Plant Species Detection Using Image Processing and Deep Learning: A Mobile-Based Application

Eleni Mangina (), Elizabeth Burke, Ronan Matson, Rossa O’Briain, Joe M. Caffrey and Mohammad Saffari
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Eleni Mangina: School of Computer Science, University College Dublin
Elizabeth Burke: School of Computer Science, University College Dublin
Ronan Matson: Inland Fisheries Ireland
Rossa O’Briain: Inland Fisheries Ireland
Joe M. Caffrey: INVAS Biosecurity Ltd.
Mohammad Saffari: University College Dublin

A chapter in Information and Communication Technologies for Agriculture—Theme II: Data, 2022, pp 103-130 from Springer

Abstract: Abstract Conservation of biodiversity requires plant species identification skills, and automatic detection is a challenging and fascinating task for both computer/data scientists and botanists alike. This chapter describes a deep learning Convolutional Neural Network (CNN), which is trained to perform mobile imagery classification on plant species found throughout Ireland. The dataset of plant-classified RGB images underwent significant pre-processing, particularly in relation to background removal and data augmentation. Several models of deep learning CNN, with varying amounts of layers and training methods, have been evaluated on this dataset. Several deep learning models were trained and evaluated to document the speed and robustness of the flora identification. The highest performing model was then embedded in a web application, creating an online system to allow for new plant images to be uploaded and classified. This chapter highlights the main research challenges associated with this work, concludes with a mobile-based application, and discusses future research.

Keywords: Deep learning; Machine learning; Image processing; Plant species; Convolutional neural network; Artificial intelligence (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-030-84148-5_5

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