Plot Detection in Sequential Crop Field Images using Deep Learning
Date
2024-05-24
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Journal ISSN
Volume Title
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ORCID
Type
Thesis
Degree Level
Masters
Abstract
Plant phenotyping, essential for understanding plant growth and development, is hindered by its resource- intensive nature. Recent advancements in non-destructive imaging techniques, particularly remote sensing and proximal sensing, have revolutionized plant phenotyping by enabling high-throughput data collection. However, challenges persist such as the laborious preprocessing demands. This thesis addresses the pre- processing challenge, particularly of selecting centered plot images from overlapping sequential plot images. Leveraging two deep-learning models, an image classification model (ICM) and a video summarization model (VSM), we show the potential of such models to automate this selection process. ICM approaches the task as a classification problem, while VSM adopts a video summary perspective to extract key frames representing centered plots. Both ICM and VSM, trained on a dataset comprising 78,420 canola plot images spanning two different years achieve a notable F1 score of 0.81. Our findings also reveal the robustness of these models to common data collection anomalies, including repeated images and a shuffled dataset where the sequential nature of the images is disrupted. This resilience underscores the practical utility of the models in addressing challenges inherent in real-world plant phenotyping scenarios. Additionally, to streamline the image selection process, we introduce PlotReel, a web application that allows quick sequential crop-field image selection. By predicting and seamlessly navigating to subsequent centered plot/row images based on regular spacing and constant camera speed, PlotReel simplifies the image selection process, saving users from manually sorting through overlapping and redundant images.
Description
Keywords
Deep learning, Plant Phenotyping, CNN, Video summary
Citation
Degree
Master of Science (M.Sc.)
Department
Computer Science
Program
Computer Science