A mobile and cloud-based framework for plant stress detection from crowdsourced visual and infrared imagery

• Main Author: Zukowski, Daniel
• Language: EN
• Published: University of Colorado at Boulder 2016
• Subjects: Plant sciences|Computer science
• Online Access: http://pqdtopen.proquest.com/#viewpdf?dispub=10124049

<p> A cloud infrastructure and Android-based system were developed to enable amateurs and professionals to make use of laboratory techniques for remote plant disease detection. The system allows users to upload and analyze plant data as citizen scientists, helping to improve models for remote disease detection in horticultural settings by greatly increasing the quantity and diversity of data available for analysis by the community. Techniques used in research laboratories for remote disease detection are generally not available to home gardeners and small commercial farmers. Lab equipment is cost-prohibitive and experiments highly controlled, leading to models that are not necessarily transferable to the user&rsquo;s environment. Plant producers rely on expert knowledge from training, experience, and extension service professionals to accurately and reliably diagnose and quantify plant health. Techniques for disease detection using visible and infrared imagery have been proven in research studies and can now be made available to individuals due to advancements in smartphones and low-cost thermal imaging devices. The framework presented in this paper provides an internet-accessible data pipeline for image acquisition, preprocessing, stereo rectification, disparity mapping, registration, feature extraction, and machine learning, designed to support research efforts and to make plant stress detection technology readily available to the public. A system of this kind has the potential to benefit both researchers and plant growers: producers can collectively create large labeled data sets which researchers can use to build and improve detection models, returning value to growers in the form of generalizable models that work in real-world horticultural settings. We demonstrate the components of the framework and show data from a water stress experiment on basil plants performed using the mobile app and cloud-based services.</p>