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.