GreenCrowd: A Generic Framework for Crowdsourcing Data to Detect Diseases in Grape Plants

Abstract

Acquiring comprehensive and varied datasets for training Machine Learning (ML) models poses a significant worldwide challenge, especially in areas that need intricate data, such as detecting plant diseases. This thesis introduces GreenCrowd, an innovative framework designed to address this challenge through crowdsourcing. This framework not only facilitates the collection of authentic data but also rewards contributors, thereby incentivizing participation and enhancing data quality. The project features two core components: a crowdsourcing mobile application and a sophisticated grape disease detection model. The mobile application, developed with Flutter, enables users primarily farmers to upload images of grape leaves along with descriptions of observed diseases. These images are stored in a centralized database with placeholder values for attributes such as image quality and plant verification, managed via a Flask API. The image processing pipeline in GreenCrowd involves several stages. Initially, a basic green color detection algorithm determines the presence of grape plants. Images confirmed to contain grape leaves are then evaluated for quality using an Image Quality Assessment (IQA) model. The IQA model assigns scores to the images, and a Q-learning algorithm ranks them on a scale from 1 to 5. Images that meet a predefined quality threshold are used to enhance the disease detection model through transfer learning, incorporating new disease data into the model continuously. This framework creates dynamic feedback loop, where the dataset’s quality improves through community participation, and the detection model’s accuracy is refined over time. The effectiveness of Green Crowd has been demonstrated in the context of grape leaf disease detection, highlighting its impact on both data collection and practical disease identification for end-users.