Knowledge of the health of banana trees is critical for farmers in order to profit from banana cultivation. Fusarium wilt and banana blood disease (BBD), two significant diseases that infect banana trees, are caused by Fusarium oxysporum and Ralstonia syzygii, respectively. They have caused a decline in crop yield, as they destroy trees, starting sequentially from the pseudostem to the fruit. The entire distribution of BBD and fusarium on a plantation can be understood using advanced geospatial information obtained from multispectral aerial photographs taken using unmanned aerial vehicles (UAVs) and a reliable data field for infected trees. Vegetation and soil indices derived from multispectral aerial photographs, such as the normalized difference vegetation index, the modified chlorophyll absorption ratio index, the normalized difference water index (NDWI), and soil pH, may have to be relied upon to explain the precise location of these two diseases. This study used a random forest algorithm to handle a large dataset consisting of multispectral and spectral models. The results show that the soil indices, soil pH, and NDWI are the most important variables for predicting the spatial distribution of these two diseases. Simultaneously, the plantation area affected by BBD is more extensive than that affected by fusarium if variations in planted banana cultivars are not considered.

Energy transition from fossil energy to renewable energy (green energy) which is more environmentally friendly has been regarded as an utter necessity that requ Energy transition from fossil energy to renewable energy (green energy) which is more environmentally friendly has been regarded as an utter necessity that requ

Knowledge on the health of banana trees is critical for farmers to profit from banana cultivation. Fusarium wilt and banana blood diseases (BDB), two significant diseases infecting banana trees, are caused by Fusarium oxysporum and Ralstonia syzygii, respectively. They have successfully caused a decline in crop yield as they destroy the trees, starting sequentially from the pseudostem to the fruits. The entire distribution of BDB and Fusarium on a plantation can be understood using advanced geospatial information obtained from multispectral aerial photographs taken using an unmanned aerial vehicle (UAV), combined with the reliable data field of infected trees. Vegetation and soil indices derived from a multispectral aerial photograph, such as normalized difference vegetation index, modified chlorophyll absorption ratio index, normalized difference water index (NDWI) and soil pH, may have to be relied on to explain the precise location of these two diseases. In this study, a random forest algorithm was used to handle a large dataset consisting of two models: the banana diseases multispectral model and the banana diseases spectral model. The results show that the soil indices, soil pH and NDWI are the most important variables for predicting the spatial distribution of these two diseases. Simultaneously, the plantation area affected by BDB is more extensive than that affected by Fusarium, if the variation of planted banana cultivars is not considered.

Oil and gas are important commodities in Indonesia and remain the main source for energy in various sectors. Therefore, the government aim to produce 1 million barrels of oil per day (BOPD) by 2030. To achieve this goal, exploration work is needed to discover new reserves and maintain production in existing fields. This study reviews the experience of oil and gas exploration in Indonesia using remote sensing data and the potential of using remote sensing data for oil and gas exploration through surface anomalies. Surface anomalies are changes or deviations that occur on the surface as the result of the presence of oil and gas underneath. These anomalies included vegetation growing stunted, yellowing or dying, changes in the quantity and composition of clay minerals, iron oxide, increased concentrations of hydrocarbons, helium, radon, carbon dioxide, microbes, and the presence of paraffin dirt formation, as well as geomorphological changes. This study aims to assess and explain the capabilities of remote sensing data in Indonesia for oil and gas exploration. The results show that remote sensing can be used for the initial exploration of oil and gas by delineating areas of potential oil and gas traps based on topographical anomalies and geological mapping integrated with gravity data and increasing confidence in the presence of oil and gas in the subsurface based on surface anomalies. These results are expected that the usefulness of remote sensing can be used to support oil and gas exploration in Indonesia and can be recognized and used for oil and gas activities by utilizing existing methods and discovering methods for data processing and their applications.