PlantDoc: A Dataset for Visual Plant Disease Detection

By D. Singh, Naman Jain, Pranjali Jain, Pratik Kayal, Sudhakar Kumawat, Nipun Batra. 2019

698 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Ecology, Plant Science, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Jun Liu
2. Xuewei Wang

In Ecology

1. Jun Liu
2. Xuewei Wang

In Plant Science

1. Jun Liu
2. Xuewei Wang

Citations in the corpus

• Plant diseases and pests detection based on deep learning: a review (2021, methodology): “[63] built the PlantDoc dataset for plant disease detection.”