Detection and severity analysis of tea leaf blight based on deep learning

At present, the detection and severity estimation of tea diseases mainly rely on manual methods, which are time consuming and laborious. Existing machine learning and image processing methods used in disease detection and severity analysis of tea leaf blight (TLB) images captured in natural scenes have low accuracy because of the influence of light variation, shadow, varying shapes, and mutual occlusion of leaves. The current study proposes a deep learning method to improve the performance of detection and severity analysis of TLB. A Retinex algorithm is utilized to enhance the original images and reduce the influence of light variation and shadow. The TLB leaves are detected using a deep learning framework called Faster Region-based Convolutional Neural Networks, to improve the detection performance of blurred, occluded, and small pieces of diseased leaves. The detected TLB leaves are inputted into the trained VGG16 networks to achieve severity grading and facilitate disease severity analysis. Experimental results show that the detection average precision and the severity grading accuracy of the proposed method are improved by more than 6% and 9%, respectively, compared with the classical machine learning methods.

Automated summary

This study proposes a deep learning method to improve the detection and severity analysis of tea leaf blight, enhancing original images using Retinex algorithm and utilizing Faster Region-based Convolutional Neural Networks for leaf detection. VGG16 networks are then used for severity grading. Experimental results show over 6% improvement in detection precision and over 9% improvement in severity grading accuracy compared to classical machine learning methods.