A Deep Learning Enabled Multi-Class Plant Disease Detection Model Based on Computer Vision

Resumen

In this paper, a deep learning enabled object detection model for multi-class plant disease has been proposed based on a state-of-the-art computer vision algorithm. While most existing models are limited to disease detection on a large scale, the current model addresses the accurate detection of fine-grained, multi-scale early disease detection. The proposed model has been improved to optimize for both detection speed and accuracy and applied to multi-class apple plant disease detection in the real environment. The mean average precision (mAP) and F1-score of the detection model reached up to 91.2%" role="presentation" style="position: relative;">91.2%91.2% 91.2 % and 95.9%" role="presentation" style="position: relative;">95.9%95.9% 95.9 % , respectively, at a detection rate of 56.9 FPS. The overall detection result demonstrates that the current algorithm significantly outperforms the state-of-the-art detection model with a 9.05%" role="presentation" style="position: relative;">9.05%9.05% 9.05 % increase in precision and 7.6%" role="presentation" style="position: relative;">7.6%7.6% 7.6 % increase in F1-score. The proposed model can be employed as an effective and efficient method to detect different apple plant diseases under complex orchard scenarios.