[1911.10317v1] PlantDoc: A Dataset for Visual Plant Disease Detection
Our benchmark experiments show the lack of efficacy of models learnt on controlled datasets, thereby, showing the significance of real-world datasets such as ours
India loses 35\% of the annual crop yield due to plant diseases. Early detection of plant diseases remains difficult due to the lack of lab infrastructure and expertise. In this paper, we explore the possibility of computer vision approaches for scalable and early plant disease detection. The lack of availability of sufficiently large-scale non-lab data set remains a major challenge for enabling vision based plant disease detection. Against this background, we present PlantDoc: a dataset for visual plant disease detection.
Our dataset contains 2,598 data points in total across 13 plant species and up to 17 classes of diseases, involving approximately 300 human hours of effort in annotating internet scraped images. To show the efficacy of our dataset, we learn 3 models for the task of plant disease classification. Our results show that modelling using our dataset can increase the classification accuracy by up to 31\%. We believe that our dataset can help reduce the entry barrier of computer vision techniques in plant disease detection.
‹Figure 1: Samples from various classes in the PlantDoc Dataset show the gap between lab-controlled and real-life images (Introduction)Figure 4: An Image with bounding boxes and its cropped leaves (Data Collection)Figure 5: Dataset Compare (Plant image classification using cropped images)Figure 6: Model Compare (Plant image classification using cropped images)Figure 2: Statistics of PlantDoc Dataset (The PlantDoc Dataset)Figure 9: Species Confusion Matrix:(a) Test (b) Train (Misclassification Judgement)Figure 10: Saliency and Activation Maps shows the affected parts of disease in a leaf. (Misclassification Judgement)Figure 11: Leaf detection results in our mobile application (Results and Discussion)Figure 12: Tomato Bacterial leaf spot(a)and Septoria(b) looks similar and are hard to label using visual features alone (Results and Discussion)›