Abstract— In improving the economy of developing countries,
the highest export commodity is the coffee plant. Indonesia
produces 639 thousand tons of coffee every year. Therefore,
establishing Indonesia as 4th in the world. However, decreased
productivity due to diseases of the coffee plant leaves can reduce
the productivity of coffee production. Leaf diseases include Miner,
Rust, Phoma, and Cercospora. Based on extant issues in
agriculture, utilization such as artificial intelligence, Computer
Vision, and Bigdata can decrease the costs incurred to trade with
plant diseases. With significant advances in artificial intelligence
in Machine Learning comes the Deep Learning method. YOLO is
Deep Learning seeded as an object detection compared to other
approaches. YOLOv7 is the latest version of the YOLO
architecture that can detect speed, high Precision, easy-to-train
data, and implementation. The main contribution of this research
was to develop using model-based YOLOv7, use coffee leaf
costume datasets, data augmentation, and preprocessing datasets.
This research utilizes Google Colab and GPU Tesla T4 to get a
result F1-score of 0.93, Precision of 0.926, Recall of 0.932,
mAP@IoU .5 of 0.956, mAP@IoU .5:.95 of 0.927 for the entire
trained data class. However, the best result is the binary class to
get a result F1-score of 0.99, Precision of 0.991, Recall of 1,
mAP@IoU .5 of 0.998, mAP@IoU .5:.95 of 0.994.