用深度学习检测茶树病害，定位病斑并区分虫害与病原体。
A Critical Study towards the Detection of Parkinsons Disease using ML Technologies
- 对比SSD MobileNet V2与Faster R-CNN ResNet50 V1进行病害检测
- Faster R-CNN在mAP达25%优于SSD的20.9%
- 结合Mask R-CNN定量计算叶片受损面积，适合农业病害分析
本文提出一种基于深度学习的茶树病害检测方法，可识别三类病害：红锈病、Helopeltis害虫和红蜘蛛螨害。采用两种目标检测模型：SSD MobileNet V2与Faster R-CNN ResNet50 V1，在IOU范围0.50:0.95下，SSD MobileNet V2的精度为0.209，召回率为0.02，mAP为20.9%；Faster R-CNN ResNet50 V1精度达0.252，召回率0.044，mAP提升至25%，表现更优。同时使用Mask R-CNN实现病害实例分割，并提出自定义方法计算叶片受损害区域占比。实验数据来自茶树叶片图像数据集，模型用于病害定位与量化分析。
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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