Image-based Plant Phenotyping Research Articles

AraDQ: an automated digital phenotyping software for quantifying disease symptoms of flood-inoculated Arabidopsis seedlings

BackgroundPlant scientists have largely relied on pathogen growth assays and/or transcript analysis of stress-responsive genes for quantification of disease severity and susceptibility. These methods are destructive to plants, labor-intensive, and time-consuming, thereby limiting their application in real-time, large-scale studies. Image-based plant phenotyping is an alternative approach that enables automated measurement of various symptoms. However, most of the currently available plant image analysis tools require specific hardware platform and vendor specific software packages, and thus, are not suited for researchers who are not primarily focused on plant phenotyping. In this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis.ResultsHere, we present the Arabidopsis Disease Quantification (AraDQ) image analysis tool for examination of flood-inoculated Arabidopsis seedlings grown on plates containing plant growth media. It is a cross-platform application program with a user-friendly graphical interface that contains highly accurate deep neural networks for object detection and segmentation. The only prerequisite is that the input image should contain a fixed-sized 24-color balance card placed next to the objects of interest on a white background to ensure reliable and reproducible results, regardless of the image acquisition method. The image processing pipeline automatically calculates 10 different colors and morphological parameters for individual seedlings in the given image, and disease-associated phenotypic changes can be easily assessed by comparing plant images captured before and after infection. We conducted two case studies involving bacterial and plant mutants with reduced virulence and disease resistance capabilities, respectively, and thereby demonstrated that AraDQ can capture subtle changes in plant color and morphology with a high level of sensitivity.ConclusionsAraDQ offers a simple, fast, and accurate approach for image-based quantification of plant disease symptoms using various parameters. Its fully automated pipeline neither requires prior image processing nor costly hardware setups, allowing easy implementation of the software by researchers interested in digital phenotyping of diseased plants.

Deep Learning in Image-Based Plant Phenotyping.

A major bottleneck in the crop improvement pipeline is our ability to phenotype crops quickly and efficiently. Image-based, high-throughput phenotyping has a number of advantages because it is nondestructive and reduces human labor, but a new challenge arises in extracting meaningful information from large quantities of image data. Deep learning, a type of artificial intelligence, is an approach used to analyze image data and make predictions on unseen images that ultimately reduces the need for human input in computation. Here, we review the basics of deep learning, assessment of deep learning success, examples of applications of deep learning in plant phenomics, best practices, and open challenges. Expected final online publication date for the Annual Review of Plant Biology, Volume 75 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Multi-growth stage plant recognition: A case study of Palmer amaranth (Amaranthus palmeri) in cotton (Gossypium hirsutum)

Many advanced image-based precision agricultural technologies for plant breeding, field crop research, and site-specific crop management hinge on the reliable detection and phenotyping of plants across highly variable morphological growth stages. Convolutional neural networks (CNNs) have shown promise for image-based plant phenotyping and weed recognition, but the ability to recognize growth stages, often with stark differences in appearance, is uncertain. Palmer amaranth (Amaranthus palmeri) is a particularly challenging weed plant in cotton (Gossypium hirsutum) production; due to high genetic diversity, it exhibits highly variable plant morphology across growth stages over a growing season, as well as between plants at a given growth stage. This paper investigates eight-class growth stage recognition of A. palmeri in cotton as a challenging detection case study for You Only Look Once (YOLO) architectures. In total, 26 architecture variants from YOLO v3, v5, v6, v6 3.0, v7, and v8 are compared on an eight-class growth stage dataset of A. palmeri. The highest mean average precision (mAP@[0.5:0.95]) for recognition of all growth stage classes was 47.34% achieved by v8-X, with inter-class confusion across visually similar classes. With all growth stages grouped as a single class, performance increased, with a maximum mAP@[0.5:0.95] of 67.05% achieved by v7-Original. Single-class recall of up to 81.42% was achieved by v5-X, and precision of up to 89.72% was achieved by v8-X. Class activation maps (CAM) were used to understand model attention on the complex dataset. Fewer classes, grouped by visual or size features improved performance over the ground-truth eight-class dataset. Successful growth stage detection highlights the substantial opportunity for improving plant phenotyping and weed recognition technologies with open-source object detection architectures. Additionally, the first open-access, benchmarking growth stage dataset ‘Palmer amaranth growth stages – 8 (PAGS8)’ is presented, which is available here: https://weed-ai.sydney.edu.au/datasets/5c78d067-8750-4803-9cbe-57df8fae55e4.

Trichomes affect grapevine leaf optical properties and thermoregulation

In the context of climate change, the optical properties of grapevine leaves have been used in imaging technologies to screen grapevine phenotypes tolerant to water deficit and heat waves in field conditions. Image-based plant phenotyping is challenging because the adaxial and the abaxial sides of the leaf present different morphology. This study investigated the effect of trichomes of the abaxial epidermis on the spectral responses in the two grapevine leaf sides. It was also examined the effect of pubescence on leaf thermoregulation, either during water deficiency or not. For this study, 99 Vitis spp. genotypes were categorised for their prostrate trichome density between main veins on the abaxial side of the leaves (using the descriptor OIV 084). In the first week of 2022, August, reflectance spectra from 400 to 700 nm and color indexes CIELAB and RGB were recorded in five leaves (both abaxial and adaxial sides) per genotype. During three days in midsummer, crop water stress index (CWSI) was also determined in each genotype. The abaxial leaf side was more reflective than the adaxial one in all visible color bands. Values of CIELAB (except for component a*) and RGB were higher for the abaxial leaf side than the adaxial one. The different spectral and color responses between leaf sides positively correlated with the descriptor OIV 084. Trichomes, which mainly occur on the abaxial side in grapevine, significantly contribute to the different optical properties between the two leaf sides. Correlation between OIV 084 and CWSI values was significantly positive (p < 0.05) only when vines were under drought, indicating that trichomes rise leaf temperature by probably reducing evaporative cooling under dry conditions. Therefore, pubescence could be a promising trait to consider when selecting varieties for drought tolerance.

Monitoring of plant growth through methods of phenotyping and image analysis

With the rapid development of imaging technology, computing power, and algorithms, computer vision has revolutionized thoroughly plant phenotyping and is now a major tool for phenotypic analysis. Those reasons constructed the base for developing image-based plant phenotyping methods, it is a priority for the complementary or even alternative to the manual measurement. Nonetheless, the use of computer vision technology to analyze plant phenotypic traits can be affected by a lot of factors such as research environment, imaging system, and model selection. The field of plant phenotyping is developing rapidly at the moment. Image-based plant phenotyping has stated proven to be in precision agriculture, providing a quantitative basis for the description of plant-environment interactions.

Estimation of rice seedling growth traits with an end-to-end multi-objective deep learning framework.

In recent years, rice seedling raising factories have gradually been promoted in China. The seedlings bred in the factory need to be selected manually and then transplanted to the field. Growth-related traits such as height and biomass are important indicators for quantifying the growth of rice seedlings. Nowadays, the development of image-based plant phenotyping has received increasing attention, however, there is still room for improvement in plant phenotyping methods to meet the demand for rapid, robust and low-cost extraction of phenotypic measurements from images in environmentally-controlled plant factories. In this study, a method based on convolutional neural networks (CNNs) and digital images was applied to estimate the growth of rice seedlings in a controlled environment. Specifically, an end-to-end framework consisting of hybrid CNNs took color images, scaling factor and image acquisition distance as input and directly predicted the shoot height (SH) and shoot fresh weight (SFW) after image segmentation. The results on the rice seedlings dataset collected by different optical sensors demonstrated that the proposed model outperformed compared random forest (RF) and regression CNN models (RCNN). The model achieved R2 values of 0.980 and 0.717, and normalized root mean square error (NRMSE) values of 2.64% and 17.23%, respectively. The hybrid CNNs method can learn the relationship between digital images and seedling growth traits, promising to provide a convenient and flexible estimation tool for the non-destructive monitoring of seedling growth in controlled environments.

Benchmarking Self-Supervised Contrastive Learning Methods for Image-Based Plant Phenotyping.

The rise of self-supervised learning (SSL) methods in recent years presents an opportunity to leverage unlabeled and domain-specific datasets generated by image-based plant phenotyping platforms to accelerate plant breeding programs. Despite the surge of research on SSL, there has been a scarcity of research exploring the applications of SSL to image-based plant phenotyping tasks, particularly detection and counting tasks. We address this gap by benchmarking the performance of 2 SSL methods-momentum contrast (MoCo) v2 and dense contrastive learning (DenseCL)-against the conventional supervised learning method when transferring learned representations to 4 downstream (target) image-based plant phenotyping tasks: wheat head detection, plant instance detection, wheat spikelet counting, and leaf counting. We studied the effects of the domain of the pretraining (source) dataset on the downstream performance and the influence of redundancy in the pretraining dataset on the quality of learned representations. We also analyzed the similarity of the internal representations learned via the different pretraining methods. We find that supervised pretraining generally outperforms self-supervised pretraining and show that MoCo v2 and DenseCL learn different high-level representations compared to the supervised method. We also find that using a diverse source dataset in the same domain as or a similar domain to the target dataset maximizes performance in the downstream task. Finally, our results show that SSL methods may be more sensitive to redundancy in the pretraining dataset than the supervised pretraining method. We hope that this benchmark/evaluation study will guide practitioners in developing better SSL methods for image-based plant phenotyping.

Patentometric review on automated plant phenotyping

Plant phenotyping involves the measurement of observable traits of plants. Plant traits like leaf area, leaf count, leaf surface temperature, chlorophyll content, plant growth rate, emergence time of leaves, and reproductive organs depend on the interaction of its genotype with the environment. Plant phenotyping serves to analyze biotic and abiotic stresses on plants, select crop varieties resilient to the surrounding environment, and improve crop yield. Recent advancements in imaging technologies help expedite the growth of automatic, non-invasive, and efficient plant phenotyping systems. These plant phenotyping systems involve using different imaging techniques like visible imaging, hyperspectral imaging, chlorophyll fluorescence imaging (CFIM), thermal imaging to record, monitor, and analyze plant phenotypes using images. In the last few years, researchers have been working on developing image processing, computer vision, machine learning, and deep learning approaches for the accurate and precise analysis of plant images. Therefore, this paper reviews and presents insights about the research reported through patents in the area of automatic plant phenotyping. This review report uses patent databases like Espacenet, Lens, and Google Patents to search, review and analyze patent documents. The paper presents a patentometric analysis of all 67 patent documents available till date focusing on automatic image-based plant phenotyping. The review provides a summary and analysis of outstanding patents in terms of qualitative and quantitative patent indices. This article provides a comprehensive global patent study to aid researchers and scientists develop more efficient plant phenotyping algorithms, devices, and systems.

ThelR547v1-An Asymmetric Dilated Convolutional Neural Network for Real-time Semantic Segmentation of Horticultural Crops.

Robust and automated image segmentation in high-throughput image-based plant phenotyping has received considerable attention in the last decade. The possibility of this approach has not been well studied due to the time-consuming manual segmentation and lack of appropriate datasets. Segmenting images of greenhouse and open-field grown crops from the background is a challenging task linked to various factors such as complex background (presence of humans, equipment, devices, and machinery for crop management practices), environmental conditions (humidity, cloudy/sunny, fog, rain), occlusion, low-contrast and variability in crops and pose over time. This paper presents a new ubiquitous deep learning architecture ThelR547v1 (Thermal RGB 547 layers version 1) that segmented each pixel as crop or crop canopy from the background (non-crop) in real time by abstracting multi-scale contextual information with reduced memory cost. By evaluating over 37,328 augmented images (aug1: thermal RGB and RGB), our method achieves mean IoU of 0.94 and 0.87 for leaves and background and mean Bf scores of 0.93 and 0.86, respectively. ThelR547v1 has a training accuracy of 96.27%, a training loss of 0.09, a validation accuracy of 96.15%, and a validation loss of 0.10. Qualitative analysis further shows that despite the low resolution of training data, ThelR547v1 successfully distinguishes leaf/canopy pixels from complex and noisy background pixels, enabling it to be used for real-time semantic segmentation of horticultural crops.

Self-Supervised Leaf Segmentation under Complex Lighting Conditions

As an essential prerequisite task in image-based plant phenotyping, leaf segmentation has garnered increasing attention in recent years. While self-supervised learning is emerging as an effective alternative to various computer vision tasks, its adaptation for image-based plant phenotyping remains rather unexplored. In this work, we present a self-supervised leaf segmentation framework consisting of a self-supervised semantic segmentation model, a color-based leaf segmentation algorithm, and a self-supervised color correction model. The self-supervised semantic segmentation model groups the semantically similar pixels by iteratively referring to the self-contained information, allowing the pixels of the same semantic object to be jointly considered by the color-based leaf segmentation algorithm for identifying the leaf regions. Additionally, we propose to use a self-supervised color correction model for images taken under complex illumination conditions. Experimental results on datasets of different plant species demonstrate the potential of the proposed self-supervised framework in achieving effective and generalizable leaf segmentation.

High-throughput phenotyping to detect anthocyanins, chlorophylls, and carotenoids in red lettuce germplasm

Some plant pigments are strong antioxidants and benefit human health. Given their low cost and popularity, biofortified lettuce cultivars can promote consumption of these compounds. In this study, we assessed anthocyanin, chlorophyll, and carotenoid content in 30 red lettuce genotypes, as well as the Soil Plant Analysis Development index (SPAD) and vegetation indices (CIG, CVI, GNDVI, and NDVI), using high-throughput phenotyping. We calculated vegetation indices by using images of the plant canopy obtained from a remotely piloted aircraft (RPA) together with a red, green, and near-infrared band camera. The SPAD index and leaf pigments were measured through traditional laboratory methods, which are costly and time-consuming. Genetic variability among the genotypes for leaf pigments was confirmed by the Scott-Knott test and UPGMA dendrogram. Anthocyanin was the pigment that most contributed to genetic diversity and highly correlated with CIG, CVI, and GNDVI. These indices also moderately correlated with chlorophylls and carotenoids. Genotypes with higher pigment content, especially anthocyanin, had higher indices. Therefore, the image-based plant phenotyping technique correlated with the traditional methodology and can provide an alternative for indirect selection of plants with high leaf pigment content, especially anthocyanin, in red lettuce.

Convolutional neural network based encoder-decoder architectures for semantic segmentation of plants

Recent advancements in the area of computer vision-based plant phenotyping are playing an important role, in determining the quantitative phenotypes of plants, and crop yield. Automatic segmentation of plants and its associated structures is the first and most important step in image based plant phenotyping. We design and implement convolutional neural network (CNN) based modified residual U-Net for semantic segmentation of plants from the background. We also use SegNet and U-Net architectures for comparison purpose. In this paper, residual U-Net, SegNet and U-Net models are tested on leaf segmentation challenge (LSC) dataset and fig dataset that are publicly available. LSC dataset consists of images of Arabidopsis and tobacco plants grown under controlled conditions whereas fig dataset includes top view images of fig plants captured in open-field conditions. We have used 8 evaluation metrics for analyzing and comparing the performance of residual U-Net, SegNet and U-Net architectures with the existing algorithms in the literature. Residual U-Net with 15.32 million trainable parameters, outperforms SegNet and other state-of-the-art methods whereas achieves comparable performance with respect to U-Net. Residual U-Net achieves dice coefficient of 0.9709 on LSC dataset and 0.9665 on fig dataset, respectively. The segmentation networks used in this paper can be used for other plant related applications such as plant trait estimation or in quantification of plant stress.

ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture.

BackgroundDeep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence approaches have been hampered by the inaccessibility of the rhizosphere. Here we present ChronoRoot, a system that combines 3D-printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium.ResultsWe developed a novel deep learning–based root extraction method that leverages the latest advances in convolutional neural networks for image segmentation and incorporates temporal consistency into the root system architecture reconstruction process. Automatic extraction of phenotypic parameters from sequences of images allowed a comprehensive characterization of the root system growth dynamics. Furthermore, novel time-associated parameters emerged from the analysis of spectral features derived from temporal signals.ConclusionsOur work shows that the combination of machine intelligence methods and a 3D-printed device expands the possibilities of root high-throughput phenotyping for genetics and natural variation studies, as well as the screening of clock-related mutants, revealing novel root traits.

Spatial Super Resolution of Real-World Aerial Images for Image-Based Plant Phenotyping

Unmanned aerial vehicle (UAV) imaging is a promising data acquisition technique for image-based plant phenotyping. However, UAV images have a lower spatial resolution than similarly equipped in field ground-based vehicle systems, such as carts, because of their distance from the crop canopy, which can be particularly problematic for measuring small-sized plant features. In this study, the performance of three deep learning-based super resolution models, employed as a pre-processing tool to enhance the spatial resolution of low resolution images of three different kinds of crops were evaluated. To train a super resolution model, aerial images employing two separate sensors co-mounted on a UAV flown over lentil, wheat and canola breeding trials were collected. A software workflow to pre-process and align real-world low resolution and high-resolution images and use them as inputs and targets for training super resolution models was created. To demonstrate the effectiveness of real-world images, three different experiments employing synthetic images, manually downsampled high resolution images, or real-world low resolution images as input to the models were conducted. The performance of the super resolution models demonstrates that the models trained with synthetic images cannot generalize to real-world images and fail to reproduce comparable images with the targets. However, the same models trained with real-world datasets can reconstruct higher-fidelity outputs, which are better suited for measuring plant phenotypes.

Spatio-temporal deep neural networks for accession classification of Arabidopsis plants using image sequences

Recently, image based plant phenotyping is used extensively for plant trait estimation, plant accession classification and selection, and plant stress analysis. Plant accession classification and selection helps in identification of plants tolerant to local climatic conditions. Over the past few years, convolutional neural network (CNN) models were predominantly used for classification of plants and plant diseases from static plant images using spatial features. In this paper, we introduce three different methods namely 3-dimensional (3-D) CNN, CNN with convolutional long short-term memory (ConvLSTM) layers and vision transformer that use temporal information along with spatial information for plant accession classification. We have used publicly available Arabidopsis plant accession classification dataset to test the performance of these methods. Time series color images of four different plant accessions of Arabidopsis are given as input to the neural network model which in turn identifies plant accession. All the three methods outperform the existing methods available in the literature in terms of average accession classification accuracy. Vision transformer achieves highest classification accuracy of 98.59% at the cost of very large number of trainable parameters as compared to the other two methods. On the other hand, CNN-ConvLSTM achieves comparable accuracy of 97.97% with very less trainable parameters as compared to vision transformer. In future, these models can also be used to identify unknown plant accessions and predict plant growth signatures in different climatic conditions.

Visual Growth Tracking for Automated Leaf Stage Monitoring Based on Image Sequence Analysis

In this paper, we define a new problem domain, called visual growth tracking, to track different parts of an object that grow non-uniformly over space and time for application in image-based plant phenotyping. The paper introduces a novel method to reliably detect and track individual leaves of a maize plant based on a graph theoretic approach for automated leaf stage monitoring. The method has four phases: optimal view selection, plant architecture determination, leaf tracking, and generation of a leaf status report. The method accepts an image sequence of a plant as the input and automatically generates a leaf status report containing the phenotypes, which are crucial in the understanding of a plant’s growth, i.e., the emergence timing of each leaf, total number of leaves present at any time, the day on which a particular leaf ceased to grow, and the length and relative growth rate of individual leaves. Based on experimental study, three types of leaf intersections are identified, i.e., tip-contact, tangential-contact, and crossover, which pose challenges to accurate leaf tracking in the late vegetative stage. Thus, we introduce a novel curve tracing approach based on an angular consistency check to address the challenges due to intersecting leaves for improved performance. The proposed method shows high accuracy in detecting leaves and tracking them through the vegetative stages of maize plants based on experimental evaluation on a publicly available benchmark dataset.

Automated Machine Learning for High-Throughput Image-Based Plant Phenotyping

Automated machine learning (AutoML) has been heralded as the next wave in artificial intelligence with its promise to deliver high-performance end-to-end machine learning pipelines with minimal effort from the user. However, despite AutoML showing great promise for computer vision tasks, to the best of our knowledge, no study has used AutoML for image-based plant phenotyping. To address this gap in knowledge, we examined the application of AutoML for image-based plant phenotyping using wheat lodging assessment with unmanned aerial vehicle (UAV) imagery as an example. The performance of an open-source AutoML framework, AutoKeras, in image classification and regression tasks was compared to transfer learning using modern convolutional neural network (CNN) architectures. For image classification, which classified plot images as lodged or non-lodged, transfer learning with Xception and DenseNet-201 achieved the best classification accuracy of 93.2%, whereas AutoKeras had a 92.4% accuracy. For image regression, which predicted lodging scores from plot images, transfer learning with DenseNet-201 had the best performance (R2 = 0.8303, root mean-squared error (RMSE) = 9.55, mean absolute error (MAE) = 7.03, mean absolute percentage error (MAPE) = 12.54%), followed closely by AutoKeras (R2 = 0.8273, RMSE = 10.65, MAE = 8.24, MAPE = 13.87%). In both tasks, AutoKeras models had up to 40-fold faster inference times compared to the pretrained CNNs. AutoML has significant potential to enhance plant phenotyping capabilities applicable in crop breeding and precision agriculture.

Leveraging Image Analysis to Compute 3D Plant Phenotypes Based on Voxel-Grid Plant Reconstruction.

High throughput image-based plant phenotyping facilitates the extraction of morphological and biophysical traits of a large number of plants non-invasively in a relatively short time. It facilitates the computation of advanced phenotypes by considering the plant as a single object (holistic phenotypes) or its components, i.e., leaves and the stem (component phenotypes). The architectural complexity of plants increases over time due to variations in self-occlusions and phyllotaxy, i.e., arrangements of leaves around the stem. One of the central challenges to computing phenotypes from 2-dimensional (2D) single view images of plants, especially at the advanced vegetative stage in presence of self-occluding leaves, is that the information captured in 2D images is incomplete, and hence, the computed phenotypes are inaccurate. We introduce a novel algorithm to compute 3-dimensional (3D) plant phenotypes from multiview images using voxel-grid reconstruction of the plant (3DPhenoMV). The paper also presents a novel method to reliably detect and separate the individual leaves and the stem from the 3D voxel-grid of the plant using voxel overlapping consistency check and point cloud clustering techniques. To evaluate the performance of the proposed algorithm, we introduce the University of Nebraska-Lincoln 3D Plant Phenotyping Dataset (UNL-3DPPD). A generic taxonomy of 3D image-based plant phenotypes are also presented to promote 3D plant phenotyping research. A subset of these phenotypes are computed using computer vision algorithms with discussion of their significance in the context of plant science. The central contributions of the paper are (a) an algorithm for 3D voxel-grid reconstruction of maize plants at the advanced vegetative stages using images from multiple 2D views; (b) a generic taxonomy of 3D image-based plant phenotypes and a public benchmark dataset, i.e., UNL-3DPPD, to promote the development of 3D image-based plant phenotyping research; and (c) novel voxel overlapping consistency check and point cloud clustering techniques to detect and isolate individual leaves and stem of the maize plants to compute the component phenotypes. Detailed experimental analyses demonstrate the efficacy of the proposed method, and also show the potential of 3D phenotypes to explain the morphological characteristics of plants regulated by genetic and environmental interactions.

A deep learning approach to measure stress level in plants due to Nitrogen deficiency

Stress due to nutrients deficiency in plants can reduce the agricultural yield significantly. Nitrogen, an essential nutrient, is a crucial growth-limiting factor and is the prime component of amino acids, proteins, nucleic acids, and chlorophyll. Nitrogen deficiency affects certain visible plant traits such as area, color, the number of leaves and plant height, etc. With the recent advancements in imaging technology, computer vision-based plant phenomics has become a promising field of plant research and management. Such imaging-based techniques are non-destructive and much faster with higher levels of automation. In this work, we have proposed an automatic image-based plant phenotyping approach for stress classification in plant shoot images. In this proposed phenotyping approach, a 23-layered deep learning technique is proposed and compared with traditional Machine Learning techniques and few other deep architectures. Results reveal that a simple 23-layered deep learning architecture is comparable to the established state of art deep learning architectures like ResNet18 and NasNet Large (having millions of trainable parameters) in yielding ceiling level stress classification from plant shoot images. In addition, the proposed model also outperforms traditional Machine Learning techniques by achieving an average of 8.25% better accuracy.

Greenotyper: Image-Based Plant Phenotyping Using Distributed Computing and Deep Learning.

Image-based phenotype data with high temporal resolution offers advantages over end-point measurements in plant quantitative genetics experiments, because growth dynamics can be assessed and analysed for genotype-phenotype association. Recently, network-based camera systems have been deployed as customizable, low-cost phenotyping solutions. Here, we implemented a large, automated image-capture system based on distributed computing using 180 networked Raspberry Pi units that could simultaneously monitor 1,800 white clover (Trifolium repens) plants. The camera system proved stable with an average uptime of 96% across all 180 cameras. For analysis of the captured images, we developed the Greenotyper image analysis pipeline. It detected the location of the plants with a bounding box accuracy of 97.98%, and the U-net-based plant segmentation had an intersection over union accuracy of 0.84 and a pixel accuracy of 0.95. We used Greenotyper to analyze a total of 355,027 images, which required 24–36 h. Automated phenotyping using a large number of static cameras and plants thus proved a cost-effective alternative to systems relying on conveyor belts or mobile cameras.