Pepercorn Leaf Disease Detection and Classification model Using Deep Learning Approach

Particularly in the most lowland areas of Ethiopia, Pepercorn is an essential crop that makes a substantial contribution to the country's agricultural economy. However, several diseases that affect crop output and quality provide a barrier to Pepercorn production. Conventional disease detection techniques depend on specialist knowledge and manual inspections, which are frequently time-consuming and ineffective, limiting prompt response. Digital image processing, computer vision, and deep learning technologies have a lot of potential, but their use in Ethiopia's agriculture industry is still unexplored. The need for more sophisticated methods is highlighted by the fact that previous studies primarily used manual feature extraction techniques for disease detection. After a careful analysis of relevant literature, four deep learning architectures were selected: VGG16, VGG19, DenseNet121 and YOLOv11n. Several train-test data splits, such as 70%/30%, 80%/20% and 90%/10% were explored to assess model performance; the VGG19 with 90%/10% split produced the best accuracy 98.05% in case of VGGNet. And the DenseNet121 with 80%/20% achieves better accuracy 98.75% than VGG19. But YOLOv11n is the better model among the entire models researchers used. It achieves a mean average precision (MAP) of 99.03%. When we see the results in terms of performance (speed) the YOLOv11n model performs its preprocessing and post processing tasks in 1Hr, 096 Seconds, while DenseNet121 takes a speed of 2Hr, 35 Seconds. According to the study's finding, out of all the algorithms studied, YOLOv11 is the best model for Pepercorn leaf disease detection and classification.