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.

翻译：本研究提出一种深度学习技术方案，旨在对三种茶树叶片病害进行分类识别，其中两种病害由害虫引起，一种由病原体（传染性生物）及环境因素导致，同时能够显示叶片受病害影响的区域面积。具体病害类型分别为红锈病、茶角盲蝽及红蜘蛛螨。本文评估了两种目标检测模型：SSD MobileNet V2 与 Faster R-CNN ResNet50 V1。SSD MobileNet V2 在交并比（IoU）阈值范围 0.50:0.95 下获得 0.209 的精确率与 0.02 的召回率，最终平均精度（mAP）为 20.9%；而 Faster R-CNN ResNet50 V1 在相同 IoU 范围内取得 0.252 的精确率与 0.044 的召回率，mAP 达到 25%，性能优于 SSD 模型。此外，本研究采用 Mask R-CNN 进行实例分割，并通过自定义方法计算叶片受病害损伤区域的比例。关键词：茶树叶片病害，深度学习，红锈病，茶角盲蝽，红蜘蛛螨，SSD MobileNet V2，Faster R-CNN ResNet50 V1，Mask R-CNN。