Spatial Analysis in Detecting the Level of Land Suitability for Clove Plants - Muhlis, Muhlis; Jusran, Jusran; Dania, Mochamad Ikbal Rezki; Muhtar, Muhtar (Journal La Lifesci, 2025)
Plantation crops remain a central choice for many farmers because of their promising yields, and clove is among the most valued due to its strong market demand. Despite this potential, clove cultivation frequently faces constraints, particularly sudden stem bud decline and disease susceptibility. These problems often arise from land that does not meet the crop’s ecological requirements, yet many farmers still find it difficult to obtain reliable and timely information on land suitability. Spatial data offers an effective solution because it provides rapid and up to date insights into environmental conditions without requiring farmers to visit each site. When combined with big data and smart farming technologies, spatial information becomes even more useful, since farmers can monitor climate patterns, soil temperature and soil texture more easily.This study aims to generate accurate information on land suitability for clove cultivation through spatial big data and to demonstrate the role of smart farming systems in detecting suitability levels. Using a quantitative approach, land conditions were classified into four suitability categories which include very suitable, suitable, marginal and not suitable. Landsat imagery from Sinjai Regency in 2024 identified approximately 24,566 pixels or 2,282 hectares of land used for clove cultivation. These areas were concentrated in South Sinjai, Central Sinjai, Sinjai Borong and West Sinjai. Additional land cover classes consisted of primary forest, rice fields, settlements, secondary forest, annual crops and mixed plantations. The classification results were supported by categorical accuracy testing, highlighting the need to evaluate each land use type individually to ensure the reliability of the spatial interpretation.