Remote Sensing and Machine Learning Modeling to Support the Identification of Sugarcane Crops

One of the main concerns of agricultural financing institutions is to make sure the loans they grant are used for the stated objective when the loan was requested. Specifically, when Banco Agrario de Colombia grants loans for crop farmers, it schedules verification visits to the cultivation sites to check if the crop stipulated in the loan agreement exists and assess its health. These visits are challenging to make due to the number of visits over vast areas that they need to schedule, lack of trained personnel, and difficulty of access. This article proposes a software tool, based on a machine learning model for processing free satellite imagery, to support the bank's identification of non-compliant crops with the investment plan before making field visits, minimizing the loss of investment by focusing on those areas to prioritize the visits. Sugarcane along the department of Boyacá, Colombia was chosen as the case of study. Free access satellite imagery through the Colombian Data Cube (CDCol) was used and machine learning models were applied on them to classify the land and predict the presence of the crop, a Random Forest model achieved an overall F1-score of 91% using Landsat-8 imagery and a K-nearest Neighbors model achieved an overall F1-score of 98% using Sentinel-2 imagery.