Search Results (236)

This research aims to develop a novel technique to acquire a large amount of high-density, high-precision 3D point cloud data for plant phenotyping using photogrammetry technology. The complexity of plant structures, characterized by overlapping thin parts such as leaves and stems, makes it difficult to reconstruct accurate 3D point clouds. One challenge in this regard is occlusion, where points in the 3D point cloud cannot be obtained due to overlapping parts, preventing accurate point capture. Another is the generation of erroneous points in non-existent locations due to image-matching errors along object outlines. To overcome these challenges, we propose a 3D point cloud reconstruction method named closed-loop coarse-to-fine method with multi-masked matching (CLCFM3). This method repeatedly executes a process that generates point clouds locally to suppress occlusion (multi-matching) and a process that removes noise points using a mask image (masked matching). Furthermore, we propose the closed-loop coarse-to-fine method (CLCFM) to improve the accuracy of structure from motion, which is essential for implementing the proposed point cloud reconstruction method. CLCFM solves loop closure by performing coarse-to-fine camera position estimation. By facilitating the acquisition of high-density, high-precision 3D data on a large number of plant bodies, as is necessary for research activities, this approach is expected to enable comparative analysis of visible phenotypes in the growth process of a wide range of plant species based on 3D information. Full article

Efficient and non-destructive extraction of organ-level phenotypic parameters of sesame (Sesamum indicum L.) plants is a key bottleneck in current sesame phenotyping research. To address this issue, this study proposes a method for organ segmentation and phenotypic parameter extraction based on CAVF-PointNet++ and geometric clustering. First, this method constructs a high-precision 3D point cloud using multi-view RGB image sequences. Based on the PointNet++ model, a CAVF-PointNet++ model is designed to perform feature learning on point cloud data and realize the automatic segmentation of stems, petioles, and leaves. Meanwhile, different leaves are segmented using curvature-density clustering technology. Based on the results of segmentation, this study extracted a total of six organ-level phenotypic parameters, including plant height, stem diameter, leaf length, leaf width, leaf angle, and leaf area. The experimental results show that in the segmentation tasks of stems, petioles, and leaves, the overall accuracy of CAVF-PointNet++ reaches 96.93%, and the mean intersection over union is 82.56%, which are 1.72% and 3.64% higher than those of PointNet++, demonstrating excellent segmentation performance. Compared with the results of manual segmentation of different leaves, the proposed clustering method achieves high levels in terms of precision, recall, and F1-score, and the segmentation results are highly consistent. In terms of phenotypic parameter measurement, the coefficients of determination between manual measurement values and algorithmic measurement values are 0.984, 0.926, 0.962, 0.942, 0.914, and 0.984 in sequence, with root-mean-square errors of 5.9 cm, 1.24 mm, 1.9 cm, 1.2 cm, 3.5°, and 6.22 cm², respectively. The measurement results of the proposed method show a strong correlation with the actual values, providing strong technical support for sesame phenotyping research and precision agriculture. It is expected to provide reference and support for the automated 3D phenotypic analysis of other crops in the future. Full article

Accurate measurement of seedling traits is essential for plant phenotyping, particularly in understanding growth dynamics and stress responses. Elm trees (Ulmus spp.), ecologically and economically significant, pose unique challenges due to their curved seedling morphology. Traditional manual measurement methods are time-consuming, prone to human error, and often lack consistency. Moreover, automated approaches remain limited and often fail to accurately process seedlings with nonlinear or curved morphologies. In this study, we introduce GLEN, a deep learning-based model for detecting germinating elm seeds and accurately estimating their lengths of germinating structures. It leverages a dual-path architecture that combines pixel-level spatial features with instance-level semantic information, enabling robust measurement of curved radicles. To support training, we construct GermElmData, a curated dataset of annotated elm seedling images, and introduce a novel synthetic data generation pipeline that produces high-fidelity, morphologically diverse germination images. This reduces the dependence on extensive manual annotations and improves model generalization. Experimental results demonstrate that GLEN achieves an estimation error on the order of millimeters, outperforming existing models. Beyond quantifying germinating elm seeds, the architectural design and data augmentation strategies in GLEN offer a scalable framework for morphological quantification in both plant phenotyping and broader biomedical imaging domains. Full article

To address the limitations of the existing cotton seedling growth prediction methods in field environments, specifically, poor representation of spatiotemporal features and low visual fidelity in texture rendering, this paper proposes an algorithm for the prediction of cotton seedling growth from images based on FCA-STNet. The model leverages historical sequences of cotton seedling RGB images to generate an image of the predicted growth at time t + 1 and extracts 37 phenotypic traits from the predicted image. A novel STNet structure is designed to enhance the representation of spatiotemporal dependencies, while an Adaptive Fine-Grained Channel Attention (FCA) module is integrated to capture both global and local feature information. This attention mechanism focuses on individual cotton plants and their textural characteristics, effectively reducing the interference from common field-related challenges such as insufficient lighting, leaf fluttering, and wind disturbances. The experimental results demonstrate that the predicted images achieved an MSE of 0.0086, MAE of 0.0321, SSIM of 0.8339, and PSNR of 20.7011 on the test set, representing improvements of 2.27%, 0.31%, 4.73%, and 11.20%, respectively, over the baseline STNet. The method outperforms several mainstream spatiotemporal prediction models. Furthermore, the majority of the predicted phenotypic traits exhibited correlations with actual measurements with coefficients above 0.8, indicating high prediction accuracy. The proposed FCA-STNet model enables visually realistic prediction of cotton seedling growth in open-field conditions, offering a new perspective for research in growth prediction. Full article

To address the problems of traditional methods that rely on destructive sampling, the poor adaptability of fixed equipment, and the susceptibility of single-view angle measurements to occlusions, a non-destructive and portable device for three-dimensional phenotyping and biomass detection in lettuce was developed. Based on the Structure-from-Motion Multi-View Stereo (SFM-MVS) algorithms, a high-precision three-dimensional point cloud model was reconstructed from multi-view RGB image sequences, and 12 phenotypic parameters, such as plant height, crown width, were accurately extracted. Through regression analyses of plant height, crown width, and crown height, and the R² values were 0.98, 0.99, and 0.99, respectively, the RMSE values were 2.26 mm, 1.74 mm, and 1.69 mm, respectively. On this basis, four biomass prediction models were developed using Adaptive Boosting (AdaBoost), Support Vector Regression (SVR), Gradient Boosting Decision Tree (GBDT), and Random Forest Regression (RFR). The results indicated that the RFR model based on the projected convex hull area, point cloud convex hull surface area, and projected convex hull perimeter performed the best, with an R² of 0.90, an RMSE of 2.63 g, and an RMSEn of 9.53%, indicating that the RFR was able to accurately simulate lettuce biomass. This research achieves three-dimensional reconstruction and accurate biomass prediction of facility lettuce, and provides a portable and lightweight solution for facility crop growth detection. Full article

The increasing global demand for resilient, sustainable agricultural systems has intensified the need for advanced monitoring strategies, particularly for climate-adaptive crops such as Moringa oleifera Lam. This study presents an integrated approach using Unmanned Aerial Vehicles (UAVs) equipped with multispectral and thermal cameras to monitor the vegetative performance and determine the optimal harvest period of four M. oleifera genotypes in a Mediterranean environment. High-resolution data were collected and processed to generate the NDVI, canopy temperature, and height maps, enabling the assessment of plant vigor, stress conditions, and spatial canopy structure. NDVI analysis revealed robust vegetative growth (0.7–0.9), with optimal harvest timing identified on 30 October 2024, when the mean NDVI exceeded 0.85. Thermal imaging effectively discriminated plant crowns from surrounding weeds by capturing cooler canopy zones due to active transpiration. A clear inverse correlation between NDVI and Land Surface Temperature (LST) was observed, reinforcing its relevance for stress diagnostics and environmental monitoring. The results underscore the value of UAV-based multi-sensor systems for precision agriculture, offering scalable tools for phenotyping, harvest optimization, and sustainable management of medicinal and aromatic crops in semiarid regions. Moreover, in this study, to produce M. oleifera leaf powder intended for use as a food ingredient, the leaves of four M. oleifera genotypes were dried, milled, and evaluated for their hygiene and safety characteristics. Plate count analyses confirmed the absence of pathogenic bacterial colonies in the M. oleifera leaf powders, highlighting their potential application as natural and functional additives in food production. Full article

Lettuce (Lactuca sativa) is the most consumed leafy vegetable in the world, with great economic and social importance in Brazil. In breeding programs, selecting genotypes with high agronomic potential is essential to meet market demands and cultivation conditions. In this context, plant phenotyping by means of multispectral imaging emerges as a modern, efficient and non-destructive tool, which enhances the analysis of phenotypic characteristics quickly and accurately. Therefore, the aim of the present study was to group different lettuce situations according to their group using image-based phenotyping, in addition to morphological descriptors and agronomic evaluations. The experiment was carried out in an experimental area of the Federal University of Uberlândia, Campus of Monte Carmelo, MG, Brazil, in randomized blocks with three replicates and 17 treatments (lettuce populations of the F₂ generation, resulting from the cross between different lettuce cultivars and/or lines). Morphological descriptors and agronomic characteristics were obtained in the field. The vegetation indices GLI, NDVI, GNDVI, NGRDI and NDRE were calculated from images acquired at 49 days after transplanting. Means were compared using the Scott–Knott test (p ≤ 0.05), and the results were presented in box plots. Genetic dissimilarity was confirmed by multivariate analysis, which resulted in a cophenetic correlation coefficient of 96.11%. In addition, validation between field-collected data and image-obtained data was performed using heat maps and Pearson’s correlation. Populations UFU 003, UFU 006, UFU 009, UFU 011, UFU 012, UFU 013, UFU 014, UFU 016 and UFU 017 stood out, with high agronomic potential. Image-based phenotyping was correlated with agronomic traits and, therefore, can be considered an alternative to grouping different lettuce populations. Full article

Dry bean, the fourth-largest pulse crop in Canada is increasingly impacted by climate variability, needing efficient methods to support cultivar development. This study investigates the potential of unmanned aerial vehicle (UAV)-based Light Detection and Ranging (LiDAR) and multispectral imaging (MSI) for high-throughput phenotyping of dry bean traits. Image data were collected across two dry bean field trials to assess plant height, lodging and seed yield. Multiple LiDAR-derived features accessing canopy height, crop lodging and digital biomass were evaluated against manual height measurements, visually rated lodging scale and seed yield, respectively. At the same time, three MSI-derived data were used to estimate seed yield. Classification- and regression-based machine learning models were used to estimate key agronomic traits using both LiDAR and MSI-based crop features. The canopy height derived from LiDAR showed a good correlation (R² = 0.86) with measured plant height at the mid-pod filling (R6) stage. Lodging classification was most effective using Gradient Boosting, Random Forest and Logistic Regression, with R8 (physiological maturity stage) canopy height being the dominant predictor. For seed yield prediction, models integrating LiDAR and MSI outperformed individual datasets, with Gradient Boosting Regression Trees yielding the highest accuracy (R² = 0.64, RMSE = 687.2 kg/ha and MAE = 521.6 kg/ha). Normalized Difference Vegetation Index (NDVI) at the R6 stage was identified as the most informative spectral feature. Overall, this study demonstrates the importance of integrating UAV-based LiDAR and MSI for accurate, non-destructive phenotyping in dry bean breeding programs. Full article

In the context of the booming development of precision agriculture and plant phenotyping, plant 3D reconstruction technology has become a research hotspot, with widespread applications in plant growth monitoring, pest and disease detection, and smart agricultural equipment. Given the complex geometric and textural characteristics of plants, traditional 2D image analysis methods are difficult to meet the modeling requirements, highlighting the growing importance of 3D reconstruction technology. This paper reviews active vision techniques (such as structured light, time-of-flight, and laser scanning methods), passive vision techniques (such as stereo vision and structure from motion), and deep learning-based 3D reconstruction methods (such as NeRF, CNN, and 3DGS). These technologies enhance crop analysis accuracy from multiple perspectives, provide strong support for agricultural production, and significantly promote the development of the field of plant research. Full article

Accurate grain yield (GY) prediction is essential in wheat breeding to enhance selection and accelerate breeding cycles. This study explored whether high-throughput phenotyping (HTP) data collected from small plot (SP) trials can effectively predict GY outcomes in later-stage big plot (BP) trials. Genomic (G) data were combined with hyperspectral (H) and multispectral + thermal (M) imaging across the 2022 and 2023 growing seasons at the Plant Science Research and Education Unit, Citra, Florida. A panel of 312 wheat genotypes was analyzed using GBLUP-based models, integrating G + H and G + M data from SP to predict BP yield. SP models demonstrated promising predictive ability, with G + H models achieving moderate within-year (0.43 to 0.51) and across-year (0.43) prediction accuracies, while G + M models reached 0.53 to 0.58 and 0.45, respectively. The Random Forest Regression (RFR) model produced an accuracy of 0.47 when M data from the 2022 SP, combined with G, was used to predict BP yield in 2023. Additionally, the top 25% specificity (coincide index) was evaluated, with models showing up to 47–51% within a year and 43–45% between years overlap in the highest predicted-yielding lines between SP and BP trials, further emphasizing the potential of SP data for early selection. These findings suggest that SP trials can provide meaningful predictions for BP yields, enabling earlier selection and faster breeding cycles. Full article

Plant-parasiticnematodes represent a significant biosecurity threat in cross-border plant quarantine, necessitating precise identification for effective border control. While DL models have demonstrated success in nematode image classification based on morphological features, the limited availability of high-quality samples and the species-specific nature of nematodes result in insufficient training data, which constrains model performance. Although generative models have shown promise in data augmentation, they often struggle to balance morphological fidelity and phenotypic diversity. This paper proposes a novel few-shot data augmentation framework based on a morphology-constrained latent diffusion model, which, for the first time, integrates morphological constraints into the latent diffusion process. By geometrically parameterizing nematode morphology, the proposed approach enhances topological fidelity in the generated images and addresses key limitations of traditional generative models in controlling biological shapes. This framework is designed to augment nematode image datasets and improve classification performance under limited data conditions. The framework consists of three key components: First, we incorporate a fine-tuning strategy that preserves the generalization capability of model in few-shot settings. Second, we extract morphological constraints from nematode images using edge detection and a moving least squares method, capturing key structural details. Finally, we embed these constraints into the latent space of the diffusion model, ensuring generated images maintain both fidelity and diversity. Experimental results demonstrate that our approach significantly enhances classification accuracy. For imbalanced datasets, the Top-1 accuracy of multiple classification models improved by 7.34–14.66% compared to models trained without augmentation, and by 2.0–5.67% compared to models using traditional data augmentation. Additionally, when replacing up to 25% of real images with generated ones in a balanced dataset, model performance remained nearly unchanged, indicating the robustness and effectiveness of the method. Ablation experiments demonstrate that the morphology-guided strategy achieves superior image quality compared to both unconstrained and edge-based constraint methods, with a Fréchet Inception Distance of 12.95 and an Inception Score of 1.21 ± 0.057. These results indicate that the proposed method effectively balances morphological fidelity and phenotypic diversity in image generation. Full article

High-throughput image analysis is a key tool for the efficient assessment of quantitative plant phenotypes. A typical approach to the computation of quantitative plant traits from image data consists of two major steps including (i) image segmentation followed by (ii) calculation of quantitative traits of segmented plant structures. Despite substantial advancements in deep learning-based segmentation techniques, minor artifacts of image segmentation cannot be completely avoided. For several commonly used traits including plant width, height, convex hull, etc., even small inaccuracies in image segmentation can lead to large errors. Ad hoc approaches to cleaning ’small noisy structures’ are, in general, data-dependent and may lead to substantial loss of relevant small plant structures and, consequently, falsified phenotypic traits. Here, we present a straightforward end-to-end approach to direct computation of phenotypic traits from image data using a deep learning regression model. Our experimental results show that image-to-trait regression models outperform a conventional segmentation-based approach for a number of commonly sought plant traits of plant morphology and health including shoot area, linear dimensions and color fingerprints. Since segmentation is missing in predictions of regression models, visualization of activation layer maps can still be used as a blueprint to model explainability. Although end-to-end models have a number of limitations compared to more complex network architectures, they can still be of interest for multiple phenotyping scenarios with fixed optical setups (such as high-throughput greenhouse screenings), where the accuracy of routine trait predictions and not necessarily the generalizability is the primary goal. Full article

Weed control is fundamental to modern agriculture, underpinning crop productivity, food security, and the economic sustainability of farming operations. Herbicides have long been the cornerstone of effective weed management, significantly enhancing agricultural yields over recent decades. However, the field now faces critical challenges, including stagnation in the discovery of new herbicide modes of action (MOAs) and the escalating prevalence of herbicide-resistant weed populations. High research and development costs, coupled with stringent regulatory hurdles, have impeded the introduction of novel herbicides, while the widespread reliance on glyphosate-based systems has accelerated resistance development. In response to these issues, advanced image-based plant phenotyping technologies have emerged as pivotal tools in addressing herbicide-related challenges in weed science. Utilizing sensor technologies such as hyperspectral, multispectral, RGB, fluorescence, and thermal imaging methods, plant phenotyping enables the precise monitoring of herbicide drift, analysis of resistance mechanisms, and development of new herbicides with innovative MOAs. The integration of machine learning algorithms with imaging data further enhances the ability to detect subtle phenotypic changes, predict herbicide resistance, and facilitate timely interventions. This review comprehensively examines the application of image phenotyping technologies in weed science, detailing various sensor types and deployment platforms, exploring modeling methods, and highlighting unique findings and innovative applications. Additionally, it addresses current limitations and proposes future research directions, emphasizing the significant contributions of phenotyping advancements to sustainable and effective weed management strategies. By leveraging these sophisticated technologies, the agricultural sector can overcome existing herbicide challenges, ensuring continued productivity and resilience in the face of evolving weed pressures. Full article

Global climate change predictions point to an increase in the frequency of droughts and floods, which are a huge challenge to food production. During crop evolution, different mechanisms for drought resilience have emerged, and studies suggest that roots can be an important key in understanding these mechanisms. However, knowledge is still scarce, being fundamental to its exploitation. Plant-based protein, especially grain legume crops, will be crucial in meeting the demand for affordable and healthy food due to their high protein content. In addition, grain legumes have the unique ability for biological nitrogen fixation (BNF) through symbiosis with bacteria, which contributes to sustainable agriculture. The exploitation of root phenotyping techniques in grain legumes is an important step toward understanding their drought resilience mechanisms and selecting more resilient genotypes. Different methodologies are available for root phenotyping, including the paper pouch approach, rhizotrons and the semi-hydroponic system. Additionally, different imaging techniques have been employed to assess root traits. This review provides an overview of the root system architecture (RSA) of grain legumes, its role in drought stress resilience and the phenotyping approaches useful for the identification of accessions resilient to water stress. Consequently, this knowledge will be important in mitigating the effects of climate change and improving grain legume production. Full article

Rice is a vital staple in many countries, and as the demand for food diversity rises, the focus has shifted towards improving rice quality rather than just yield. This shift in breeders’ goals has led to the development of breeding populations aimed at comprehensively assessing rice grain appearance quality. In this regard, we developed an F₁₁ rice recombinant inbred line population derived from a cross between the IR28 and Shahpasand (SH) varieties and assessed the grain appearance characteristics of 151 lines and seven varieties using a computer vision system and a new generation of phenotyping tools for rapidly and accurately evaluating all grain quality-related traits. In this method, characteristics such as area, perimeter, length, width, aspect ratio, roundness, whole kernel, chalkiness, red stain, mill rate, and brown kernel were measured very quickly and precisely. To select the best lines, considering multiple traits simultaneously, we used the multi-trait genotype ideotype distance index (MGIDI) as a successful selection index. Based on the MGIDI and a 13% selection intensity, we identified 17 lines and three varieties as superior genotypes for their grain appearance quality traits. Line 59 was considered the best due to its lowest MGIDI value (0.70). Lines 19, 31, 32, 45, 50, 59, 60, 62, 73, 107, 114, 122, 125, 135, 139, 144, and 152 exhibited superior grain quality traits compared to the parents, making them high-quality candidates and indicating transgressive segregation within the current RIL population. In conclusion, the image processing technique used in this study was found to be a fast and precise tool for phenotyping in large populations, helpful in the selection process in plant breeding. Additionally, the MGIDI, by considering multiple traits simultaneously, can help breeders select high-quality genotypes that better match consumer preferences. Full article