Use of Plant Health Level Based on Random Forest Algorithm for Agricultural Drone Target Points

Use of Plant Health Level Based on Random Forest Algorithm for Agricultural Drone Target Points

NDV; drone; agriculture; random forest

Chemical residues from the use of pesticides in agriculture can impact human health through environmental and food
pollution. To lessen the negative effects of excessive pesticide use, pesticides must be applied to plants by dose. The dose of
pesticide application can be based on a plant health level, which is the result of drone Normalized Difference Vegetation
Index (NDVI) image analysis. Drones can also be used for spraying pesticides. Analysis of plant health levels was carried
out using the Random Forest (RF) algorithm. The results of the classification plant health levels will be used to design spray
drone flight routes. The objective of this research is to classify plant health levels of rice based on NDVI imagery using the
RF algorithm and to compile a database of spray drone target points. The results of this study indicate that the classification
of plant health levels using the RF algorithm produces an accuracy value of 98% and a Kappa value of 0.96. As a result, the
model developed and the algorithm employed is quite effective at classifying the level of plant health. Furthermore, spray
drone target points based on plant health levels can be generated. Optimally the spray distance between rows is 2 m

Try Kusuma Wardana, Yandra Arkeman, Karlisa Priandana, Farohaji Kurniawan

http://jurnal.iaii.or.id

Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association

June 2023

Sri Wahyuni

ISSN Media Electronic: 2580-0760

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