground field method was 50% while of NDVI was 90% compared to the actual yield of the season. This estimation will help in the management of the season and support decision making for sugarcane farms as the needed information were available on time with less cost and effort.
