Detecting Palm Oil Deficiencies: A Study of Boron, Nitrogen, Potassium, And Magnesium Deficiencies Using Yolov5 Model

Rusdi Efendi, Nurul Laila Tusya’diah, Ruvita Faurina


Since palm oil plants are extremely hungry for nutrients, this will affect their growth and production. In this research, the YOLOv5 model was utilized as the primary analysis and data interpretation tool. This research aimed to develop an Android-based application to identify plant deficiency issues in the palm oil industry. The deficiencies examined were boron, potassium, magnesium, and nitrogen from the dataset of 2,789 palm oil leaf image samples acquired for training and analysis. At two different Intersection Over Union (IoU) thresholds of 0.5 and 0.75, the model training results demonstrated high precision, recall, and mean average precision (mAP) levels. The IoU assessment results for values of 0.5 were: boron (0.989), potassium (0.577), magnesium (0.968), nitrogen (0.96), and the healthy class (0.995). At an IoU value of 0.75, the obtained results were: boron (0.991), potassium (0.564), magnesium (0.968), nitrogen (0.958), and healthy (0.995).


Object Detection, YOLOv5, Deficiency, Palm Oil

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