PlantDoc

2020/01/05 by Davinder Singh, Naman Jain, Pranjali Jain +3 · 9 citations
Agricultural and Biological Sciences · Environmental Science · #Plant Disease Management Techniques #Remote Sensing in Agriculture #Smart Agriculture and AI

paper · doi:10.1145/3371158.3371196

openalex created_date 2019/12/05 · openalex publication_date 2020/01/05 · openalex updated_date 2026/07/30

Abstract

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.

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• ImageNet classification with deep convolutional neural networks
• Plant Disease: A Threat to Global Food Security
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Learning Multiple Layers of Features from Tiny Images

Cited by

• BioCAP: Exploiting Synthetic Captions Beyond Labels in Biological Foundation Models
• Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition
• LeafDNet: Transforming Leaf Disease Diagnosis Through Deep Transfer Learning
• PlantExpertVQA: A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science
• Plant diseases and pests detection based on deep learning: a review
• Mobile-Friendly Deep Learning for Plant Disease Detection: A Lightweight CNN Benchmark Across 101 Classes of 33 Crops
• Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting
• AgroBench: Vision-Language Model Benchmark in Agriculture
• Multi-output Deep-Supervised Classifier Chains for Plant Pathology

Related

• Plant Disease Detection Using Image Processing and Machine Learning
• Using Deep Learning for Image-Based Plant Disease Detection
• Plant Disease Recognition Datasets in the Age of Deep Learning: Challenges and Opportunities
• Leaf Image-based Plant Disease Identification using Color and Texture Features
• PlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease Segmentation