Automated recovery of 3D models of plant shoots from multiple colour images

Increased adoption of the systems approach to biological research has focussed attention on the use of quantitative models of biological objects. This includes a need for realistic 3D representations of plant shoots for quantification and modelling. Previous limitations in single or multi-view stereo algorithms have led to a reliance on volumetric methods or expensive hardware to record plant structure. We present a fully automatic approach to image-based 3D plant reconstruction that can be achieved using a single low-cost camera. The reconstructed plants are represented as a series of small planar sections that together model the more complex architecture of the leaf surfaces. The boundary of each leaf patch is refined using the level set method, optimising the model based on image information, curvature constraints and the position of neighbouring surfaces. The reconstruction process makes few assumptions about the nature of the plant material being reconstructed, and as such is applicable to a wide variety of plant species and topologies, and can be extended to canopy-scale imaging. We demonstrate the effectiveness of our approach on datasets of wheat and rice plants, as well as a novel virtual dataset that allows us to compute quantitative measures of reconstruction accuracy. The output is a 3D mesh structure that is suitable for modelling applications, in a format that can be imported in the majority of 3D graphics and software packages

Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling

Plant phenotyping is the quantitative description of a plant’s physiological, biochemical and anatomical status which can be used in trait selection and helps to provide mechanisms to link underlying genetics with yield. Here, an active vision- based pipeline is presented which aims to contribute to reducing the bottleneck associated with phenotyping of architectural traits. The pipeline provides a fully automated response to photometric data acquisition and the recovery of three-dimensional (3D) models of plants without the dependency of botanical expertise, whilst ensuring a non-intrusive and non-destructive approach. Access to complete and accurate 3D models of plants supports computation of a wide variety of structural measurements. An Active Vision Cell (AVC) consisting of a camera-mounted robot arm plus combined software interface and a novel surface reconstruction algorithm is proposed. This pipeline provides a robust, flexible and accurate method for automating the 3D reconstruction of plants. The reconstruction algorithm can reduce noise and provides a promising and extendable framework for high throughput phenotyping, improving current state-of-the-art methods. Furthermore, the pipeline can be applied to any plant species or form due to the application of an active vision framework combined with the automatic selection of key parameters for surface reconstruction

Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields

Background Convolvulus sepium (hedge bindweed) detection in sugar beet fields remains a challenging problem due to variation in appearance of plants, illumination changes, foliage occlusions, and different growth stages under field conditions. Current approaches for weed and crop recognition, segmentation and detection rely predominantly on conventional machine-learning techniques that require a large set of hand-crafted features for modelling. These might fail to generalize over different fields and environments. Results Here, we present an approach that develops a deep convolutional neural network (CNN) based on the tiny YOLOv3 architecture for C. sepium and sugar beet detection. We generated 2271 synthetic images, before combining these images with 452 field images to train the developed model. YOLO anchor box sizes were calculated from the training dataset using a k-means clustering approach. The resulting model was tested on 100 field images, showing that the combination of synthetic and original field images to train the developed model could improve the mean average precision (mAP) metric from 0.751 to 0.829 compared to using collected field images alone. We also compared the performance of the developed model with the YOLOv3 and Tiny YOLO models. The developed model achieved a better trade-off between accuracy and speed. Specifically, the average precisions ([email protected]) of C. sepium and sugar beet were 0.761 and 0.897 respectively with 6.48 ms inference time per image (800 × 1200) on a NVIDIA Titan X GPU environment

Convolutional Neural Net-Based Cassava Storage Root Counting Using Real and Synthetic Images

© Copyright © 2019 Atanbori, Montoya-P, Selvaraj, French and Pridmore. Cassava roots are complex structures comprising several distinct types of root. The number and size of the storage roots are two potential phenotypic traits reflecting crop yield and quality. Counting and measuring the size of cassava storage roots are usually done manually, or semi-automatically by first segmenting cassava root images. However, occlusion of both storage and fibrous roots makes the process both time-consuming and error-prone. While Convolutional Neural Nets have shown performance above the state-of-the-art in many image processing and analysis tasks, there are currently a limited number of Convolutional Neural Net-based methods for counting plant features. This is due to the limited availability of data, annotated by expert plant biologists, which represents all possible measurement outcomes. Existing works in this area either learn a direct image-to-count regressor model by regressing to a count value, or perform a count after segmenting the image. We, however, address the problem using a direct image-to-count prediction model. This is made possible by generating synthetic images, using a conditional Generative Adversarial Network (GAN), to provide training data for missing classes. We automatically form cassava storage root masks for any missing classes using existing ground-truth masks, and input them as a condition to our GAN model to generate synthetic root images. We combine the resulting synthetic images with real images to learn a direct image-to-count prediction model capable of counting the number of storage roots in real cassava images taken from a low cost aeroponic growth system. These models are used to develop a system that counts cassava storage roots in real images. Our system first predicts age group ('young' and 'old' roots; pertinent to our image capture regime) in a given image, and then, based on this prediction, selects an appropriate model to predict the number of storage roots. We achieve 91% accuracy on predicting ages of storage roots, and 86% and 71% overall percentage agreement on counting 'old' and 'young' storage roots respectively. Thus we are able to demonstrate that synthetically generated cassava root images can be used to supplement missing root classes, turning the counting problem into a direct image-to-count prediction task

Three Dimensional Root CT Segmentation Using Multi-Resolution Encoder-Decoder Networks

© 1992-2012 IEEE. We address the complex problem of reliably segmenting root structure from soil in X-ray Computed Tomography (CT) images. We utilise a deep learning approach, and propose a state-of-the-art multi-resolution architecture based on encoder-decoders. While previous work in encoder-decoders implies the use of multiple resolutions simply by downsampling and upsampling images, we make this process explicit, with branches of the network tasked separately with obtaining local high-resolution segmentation, and wider low-resolution contextual information. The complete network is a memory efficient implementation that is still able to resolve small root detail in large volumetric images. We compare against a number of different encoder-decoder based architectures from the literature, as well as a popular existing image analysis tool designed for root CT segmentation. We show qualitatively and quantitatively that a multi-resolution approach offers substantial accuracy improvements over a both a small receptive field size in a deep network, or a larger receptive field in a shallower network. We then further improve performance using an incremental learning approach, in which failures in the original network are used to generate harder negative training examples. Our proposed method requires no user interaction, is fully automatic, and identifies large and fine root material throughout the whole volume

Recovering Wind-induced Plant motion in Dense Field Environments via Deep Learning and Multiple Object Tracking

Understanding the relationships between local environmental conditions and plant structure and function is critical for both fundamental science and for improving the performance of crops in field settings. Wind-induced plant motion is important in most agricultural systems, yet the complexity of the field environment means that it remained understudied. Despite the ready availability of image sequences showing plant motion, the cultivation of crop plants in dense field stands makes it difficult to detect features and characterize their general movement traits. Here, we present a robust method for characterizing motion in field-grown wheat plants (Triticum aestivum) from time-ordered sequences of red, green and blue (RGB) images. A series of crops and augmentations was applied to a dataset of 290 collected and annotated images of ear tips to increase variation and resolution when training a convolutional neural network. This approach enables wheat ears to be detected in the field without the need for camera calibration or a fixed imaging position. Videos of wheat plants moving in the wind were also collected and split into their component frames. Ear tips were detected using the trained network, then tracked between frames using a probabilistic tracking algorithm to approximate movement. These data can be used to characterize key movement traits, such as periodicity, and obtain more detailed static plant properties to assess plant structure and function in the field. Automated data extraction may be possible for informing lodging models, breeding programmes and linking movement properties to canopy light distributions and dynamic light fluctuation

Intelligent Personal Assistants and the Intercultural Negotiations of Dataveillance in Platformed Households

The platformization of households is increasingly possible with the introduction of intelligent personal assistants (IPAs) embedded in smart, always-listening speakers and screens, such as Google Home and the Amazon Echo. These devices exemplify Zuboff\u27s surveillance capitalism by commodifying familial and social spaces and funneling data into corporate networks. However, the motivations driving the development of these platforms-and the dataveillance they afford-vary: Amazon appears focused on collecting user data to drive personalized sales across its shopping platform, while Google relies on its vast dataveillance infrastructure to build its Al-driven targeted advertising platform. This paper draws on cross-cultural focus groups regarding IPAs in the Netherlands and the United States. It reveals how respondents in these two countries articulate divergent ways of negotiating the dataveiLlance affordances and privacy concerns of these IPA platforms. These findings suggest the need for a nuanced approach to combating and limiting the potential harms of these home devices, which may otherwise be seen as equivalents

RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures

© The Author(s) 2019. Published by Oxford University Press. BACKGROUND: In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentation and feature extraction of plant roots from images presents a significant computer vision challenge. Root images contain complicated structures, variations in size, background, occlusion, clutter and variation in lighting conditions. We present a new image analysis approach that provides fully automatic extraction of complex root system architectures from a range of plant species in varied imaging set-ups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task convolutional neural network architecture. The network also locates seeds, first order and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. RESULTS: We develop and train a novel deep network architecture to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. The proposed method was evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. Compared with semi-automatic analysis via the original RootNav tool, the proposed method demonstrated comparable accuracy, with a 10-fold increase in speed. The network was able to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. A final instance of transfer learning, to images of Brassica napus from a hydroponic assay, still demonstrated good accuracy despite many fewer training images. CONCLUSIONS: We present RootNav 2.0, a new approach to root image analysis driven by a deep neural network. The tool can be adapted to new image domains with a reduced number of images, and offers substantial speed improvements over semi-automatic and manual approaches. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist (http://rootsystemml.github.io/), as well as segmentation masks compatible with other automated measurement tools. The tool will provide researchers with the ability to analyse root systems at larget scales than ever before, at a time when large scale genomic studies have made this more important than ever

Approaches to three-dimensional reconstruction of plant shoot topology and geometry

There are currently 805 million people classified as chronically undernourished, and yet the World’s population is still increasing. At the same time, global warming is causing more frequent and severe flooding and drought, thus destroying crops and reducing the amount of land available for agriculture. Recent studies show that without crop climate adaption, crop productivity will deteriorate. With access to 3D models of real plants it is possible to acquire detailed morphological and gross developmental data that can be used to study their ecophysiology, leading to an increase in crop yield and stability across hostile and changing environments. Here we review approaches to the reconstruction of 3D models of plant shoots from image data, consider current applications in plant and crop science, and identify remaining challenges. We conclude that although phenotyping is receiving an increasing amount of attention – particularly from computer vision researchers – and numerous vision approaches have been proposed, it still remains a highly interactive process. An automated system capable of producing 3D models of plants would significantly aid phenotyping practice, increasing accuracy and repeatability of measurements

A patch-based approach to 3D plant shoot phenotyping

The emerging discipline of plant phenomics aims to measure key plant characteristics, or traits, though as yet the set of plant traits that should be measured by automated systems is not well defined. Methods capable of recovering generic representations of the 3D structure of plant shoots from images would provide a key technology underpinning quantification of a wide range of current and future physiological and morphological traits. We present a fully automatic approach to image-based 3D plant reconstruction which represents plants as series of small planar sections that together model the complex architecture of leaf surfaces. The initial boundary of each leaf patch is refined using a level set method, optimising the model based on image information, curvature constraints and the position of neighbouring surfaces. The reconstruction process makes few assumptions about the nature of the plant material being reconstructed. As such it is applicable to a wide variety of plant species and topologies, and can be extended to canopy-scale imaging. We demonstrate the effectiveness of our approach on real images of wheat and rice plants, an artificial plant with challenging architecture, as well as a novel virtual dataset that allows us to compute distance measures of reconstruction accuracy. We also illustrate the method’s potential to support the identification of individual leaves, and so the phenotyping of plant shoots, using a spectral clustering approach