用深度学习检测三种茶叶病害并定位受损区域。
A Critical Study on Tea Leaf Disease Detection using Deep Learning Techniques
- 对比SSD MobileNet V2与Faster R-CNN ResNet50 V1检测效果
- Faster R-CNN mAP达25%，优于SSD的20.9%
- 结合Mask R-CNN实现病害区域精准分割
本文评估了两种目标检测模型：SSD MobileNet V2 和 Faster R-CNN ResNet50 V1，用于识别三种茶叶病害——红锈病（Red Rust）、赫洛佩尔蒂斯害虫（Helopeltis）和红蜘蛛螨（Red Spider Mite）。其中，红锈病由病原体及环境因素引起，其余两种由害虫造成。实验中，SSD MobileNet V2 在 IOU 0.50:0.95 范围下的精度为 0.209，召回率为 0.02，mAP 为 20.9%；Faster R-CNN ResNet50 V1 的精度为 0.252，召回率为 0.044，mAP 达 25%，表现更优。此外，采用 Mask R-CNN 实现病害实例分割，并提出自定义方法计算叶片受病区域占比。
原文摘要 · Abstract (English)
The proposed solution is Deep Learning Technique that will be able classify three types of tea leaves diseases from which two diseases are caused by the pests and one due to pathogens (infectious organisms) and environmental conditions and also show the area damaged by a disease in leaves. Namely Red Rust, Helopeltis and Red spider mite respectively. In this paper we have evaluated two models namely SSD MobileNet V2 and Faster R-CNN ResNet50 V1 for the object detection. The SSD MobileNet V2 gave precision of 0.209 for IOU range of 0.50:0.95 with recall of 0.02 on IOU 0.50:0.95 and final mAP of 20.9%. While Faster R-CNN ResNet50 V1 has precision of 0.252 on IOU range of 0.50:0.95 and recall of 0.044 on IOU of 0.50:0.95 with a mAP of 25%, which is better than SSD. Also used Mask R-CNN for Object Instance Segmentation where we have implemented our custom method to calculate the damaged diseased portion of leaves. Keywords: Tea Leaf Disease, Deep Learning, Red Rust, Helopeltis and Red Spider Mite, SSD MobileNet V2, Faster R-CNN ResNet50 V1 and Mask RCNN.
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