Computer vision based method for severity estimation of tea leaf blight in natural scene images

Tea leaf diseases seriously affect the yield and quality of tea. Early warning and severity estimation of the diseases can be used to guide tea farmers to spray pesticide reasonably. Tea leaves infected with leaf blight are usually damaged, deformed, and occluded. An insufficient number of disease image samples will lead to overfitting of the estimated model. Thus, existing methods based on machine learning can only estimate the severity of tea diseases in natural scene images with low accuracy. Aiming to solve these problems, this study proposes a computer vision based method for the severity estimation of tea leaf blight in RGB images obtained under natural scenes. In this method, the influence of complex backgrounds is reduced by segmenting diseased tea leaves and spots, the problems of partial occlusion, deformation and damage of diseased leaves are solved by area fitting, and the severity of tea leaf blight is accurately estimated by the gradient boosting machine. Compared with classical machine learning methods and conventional convolution neural network methods, the method presented in this study only needs a small number of manually labeled samples and has better accuracy and robustness for the severity estimation of tea leaf blight in natural scene images.