Indonesia's growing area of cultivation and agriculture requires a monitoring system of growingvegetation on it. By using images captured from the top (orthophotos), the result was processedusing algorithms of digital image processing combined with machine learning in order to get theaccurate information of vegetation status. The long-term goal of this program is the establishment ofan independent business unit engaged in the field of orthophotos image sensing for the purposes ofutilization in agriculture, health, territorial mapping, mining, and industrial and governanceindustries requiring sensing results taken from the upper side of the object. For applicationdevelopment, agile methodology is used, while business-side planning use business model canvasand SWOT analysis. With the geospatial orthophotos, it is possible to identify which part of theplantation land is fertile for planted crops, means to grow perfectly. It is also possible furthermoreto identify less fertile in terms of growing but not perfect, and also part of plantation field that is notgrowing at all. This information can be easily known quickly with the use of UAV photos. Theresulting orthophotos image were processed using Matlab including classification of fertile,infertile, and dead palm oil plants by using Gray Level Co-Occurrence Matrix (GLCM) method.From the results of research conducted with 30 image samples, it was found that the accuracy of thesystem can be reached by using the features extracted from the matrix as parameters Contrast,Correlation, Energy, and Homogeneity.
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