Deep Learning-Based Approach for Disease Stage Classification of Sunflower Leaf

Abstract

Accurate disease severity evaluation is crucial for managing the disease and yield loss. The classification of disease stages is essential for the estimation of disease severity. It takes extensive time for cultivators and botanical researchers to meticulously examine each leaf image and identify the disease stage to assess the severity of the disease at the field scale. Extracting the damaged leaf area is also achievable with image segmentation, although there are drawbacks such as threshold selection and lack of grayscale difference. Thus, deep learning has produced recent breakthroughs in various fields, such as high-resolution image synthesis, recognition, and categorization of images. In this work, the disease stages of two diseases (Alternaria leaf blight and Powdery Mildew) are classified using sunflower leaf images taken from sunflower farms in India (Marathwada State) during the Rabi season. With the help of botanists, images are labeled as three disease stage classes and one healthy stage as ground truth. A series of deep convolutional neural networks (Visual Geometry Group models with 16 and 19 neurons, respectively) with transfer learning and fine-tuning approach is trained, validated, and tested using stratified k-fold values four and five. The findings indicate that VGG16, with k-fold=5, gives the highest testing accuracy, which is 90.25%, with fine-tuning for Alternaria Leaf Blight. For VGG19 with kfold=5, the highest testing accuracy is 86.89% with fine- tuning for Powdery Mildew. Additionally, confidence interval calculation shows smaller intervals of 3% and 4% with a significance level of 95% for the VGG16 and VGG19 models, respectively.