Traditional manual measurement of garlic bulb phenotypic traits is inefficient, subjective, poorly reproducible, and may cause sample damage. To improve the adaptability of three-dimensional reconstruction to garlic bulb morphology and grading-related parameter extraction, this study developed a non-destructive phenotypic measurement workflow based on multi-view image-based three-dimensional reconstruction. Four garlic materials with distinct bulb morphologies and epidermal characteristics were used to demonstrate the feasibility of the reconstruction workflow, and 40 Lanling white-skinned garlic bulbs were used for quantitative accuracy validation. Multi-view images were acquired using a high-resolution camera, a motorized turntable, and a controlled illumination system. Three-dimensional models were reconstructed using ContextCapture, and the resulting point clouds were processed in CloudCompare through cropping, denoising, downsampling, and pose correction. Maximum longitudinal diameter, maximum transverse diameter, and volume were extracted from the processed point clouds according to GB/T 45244-2025 (Grades and Specifications of Garlic) and validated against manual reference measurements. The coefficients of determination for maximum longitudinal diameter, maximum transverse diameter, and volume were 0.9935, 0.9909, and 0.9924, respectively, with RMSE values of 0.0529 cm, 0.0520 cm, and 0.8874 cm3, and MAPE values of 0.6647%, 0.7765%, and 1.9149%. Additional MAE, bias, confidence interval, and Bland–Altman analyses further supported the agreement between model-derived and manual reference measurements. These results demonstrate the feasibility of multi-view image-based three-dimensional reconstruction for non-destructive garlic bulb phenotypic measurement and provide a methodological basis for future grading-related assessment and three-dimensional phenotyping of bulbous horticultural crops.
Why it matches plant phenotyping methodsニンニク球の形態形質を非破壊的に取得する3D画像計測ワークフローを開発し、手動測定と定量検証しており、フェノタイピング手法が中心である。
abstractthis study developed a non-destructive phenotypic measurement workflow based on multi-view image-based three-dimensional reconstruction.
Background Accurate pre-harvest yield estimation of underground bulb crops such as onion and garlic is important for precision agriculture, harvest planning, and food-security-oriented decision-making. However, their harvestable organs develop below ground and cannot be directly observed using conventional remote sensing methods. This study aimed to develop a non-destructive yield estimation framework by integrating UAV-based hyperspectral imaging with hybrid machine learning models. Method Field experiments were conducted in Muan-gun, Korea, using onion and garlic as representative underground bulb crops. UAV-based hyperspectral images, crop growth traits, and destructive live bulb weight measurements were collected during the growing period. Hyperspectral images were processed through geometric correction, radiometric correction, and Savitzky-Golay spectral smoothing. Three dimensionality reduction methods, including genetic algorithm (GA), principal component analysis (PCA), and clustering, were used to reduce spectral redundancy. Five prediction models, including random forest (RF), XGBoost, partial least squares regression (PLSR), multilayer perceptron (MLP), and residual network (ResNet), were then evaluated for live bulb weight prediction. Result Significant spectral differences were observed in the 550-680 nm and 730-800 nm bands, which were closely associated with crop yield and below-ground bulb development. GA was the most effective feature selection method for extracting yield-related spectral bands. For onion yield prediction, the GA + RF model achieved the highest predictive accuracy, with an R 2 of 0.9656 and an NRMSE of 18.55%. For garlic yield prediction, PLSR showed the best performance, with an R 2 of 0.9260 and an NRMSE of 27.20%. Conclusion The proposed UAV-based hyperspectral framework enables accurate, real-time, and non-destructive yield estimation for underground bulb crops. This approach reduces reliance on labor-intensive destructive sampling and provides a practical tool for precision crop monitoring and data-driven agricultural management.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像と機械学習による地下球根の収量（生体球重）推定フレームワークの開発・比較評価が研究の中心であり、植物形質の取得・推定手法に該当する。
abstractThis study aimed to develop a non-destructive yield estimation framework by integrating UAV-based hyperspectral imaging with hybrid machine learning models.
본 연구는 UAV 기반 다중분광 영상을 활용하여 양파 및 마늘 재배지에서 작물 분할 기법이 식생지수 기반 생산량 예측 성능에 미치는 영향을 분석하는 것을 목적으로 한다. 기존 연구에서는 처리구(plot)별 ROI 평균을 이용한 식생지수 산출이 주로 활용되었으나, 작물 분할 전략 자체가 예측 성능에 미치는 영향은 충분히 검토되지 않았다. 이에 본 연구에서는 비지도 학습 기반작물 분할(none, otsu-NDVI, k-means, GMM)을 적용하고, 분할 방법에 따른 처리구 단위 식생지수 특성과 예측 성능 차이를 비교하였다. 6개 생육 시점의 UAV 다중분광 영상으로부터 분할된 식생 영역 대상으로 9개 식생지수를 산출하고, PLS 기반으로 차원 축소하고 머신러닝 모델을 이용해 생산량을 예측하였다. 모델 성능은 처리구 단위 Leave-one-out 교차검증으로 평가하였다. 분석결과, 작물 분할 적용 시 식생지수 분포가 유의하게 변화하였으며, 반복측정 분산분석을 통해 분할 방법과 생육 시점 간 상호작용 효과가 확인되었다. 이러한 입력 변수 특성의 개선은 생산량 예측 성능 향상으로 이어졌고, 특히 군집 기반 분할에서 RMSE 및 MAPE가 가장 크게 감소하였다.
Why it matches plant phenotyping methodsUAV多波長画像における作物分割手法を比較し、植生指数から収量を推定する方法と性能を評価しており、表現型取得・抽出が研究の中心である。
This systematic literature review investigates the development of a Ground-Penetrating Radar (GPR)-based object detection system tailored for under-ground garlic crop monitoring. While garlic-specific GPR applications re-main limited, studies on structurally similar root crops such as potatoes and carrots provide a valuable reference framework. Using a PRISMA-guided methodology, 16 relevant studies were analysed and synthesized, highlighting advancements in GPR signal processing, object reconstruction, and machine learning integration. Results show that mid- frequency GPR (500–800 MHz), especially when paired with deep learning models such as 3D Convolutional Neural Networks (CNNs), offers high accuracy in detecting root structures. Key challenges such as signal attenuation in clay-rich and tropical soils are addressed through electromagnetic induction (EMI) hybridization and antenna optimization. A comparative matrix summarizes the most relevant findings, and actionable recommendations are proposed to guide future research. These include the development of garlic-specific datasets, localized field testing, and AI- enhanced signal classification. GPR, when effectively configured and paired with machine learning, presents a viable solution for real-time, non-invasive garlic crop monitoring in tropical agriculture.
Why it matches plant phenotyping methods地下作物の成長・根構造をGPRで検出する手法の開発に焦点を当てた系統的レビューであり、植物形態の非破壊取得・抽出方法が中心です。
titleA Systematic Literature Review for the Development of an Object Detection System for Monitoring Underground Crop (Garlic) Growth Using GPR
Problem Garlic is a common ingredient that not only enhances the flavor of dishes but also has various beneficial effects and functions for humans. However, its leaf diseases and pests have a serious impact on the growth and yield. Traditional plant leaf disease detection methods have shortcomings, such as high time consumption and low recognition accuracy. Methodology As a result, we present a deep learning approach based on an upgraded ResNet18, triplet, convolutional block (RTCB) attention mechanism for recognizing garlic leaf diseases. First, we replace the convolutional layers in the residual block with partial convolutions based on the classic ResNet18 architecture to improve computational efficiency. Then, we introduce triplet attention after the first convolutional layer to enhance the model's ability to focus on key features. Finally, we add a convolutional block attention mechanism after each residual layer to improve the model's feature perception. Results The experimental results demonstrate that the proposed model achieves a classification accuracy of 98.90%, which is superior to outstanding deep learning models such as Efficient-v2-B0, MobileOne-S0, OverLoCK-S, EfficientFormer, and MobileMamba. The proposed RTCB has a faster computation speed, higher recognition precision, and stronger generalization ability. Contribution The proposed approach provides a scalable technical reference for the engineering application of automatic disease monitoring and control in intelligent agriculture. The current strategy is conducive to the deployment of edge computing equipment and has extensive significance and application potential in plant leaf disease detection.
Why it matches plant phenotyping methodsニンニク葉の病害状態を画像から識別する深層学習モデルの開発・比較評価が中心であり、植物病害フェノタイピング手法に該当する。
abstractwe present a deep learning approach based on an upgraded ResNet18, triplet, convolutional block (RTCB) attention mechanism for recognizing garlic leaf diseases.
Volatile organic compounds (VOCs) are common constituents of fruits, vegetables, and crops, and are closely associated with their quality attributes, such as firmness, sugar level, ripeness, translucency, and pungency levels. While VOCs are vital for assessing vegetable quality and phenotypic classification, traditional detection methods, such as Gas Chromatography-Mass Spectrometry (GC-MS) and Proton Transfer Reaction Mass Spectrometry (PTR-MS) are limited by expensive equipment, complex sample preparation, and slow turnaround time. Additionally, the transient nature of VOCs complicates their detection using these methods. Here, we developed a paper-based colorimetric sensor array combined with needles that could: 1) induce vegetable VOC release in a minimally invasive fashion, and 2) analyze VOCs in situ with a smartphone reader device. The needle sampling device helped release specific VOCs from the studied vegetables that usually require mechanic stimulation, while maintaining the vegetable viability. On the other hand, the colorimetric sensor array was optimized for sulfur compound-based VOCs with a limit of detection (LOD) in the 1-25 ppm range, and classified fourteen different vegetable VOCs, including sulfoxides, sulfides, mercaptans, thiophenes, and aldehydes. By combining principal components analysis (PCA) analysis, the integrated sensor platform proficiently discriminated between four vegetable subtypes originating from two major categories within 2 min of testing time. Additionally, the sensor demonstrates the capability to distinguish between different types of tested fruits and vegetables, including garlic, green pepper, and nectarine. This rapid and minimally invasive sensing technology holds great promise for conducting field-based vegetable quality monitoring.
Why it matches plant phenotyping methods野菜のVOCを低侵襲に取得し、センサーアレイとスマートフォンで分類する測定プラットフォームの開発が研究の中心であり、野菜の品質・表現型分類に直接用いられている。
abstractHere, we developed a paper-based colorimetric sensor array combined with needles that could: 1) induce vegetable VOC release in a minimally invasive fashion, and 2) analyze VOCs in situ with a smartphone reader device.
Canopy coverage-based crop growth monitoring is highly dependent on the performance of crop segmentation algorithms. Under field conditions, crop segmentation for unmanned aerial vehicle (UAV) imagery should be sophisticated considering geometric distortion of images by wind and illumination variations. Under Korean cultivation conditions, a plastic mulch used to restrict weeds and prevent cold weather damage increases the complexity of the image background. In particular, on-site monitoring of onion and garlic growth has been limited by their morphology because they have long narrow leaves. The ultimate goal of this study was to quantify the growth parameters of onion and garlic at multiple growth stages using red, green, and blue (RGB) imagery obtained with UAVs. Canopy coverage and plant height were used as predictor variables to develop mathematical models to estimate the fresh weights of onion and garlic. The use of a CIE L*a*b* color space and mean shift (MS) algorithm enhanced the extraction of the canopy coverage of onion and garlic from complex backgrounds, including plastic mulch, soil, and shadows under varying illumination conditions. Multiple linear regression models consisting of the a* band-based vegetation fraction (VF) and structure from motion (SfM)-based plant height (PH) fitted the fresh weight data of onion and garlic well with high coefficients of determination (R²) ranging from 0.82 to 0.92. The validation results showed an almost 1:1 slope with highly linear relationships (R² > 0.82) between the onion and garlic fresh weights obtained with the UAV RGB imagery and actual fresh weights, confirming that the UAV-RGB imagery based on the use of the a*band and PH can be used to quantify the spatial and temporal variability of onion and garlic growth parameters during the growing season.
Why it matches plant phenotyping methodsUAV RGB画像の分割・SfM解析により、タマネギとニンニクのキャノピー被覆、草丈、成長量・新鮮重を推定する手法を開発・検証しており、表現型取得が研究の中心である。
abstractThe ultimate goal of this study was to quantify the growth parameters of onion and garlic at multiple growth stages using red, green, and blue (RGB) imagery obtained with UAVs.
A smart agricultural system is necessary for monitoring crop growth and stress conditions using near-ground-based remote sensing techniques. Crop growth can be estimated using several standard crop growth parameters. However, obtaining timed sequential data for observing leaf area index (LAI) is challenging, and normalized difference vegetation index (NDVI) estimation in crop fields requires the installation of sensors on frames structure that are taller than the crop. Canopy light transmittance (CLT) indicates the degree of decrease in the amount of light passing through some material. It was conventionally used to understand canopy radiative transfer. This study examined the viability of CLT as a novel crop parameter for monitoring crop growth conditions. The CLT, LAI, and NDVI of a garlic field were recorded for five years. The correlation between daily CLT and LAI was higher than that between NDVI and LAI. Thus, CLT has the potential to sequentially estimate crop LAI values, particularly for capturing the temporal patterns of LAI. In addition, like NDVI, CLT showed sensitivity in representing the canopy structure and the amount of biomass because CLT is conceptually related to the sky gap fraction. Thus, CLT has the potential to serve as a novel growth parameter for continuous crop growth monitoring.
Why it matches plant phenotyping methodsニンニク圃場でのCLTによるLAI・キャノピー構造・バイオマス推定を、NDVIとの比較および5年間の相関評価で検証しており、植物形質取得手法が中心である。
abstractThis study examined the viability of CLT as a novel crop parameter for monitoring crop growth conditions.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 7 Sept 2026
The field of plant phenotype is used to analyze the shape and physiological characteristics of crops in multiple dimensions. Imaging, using non-destructive optical characteristics of plants, analyzes growth characteristics through spectral data. Among these, fluorescence imaging technology is a method of evaluating the physiological characteristics of crops by inducing plant excitation using a specific light source. Through this, we investigate how fluorescence imaging responds sensitively to environmental stress in garlic and can provide important information on future stress management. In this study, near UV LED (405 nm) was used to induce the fluorescence phenomenon of garlic, and fluorescence images were obtained to classify and evaluate crops exposed to abiotic environmental stress. Physiological characteristics related to environmental stress were developed from fluorescence sample images using the Chlorophyll ratio method, and classification performance was evaluated by developing a classification model based on partial least squares discrimination analysis from the image spectrum for stress identification. The environmental stress classification performance identified from the Chlorophyll ratio was 14.9% in F673/F717, 25.6% in F685/F730, and 0.209% in F690/F735. The spectrum-developed PLS-DA showed classification accuracy of 39.6%, 56.2% and 70.7% in Smoothing, MSV, and SNV, respectively. Spectrum pretreatment-based PLS-DA showed higher discrimination performance than the existing image-based Chlorophyll ratio.
Why it matches plant phenotyping methodsニンニクの環境ストレス状態を蛍光画像から推定し、クロロフィル比とPLS-DAの性能を評価する画像ベース表現型手法が中心である。
abstractfluorescence images were obtained to classify and evaluate crops exposed to abiotic environmental stress.
Garlic (Allium sativum) is an important crop with numerous benefits and uses. This plant is highly exposed to various pathogenic factors, including fungi. Fungi are the most distinct group responsible for planting diseases the loss involved. In this respect, detection of fungal pathogens infection in the early stages is a major challenge in food security to minimize losses as much as possible. Aroma investigation to detect fungal pathogens infection has been widely welcomed in this regard. In the present study, an electronic nose (E-nose) is utilized as a non-destructive and fast method for early detection of fungal infection on garlic that was synthetically infected with Fusariumoxysporum f. sp. Cepae (FU), Alternariaembellisia (syn. Embellisiaallii) (AL), and Botrytis allii (BO). Statistical analyses including ANOVA, PCA, LDA, SVM, and BPNN were employed to evaluate the aroma profile obtained by the E-nose. According to the obtained results, degradation occurs more quickly in the presence of infection. Due to the different destructive effects of each type of infection, the response changes of each sensor toward the aroma of various infection treatments were not the same. Thus, the E-nose can be used as a practical and beneficial tool to detect fungal infection on garlic in the early stages.
Why it matches plant phenotyping methodsE-noseを用いて感染 garlic の状態を非破壊的に早期検出・分類する手法が研究の中心であり、植物病害状態を直接評価している。
Climate change entails increasingly frequent, longer, and more severe droughts, especially in some regions, such as the Mediterranean region. Under these water scarcity conditions, agricultural yields of important crops, such as garlic, are threatened. Finding better adapted cultivars to low water availability environments could help mitigate the negative agricultural and economic impacts of climate change. For this purpose, plant phenotyping protocols based on remote-sensing technologies, such as thermal imaging, can be particularly valuable since they facilitate screening and selection of germplasm in a cost-effective manner, covering a wide range of temporal and spatial scales. In this study, the use of a thermal index known as the crop water stress index (CWSI) was tested as a predictor of bulb biomass and for the assessment of inter-cultivar variability of five garlic cultivars in response to a gradient of soil volumetric water contents (VWCs). Three experimental assays, one in the 2018 season and two in 2019, covering a wide range of water availability levels were carried out. Different linear models were developed, with CWSI and VWCs as continuous predictors of bulb biomass, and the factor cultivar as a categorical predictor. The results support the existence of inter-cultivar variation in terms of sensitivity to water availability. The most productive cultivars under favorable conditions were also the most sensitive to water availability. In contrast, the cultivars with lower bulb production potential displayed lower sensitivity to water availability and higher stability across experimental assays. The results also support that CWSI, which was sensitive to inter-cultivar variability, is a good predictor of garlic bulb biomass. Therefore, CWSI can be a valuable tool for garlic phenotyping and cultivar screening.
Why it matches plant phenotyping methods熱画像から算出したCWSIを用いてニンニクの水ストレスと球根バイオマスを評価し、品種スクリーニングへの有用性を検証しており、フェノタイピング手法が中心である。
abstractplant phenotyping protocols based on remote-sensing technologies, such as thermal imaging, can be particularly valuable since they facilitate screening and selection of germplasm
Garlic is an important economic crop whose planting areas has been annually increasing. Studies have shown that the direction of garlic cloves at the time of sowing has important effects on the germination time, yield and visual appearance of garlic. In order to ensure that the garlic cloves are upright when garlic is planted, an adjustment device based on computer vision has been designed to re-direct garlic cloves. As the garlic clove enters the adjustment device, images are collected by an industrial camera, from which the direction of the clove is identified through image analysis. In order to effectively identify the clove direction in images, a multi-feature algorithm is proposed. This algorithm is found to have higher accuracy than the single-feature recognition method, especially for garlic varieties with large individual differences. Once the clove’s direction is known, the adjustment device manipulates the direction of garlic clove to move it into the ideal planting position. Experimental results showed that the adjustment rate of ‘Jinxiang’ and ‘Cangshan’ garlic was 94.6% and 97.5%, respectively, and the average adjustment time was 1.13 s and 1.24 s, respectively. The proposed method of clove adjustment will not only contribute to improved precision planting outcomes for garlic but could also be extended to other crops whose yield levels are crucially affected by the seed direction.
Why it matches plant phenotyping methodsニンニク鱗片の向きを画像から推定するコンピュータビジョン手法と調整装置が研究の中心であり、植物器官の形態・状態を抽出する方法開発に該当する。
abstractan adjustment device based on computer vision has been designed to re-direct garlic cloves
X-ray high resolution three-dimensional computed tomography (XHR3DCT) is a non-invasive technique to monitor the inner morphology of an object. It permits to obtain a series of horizontal stack of the structure that allows its 3D reconstruction of images by a computer post-processing analysis. This technology is commonly used for medical analysis on human or rarely on animals and its utilization in the plant field has been recently discussed. As we are engaged in the investigation on the possibility to use XHR3DCT for monitoring the storage quality and/or post-harvest development of fresh produces such as vegetables, here we report on minimal demonstration performed on garlic bulbs. In particular, immediately after the harvest from the soil, cloves of garlic bulbs have been maintained under different conditions differed in temperature and humidity, with and without irradiation by red (660 nm) or infra-red (735 nm) lights. At an intermediate time, some cloves have been non-invasively monitored by XHR3DCT to predict the changes in the size (volume) of growing inner shoots (sprouts). To determine the sprout volume based on the XHR3DCT-scanned images, several mathematical approaches have been tested. With approximation of the garlic sprout shape as a parabolic cone, estimation of shoot volume could be readily achieved. By analyzing the inner shoot size in garlic clove kept under different conditions, increase in the shoot size under red light or under higher temperature and relative humidity could be monitored non-invasively, suggesting that XHR3DCT can be used for monitoring of inner structure within the clove of garlic without damaging the samples. Future applications of this technique in during post-harvest managements of a wide range of fresh produces are expected.
Why it matches plant phenotyping methodsニンニク鱗片内部のシュート体積を非破壊X線3D-CT画像から推定する手法を検討し、体積推定アルゴリズムも比較しており、植物形質取得法が中心である。
abstractTo determine the sprout volume based on the XHR3DCT-scanned images, several mathematical approaches have been tested.