Monitoring of maize crops through multispectral aerial imagery processed with artificial intelligence.
DOI:
https://doi.org/10.71112/5yx9sp43Keywords:
Remote sensing, precision agriculture, vegetation index, image segmentation, machine learningAbstract
Different artificial intelligence models were evaluated for the segmentation of multispectral images of maize crops obtained by drone, with the purpose of monitoring their development under traditional farming conditions in El Salvador. The models Binary Threshold, Random Forest, K-Means, DNN, and CNN-MobileNet were applied to discriminate maize, weed, and soil areas from images with colors normalized based on the GNDVI index. The results showed that Random Forest achieved the highest accuracy (0.95) and consistency in predicting the vegetation indices LAI and GNDVI, significantly outperforming deep learning models, whose results were limited by the quality of the masks generated by K-Means. The Binary Threshold method also showed acceptable performance due to its simplicity and speed. It was concluded that Random Forest provided balanced results in terms of accuracy, cost, and practical applicability for monitoring subsistence crops.
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