A Critical Study on Tea Leaf Disease Detection using Deep Learning Techniques

TL;DR

This paper evaluates deep learning models for detecting and segmenting three types of tea leaf diseases, demonstrating that Faster R-CNN ResNet50 V1 outperforms SSD MobileNet V2 in accuracy.

Contribution

The study compares two object detection models and introduces a custom Mask R-CNN method for segmenting diseased areas on tea leaves.

Findings

Faster R-CNN ResNet50 V1 achieved higher mAP than SSD MobileNet V2.

The models can classify diseases caused by pests and pathogens.

A custom Mask R-CNN approach effectively segments damaged leaf areas.

Abstract

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.