Detection of crops from satellite images for biomass fuzzy estimation: A case study on oil palm crops in Colombia
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Ensuring access to safe, affordable, and non-polluting energy sources is an important goal for sustainable development. One such source is residual biomass, which can be used for the production of biogas through anaerobic digestion methods. To aid the adoption of green energy production, a general methodology is developed and implemented for the automatic detection of crops, which should enable the corresponding estimation of their residual biomass potential through a fuzzy formulation. This study explores the use of Machine and Deep Learning jointly with RGB images taken by the Sentinel 2 satellite over different regions in Colombia. As a result, convolutional neural networks achieved a validation accuracy in the detection of crops of 97.7%, while XGBoost models achieved a 96% accuracy. A specific case study is developed for oil palm plantations, implementing the general methodology for detecting crops and estimating their bio-energy potential.
