Wei Guo

• PhD
• Professor (Associate) at The University of Tokyo

Effective and efficient segmentation of vegetation from digital plant images is an actively studied topic in crop phenotyping. Many of the formerly proposed methods showed good performance in the extraction under controlled light conditions but it is still hard to properly extract only vegetation from RGB images taken under natural light condition...

Flowering (spikelet anthesis) is one of the most important phenotypic characteristics of paddy rice, and researchers expend efforts to observe flowering timing. Observing flowering is very time-consuming and labor-intensive, because it is still visually performed by humans. An image-based method that automatically detects the flowering of paddy ric...

Developing computer vision-based rice phenotyping techniques is crucial for precision field management and accelerating breeding, thereby continuously advancing rice production. Among phenotyping tasks, distinguishing image components is a key prerequisite for characterizing plant growth and development at the organ scale, enabling deeper insights...

Computer vision is increasingly used in farmers' fields and agricultural experiments to quantify important traits related to crop performance. In particular, imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of plant features, including size, shape and color. While today's AI-driven foundation models se...

Species-level crop and weed semantic segmentation in agricultural field images enables plant identification and enhanced precision weed management. However, the scarcity of labeled data poses significant challenges for model development. Here, we report a patch-level synthetic data generation pipeline that improves semantic segmentation performance...

The increase in the global population is leading to a doubling of the demand for protein. Soybean (Glycine max), a key contributor to global plant-based protein supplies, requires ongoing yield enhancements to keep pace with increasing demand. Precise, on-plant seed counting and localization may catalyze breeding selection of shoot architectures an...

Accurate image segmentation is essential for image-based estimation of vegetation canopy traits, as it minimizes background interference. However, existing segmentation models often lack the generalization ability to effectively tackle both ground-based and aerial images across a wide range of spatial resolutions. To address this limitation, a cros...

Wheat is a crucial crop worldwide, and accurate detection and counting of wheat spikes are vital for yield estimation and breeding. However, these tasks are daunting in complex field environments. To tackle this, we introduce RIA-SpikeNet, a model designed to detect and count wheat spikes in such conditions. First, we introduce an Implicit Decoupli...

An open-source software for field-based plant phenotyping, Precision Plots Analyzer (PREPs), was developed using Window.NET. The software runs on 64-bit Windows computers. This software allows the extraction of phenotypic traits on a per-microplot basis from orthomosaic and digital surface model (DSM) images generated by Structure-from-Motion/Multi...

Potato yield is an important metric for farmers to further optimize their cultivation practices. Potato yield can be estimated on a harvester using an RGB-D camera that can estimate the three-dimensional (3D) volume of individual potato tubers. A challenge, however, is that the 3D shape derived from RGB-D images is only partially completed, underes...

Data-driven techniques could be used to enhance decision-making capacity of breeders and farmers. We used an RGB camera on an unmanned aerial vehicle (UAV) to collect time series data on sugar beet canopy coverage (CC) and canopy height (CH) from small-plot breeding fields including 20 genotypes per season over 3 seasons. Digital orthomosaic and di...

This article addresses the challenges of measuring the 3D architecture traits, such as height and volume, of fruit tree canopies, constituting information that is essential for assessing tree growth and informing orchard management. The traditional methods are time-consuming, prompting the need for efficient alternatives. Recent advancements in unm...

Weed is a major biological factor causing declines in crop yield. However, widespread herbicide application and indiscriminate weeding with soil disturbance are of great concern because of their environmental impacts. Site-specific weed management (SSWM) refers to a weed management strategy for digital agriculture that results in low energy loss. D...

The shape of a fruit is one of the main characteristics of fruit morphology, and its quantitative description is crucial to improve fruit quality. However, the shape of a pear fruit is relatively complex, and difficult to describe comprehensively. This study aimed to quantitatively analyze the morphological diversity of 2D fruit shapes of 276 culti...

Recent years have seen the development of novel, rapid, and inexpensive techniques for collecting plant data to monitor the nutritional status of crops. These techniques include hyperspectral imaging, which has been widely used in combination with machine learning models to predict element concentrations in plants. When there are multiple elements,...

Recently, deep learning-based fruit detection applications have been widely used in the modern fruit industry; however, the training data labeling process remains a time-consuming and labor-intensive process. Auto labeling can provide a convenient and efficient data source for constructing smart orchards based on deep-learning technology. In our pr...

Traditional agriculture is gradually being combined with artificial intelligence technology. High-performance fruit detection technology is an important basic technology in the practical application of modern smart orchards and has great application value. At this stage, fruit detection models need to rely on a large number of labeled datasets to s...

Photogrammetry techniques (3D reconstruction) have been commonly used in applications of high-throughput plant phenotyping. However, some intermediate processing still requires manual operation, which is often time-consuming and labor-intensive. For example, only some Regions of Interest (ROIs) rather than the entire plot or field are worth analyzi...

The rapid urbanization observed in Asian tropics has resulted in extensive landscape transformations, giving rise to novel challenges such as conflicts of interest among citizens and threats to biodiversity. To facilitate informed urban management policies, there is a pressing need for contemporary land use and cover maps that provide precise insig...

The use of unmanned aerial vehicles (UAVs) has facilitated crop canopy monitoring, enabling yield prediction by integrating regression models. However, the application of UAV-based data to individual-level harvest weight prediction is limited by the effectiveness of obtaining individual features. In this study, we propose a method that automaticall...

On-farm food loss (i.e., grade-out vegetables) is a difficult challenge in sustainable agricultural systems. The simplest method to reduce the number of grade-out vegetables is to monitor and predict the size of all individuals in the vegetable field and determine the optimal harvest date with the smallest grade-out number and highest profit, which...

Although deep learning-based fruit detection techniques are becoming popular, they require a large number of labeled datasets to support model training. Moreover, the manual labeling process is time-consuming and labor-intensive. We previously implemented a generative adversarial network-based method to reduce labeling costs. However, it does not c...

The green fraction (GF), which is the fraction of green vegetation in a given viewing direction, is closely related to the light interception ability of the crop canopy. Monitoring the dynamics of GF is therefore of great interest for breeders to identify genotypes with high radiation use efficiency. The accuracy of GF estimation depends heavily on...

Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems. Data competitions have a rich history in plant phenotyping, and new outdoor field datasets have the potential to embrace solutions across research and commercial applications. We developed the Global Wheat Cha...

Detailed observation of the phenotypic changes in rice panicle substantially helps us to understand the yield formation. In present studies, phenotyping of rice panicles during the heading-flowering stage still lacks comprehensive analysis, especially of panicle development under different nitrogen treatments. In this work, we proposed a pipeline t...

Applying deep learning to images of cropping systems provides new knowledge and insights in research and commercial applications. Semantic segmentation or pixel-wise classification, of RGB images acquired at the ground level, into vegetation and background is a critical step in the estimation of several canopy traits. Current state of the art metho...

The leaf phenotypic traits of plants have a significant impact on the efficiency of canopy photosynthesis. However, traditional methods such as destructive sampling will hinder the continuous monitoring of plant growth, while manual measurements in the field are both time-consuming and laborious. Nondestructive and accurate measurements of leaf phe...

This paper discusses the use of the terrestrial multi-frame photogrammetry method. This method is applied to a group of plants in a natural environment of a greenhouse. Image data is captured by two sensing devices with different types of lenses. Image data is used for the realistic 3D reconstruction of objects into a 3D model. In this experiment,...

The number of leaves at a given time is important to characterize plant growth and development. In this work, we developed a high-throughput method to count the number of leaves by detecting leaf tips in RGB images. The digital plant phenotyping platform was used to simulate a large and diverse dataset of RGB images and corresponding leaf tip label...

Maize is the world's most produced cereal crop, and the selection of maize cultivars with a high stem elastic modulus is an effective method to prevent cereal crop lodging. We developed an ultra-compact sensor array inspired by earthquake engineering and proposed a method for the high-throughput evaluation of the elastic modulus of maize cultivars....

Developing automated soybean seed counting tools will help automate yield prediction before harvesting and improving selection efficiency in breeding programs. An integrated approach for counting and localization is ideal for subsequent analysis. The traditional method of object counting is labor-intensive and error-prone and has low localization a...

This article aims to compare two different scanning devices (360 camera and digital single lens reflex (DSLR) camera) and their properties in the three-dimensional (3D) reconstruction of the object by the photogrammetry method. The article first describes the various stages of the process of 3D modeling and reconstruction of the object. A point clo...

Calculating the complex 3D traits of trees such as branch structure using drones/unmanned aerial vehicles (UAVs) with onboard RGB cameras is challenging because extracting branch skeletons from such image-generated sparse point clouds remains difficult. This paper proposes a skeleton extraction algorithm for the sparse point cloud generated by UAV...

Head (panicle) density is a major component in understanding crop yield, especially in crops that produce variable numbers of tillers such as sorghum and wheat. Use of panicle density both in plant breeding and in the agronomy scouting of commercial crops typically relies on manual counts observation, which is an inefficient and tedious process. Be...

In modern smart orchards, fruit detection models based on deep learning require expensive dataset labeling work to support the construction of detection models, resulting in high model application costs. Our previous work combined generative adversarial networks (GANs) and pseudolabeling methods to transfer labels from one specie to another to save...

Deep learning is widely used in modern orchard production for various inspection missions, which helps improve the efficiency of orchard operations. In the mission of visual detection during fruit picking, most current lightweight detection models are not yet effective enough to detect multi-type occlusion targets, severely affecting automated frui...

The increase in the number of tillers of rice significantly affects grain yield. However, this is measured only by the manual counting of emerging tillers, where the most common method is to count by hand touching. This study develops an efficient, non-destructive method for estimating the number of tillers during the vegetative and reproductive st...

Understanding of pollination systems is an important topic for evolutionary ecology, food production, and biodiversity conservation. However, it is difficult to grasp the whole picture of an individual system, because the activity of pollinators fluctuates depending on the flowering period and time of day. In order to reveal effective pollinator ta...

The camera response function (CRF) that projects hyperspectral radiance to the corresponding RGB images is important for most hyperspectral image super-resolution (HSI-SR) models. In contrast to most studies that focus on improving HSI-SR performance through new architectures, we aim to prevent the model performance drop by learning the CRF of any...

Multispectral images (MSIs) are valuable for precision agriculture due to the extra spectral information acquired compared to natural color RGB (ncRGB) images. In this paper, we thus aim to generate high spatial MSIs through a robust, deep learning-based reconstruction method using ncRGB images. Using the data from the agronomic research trial for...

Training deep learning models typically requires a huge amount of labeled data which is expensive to acquire, especially in dense prediction tasks such as semantic segmentation. Moreover, plant phenotyping datasets pose additional challenges of heavy occlusion and varied lighting conditions which makes annotations more time-consuming to obtain. Act...

Fruit yield estimation is crucial to establish fruit harvesting and marketing strategies. Recently, computer vision and deep learning techniques have been used to estimate citrus fruit yield and have exhibited a notable fruit detection ability. However, computer-vision-based citrus fruit counting has two key limitations: inconsistent fruit detectio...

Phenotyping is a critical process in plant breeding, especially when there is an increasing demand for streamlining a selection process in a breeding program. Since manual phenotyping has limited efficiency, highthroughput phenotyping methods are recently popularized owing to progress in sensor and image processing technologies. However, in a size-...

Machine learning systems deployed in the wild are often trained on a source distribution but deployed on a different target distribution. Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data. However, existing distribution shift benchmarks for unlabele...

Fruit detection and counting are essential tasks for horticulture research. With computer vision technology development, fruit detection techniques based on deep learning have been widely used in modern orchards. However, most deep learning-based fruit detection models are generated based on fully supervised approaches, which means a model trained...

In recent years, deep-learning-based fruit-detection technology has exhibited excellent performance in modern horticulture research. However, deploying deep learning algorithms in real-time field applications is still challenging, owing to the relatively low image processing capability of edge devices. Such limitations are becoming a new bottleneck...

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultura...

Early-stage plant density is an essential trait that determines the fate of a genotype under given environmental conditions and management practices. The use of RGB images taken from UAVs may replace the traditional visual counting in fields with improved throughput, accuracy, and access to plant localization. However, high-resolution images are re...

Convenient, efficient, and high-throughput estimation of wheat heading dates is of great significance in plant sciences and agricultural research. However, documenting heading dates is time-consuming, labor-intensive, and subjective on a large-scale field. To overcome these challenges, model- and image-based approaches are used to estimate heading...

Unmanned aerial vehicle (UAV) and structure from motion (SfM) photogrammetry techniques are widely used for field-based, high-throughput plant phenotyping nowadays, but some of the intermediate processes throughout the workflow remain manual. For example, geographic information system (GIS) software is used to manually assess the 2D/3D field recons...

Distribution shifts—where the training distribution differs from the test distribution—can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild. Despite their ubiquity in the real-world deployments, these distribution shifts are under-represented in the datasets widely used in the ML community today. To address t...

1. High-throughput 3D phenotyping is a rapidly emerging field that has widespread application for measurement of individual plants. Despite this, high-throughput plant phenotyping is rarely used in ecological studies due to financial and logistical limitations. 2. We introduce EasyDCP, a Python package for 3D phenotyping, which uses photogrammetry...

Unmanned aircraft system (UAS) is a particularly powerful tool for plant phenotyping, due to reasonable cost of procurement and deployment, ease and flexibility for control and operation, ability to reconfigure sensor payloads to diversify sensing, and the ability to seamlessly fit into a larger connected phenotyping network. These advantages have...

Regular monitoring is worthwhile to maintain a healthy crop. Historically, the manual observation was used to monitor crops, which is time-consuming and often costly. The recent boom in the development of Unmanned Aerial Vehicles (UAVs) has established a quick and easy way to monitor crops. UAVs can cover a wide area in a few minutes and obtain use...

Recent advances in unmanned aerial vehicle (UAV) remote sensing and image analysis provide large amounts of plant canopy data, but there is no method to integrate the large imagery datasets with the much smaller manually collected datasets. A simple geographic information system (GIS)-based analysis for a UAV-supported field study (GAUSS) analytica...

Early-stage plant density is an essential trait that determines the fate of a genotype under given environmental conditions and management practices. The use of RGB images taken from UAVs may replace traditional visual counting in fields with improved throughput, accuracy and access to plant localization. However, high-resolution (HR) images are re...

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4,700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD has successfully attracted attention from both the computer vision and agricultural sc...

Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems. In plant phenotyping, data competitions have a rich history, and new outdoor field datasets have potential for new data competitions. We developed the Global Wheat Challenge as a generalization competition to...

The automation of plant phenotyping using 3D imaging techniques is indispensable. However, conventional methods for reconstructing the leaf surface from 3D point clouds have a trade-off between the accuracy of leaf surface reconstruction and the method’s robustness against noise and missing points. To mitigate this trade-off, we developed a leaf su...

Background:The use of 3D based high-throughput phenotyping improves theefficiency of crop management and monitoring practices. Thestructure-from-motion and multi-view stereo photogrammetry (SfM-MVS)technique, applicable to common RGB digital cameras, has been widely used forthis and can be implemented by many commercial and open-source tools. Byusi...

In this study, we are developing a work assist system using Augmented Reality (AR) using a hyperspectral camera. In recent years, spectral information has been used to analyze agricultural products. In the analysis, it is expected that the understanding of the analysis will be improved by comparing it with human visual information such as RGB image...

• Recent advances in Unmanned Aerial Vehicle (UAVs) and image processing have made high‐throughput field phenotyping possible at plot/canopy level in the mass grown experiment. Such techniques are now expected to be used for individual level phenotyping in the single grown experiment. • We found two main challenges of phenotyping individual plants...

Competition among neighbouring plants plays essential roles in growth, reproduction, population dynamics, and community assembly, but how competition drives local adaptation and the traits underlying the adaptation remain unclear. Here, we focused on populations of the aggressive weed Digitaria ciliaris from urban and rural habitats as low‐ and hig...

The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health, size, maturity stage, and the presence of awns. Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning...

In light of growing challenges in agriculture with ever growing food demand across the world, efficient crop management techniques are necessary to increase crop yield. Precision agriculture techniques allow the stakeholders to make effective and customized crop management decisions based on data gathered from monitoring crop environments. Plant ph...