This study presents an unmanned aerial vehicle (UAV)-based approach for estimating shoot biomass and characterizing growth patterns in single-staked white Guinea yams ( Dioscorea rotundata ). Multi-angle aerial images from nadir and oblique views were used to extract vegetation- and height-related indices that served as predictors in machine learning models. Support vector regression using combined-view imagery provided the highest prediction accuracy (R² = 0.79) and remained robust across growth stages, years, fertilizer treatments, and genotypes. Notably, the combined-view configuration outperformed single-view imaging, demonstrating the advantage of capturing complementary canopy-structure information in complex staked-vine canopies. Time-series biomass estimates enabled the fitting of genotype-specific Richards growth curves using Bayesian inference. Significant genotypic variations were observed in parameters associated with maximum biomass and early growth rate, whereas phenology-related parameters showed comparatively minimal differences. These parameter differences may reflect variation in canopy architecture and growth allocation among genotypes. Overall, this integrated workflow provides a scalable tool for nondestructive monitoring of yam growth dynamics and for summarizing biomass trajectories with interpretable parameters, supporting breeding efforts aimed at improving yam productivity and yield stability across diverse cultivation conditions.
Why it matches plant phenotyping methodsUAV画像からヤムのシュートバイオマスと生育を推定する画像解析・機械学習ワークフローが研究の中心であり、複数視点画像の比較検証と時系列形質推定を行っている。
abstractThis study presents an unmanned aerial vehicle (UAV)-based approach for estimating shoot biomass and characterizing growth patterns
Introduction Yam is an important medicinal and edible crop, but its quality and yield are greatly affected by leaf diseases. Currently, research on yam leaf disease segmentation remains unexplored. Challenges like leaf overlapping, uneven lighting and irregular disease spots in complex environments limit segmentation accuracy. Methods To address these challenges, this paper introduces the first yam leaf disease segmentation dataset and proposes BiSeNeXt, an enhanced method based on BiSeNetV2. Firstly, dynamic feature extraction block (DFEB) enhances the precision of leaf and disease edge pixels and reduces lesion omission through dynamic receptive-field convolution (DRFConv) and pixel shuffle (PixelShuffle) downsampling. Secondly, efficient asymmetric multi-scale attention (EAMA) effectively alleviates the problem of lesion adhesion by combining asymmetric convolution with a multi-scale parallel structure. Finally, PointRefine decoder adaptively selects uncertain points in the image predictions and refines them point-by-point, producing accurate segmentation of leaves and spots. Results Experimental results indicated that the approach achieved a 97.04% intersection over union (IoU) for leaf segmentation and an 84.75% IoU for disease segmentation. Compared to DeepLabV3+, the proposed method improves the IoU of leaf and disease segmentation by 2.22% and 5.58%, respectively. Additionally, the FLOPs and total number of parameters of the proposed method require only 11.81% and 7.81% of DeepLabV3+, respectively. Discussion Therefore, the proposed method can efficiently and accurately extract yam leaf spots in complex scenes, providing a solid foundation for analyzing yam leaves and diseases.
Why it matches plant phenotyping methodsヤム葉と病斑を画像から分割・抽出する手法とデータセットを開発し、性能比較まで行っており、植物の病害状態を取得する方法が中心である。
abstractthis paper introduces the first yam leaf disease segmentation dataset and proposes BiSeNeXt, an enhanced method based on BiSeNetV2.
Reproduction assets foundThe paper's authors publicly released their self-constructed yam leaf disease segmentation dataset (1,097 annotated images of anthracnose, brown spot, and gray spot) via a Google Drive link in the Data availability statement. No code or trained model deposit is explicitly stated.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://drive.google.com/drive/folders/1_ojcb_84TMbkZwYfm0dgsL1NjiGw7GRF?usp=sharing .Open asset ↗lines:1046-1093Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Jun 2024Journal of the science of food and agriculture.
Why it matches plant phenotyping methodsヤム塊茎品質のアミロース含量をNIRSとPLS/CNNで推定する方法を開発し、独立データセットで検証しており、植物育種向けの高スループット表現型計測が中心である。
abstractTwo calibration methods were developed and validated on an independent dataset: partial least squares (PLS) and convolutional neural networks (CNN).
This study introduces a comprehensive framework aimed at automating the process of detecting yam tuber quality attributes. This is achieved through the integration of Internet of Things (IoT) devices and robotic systems. The primary focus of the study is the development of specialized computer codes that extract relevant image features and categorize yam tubers into one of three classes: "Good," "Diseased," or "Insect Infected." By employing a variety of machine learning algorithms, including tree algorithms, support vector machines (SVMs), and k-nearest neighbors (KNN), the codes achieved an impressive accuracy of over 90% in effective classification. Furthermore, a robotic algorithm was designed utilizing an artificial neural network (ANN), which exhibited a 92.3% accuracy based on its confusion matrix analysis. The effectiveness and accuracy of the developed codes were substantiated through deployment testing. Although a few instances of misclassification were observed, the overall outcomes indicate significant potential for transforming yam quality assessment and contributing to the realm of precision agriculture. This study is in alignment with prior research endeavors within the field, highlighting the pivotal role of automated and precise quality assessment. The integration of IoT devices and robotic systems in agricultural practices presents exciting possibilities for data-driven decision-making and heightened productivity. By minimizing human intervention and providing real-time insights, the study approach has the potential to optimize yam quality assessment processes. Therefore, this study successfully demonstrates the practical application of IoT and robotic technologies for the purpose of yam quality detection, laying the groundwork for progress in the agricultural sector.
Why it matches plant phenotyping methodsヤム塊茎の品質・病害・虫害状態を画像特徴から分類するコンピュータビジョン手法を開発し、機械学習およびロボット実装で精度検証しており、植物状態の取得・判定が中心である。
abstractThis study introduces a comprehensive framework aimed at automating the process of detecting yam tuber quality attributes.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
Why it matches plant phenotyping methodsヤム塊茎の品質形質であるアミロース含量をNIRSとCNNで推定する方法を開発し、独立データセットで検証しており、植物表現型取得法が研究の中心である。
abstractTwo calibration methods were developed and validated on an independent dataset: partial least squares (PLS) and convolutional neural networks (CNN).
Yam ( Dioscorea spp.) plants are mostly dioecious and sometimes monoecious. Low, irregular, and asynchronous flowering of the genotypes are critical problems in yam breeding. Selecting suitable pollen parents and preserving yam pollen for future use are potential means of controlling these constraints and optimizing hybridization practice in yam breeding programs. However, implementing such procedures requires a robust protocol for pollen collection and viability testing to monitor pollen quality in the field and in storage. This study, therefore, aimed at optimizing the pollen germination assessment protocol for yam. The standard medium composition was stepwisely modified, the optimal growth condition was tested, and in vivo predictions were made. This study showed that the differences in yam pollen germination percentage are primarily linked to the genotype and growing conditions (i.e., medium viscosity, incubation temperature, and time to use) rather than the medium composition. The inclusion of polyethylene glycol (PEG) in the culture medium caused 67-75% inhibition of germination in D. alata . Although the in vivo fertilization was dependent on female parents, the in vitro germination test predicted the percentage fruit set at 25.2-79.7% and 26.4-59.7% accuracy for D. rotundata and D. alata genotypes, respectively. This study provides a reliable in vitro yam pollen germination protocol to support pollen management and preservation efforts in yam breeding.
Why it matches plant phenotyping methodsヤムの花粉発芽率を測定する in vitro 評価プロトコルの最適化と、受精・結実率による予測検証が研究の中心であり、植物形質の取得方法に該当する。
abstractThis study, therefore, aimed at optimizing the pollen germination assessment protocol for yam.
The review aimed to identify the different high‐throughput phenotyping (HTP) techniques that used for quality evaluation in cassava and yam breeding programmes, and this has provided insights towards the development of metrics and their application in cassava and yam improvements. A systematic review of the published research articles involved the use of NIRS in analysing the quality traits of cassava and yam was carried out, and Scopus, Science Direct, Web of Sciences and Google Scholar were searched. The results of the review established that NIRS could be used in understanding the chemical constituents (carbohydrate, protein, vitamins, minerals, carotenoids, moisture, starch, etc.) for high‐throughput phenotyping. This study provides preliminary evidence of the application of NIRS as an efficient and affordable procedure for HTP. However, the feasibility of using mid‐infrared spectroscopy (MIRS) and hyperspectral imaging (HSI) in combination with the NIRS could be further studied for quality traits phenotyping.
Why it matches plant phenotyping methodsNIRSによる作物品質形質のハイスループット表現型計測を主題とするレビューであり、方法の適用可能性と他の分光技術との組合せを検討しているため。
titleNear‐infrared spectroscopy applications for high‐throughput phenotyping for cassava and yam: A review
New management practices must be developed to improve yam productivity. By allowing non-destructive analyses of important plant traits, image-based phenotyping techniques could help developing such practices. Our objective was to determine the potential of image-based phenotyping methods to assess traits relevant for tuber yield formation in yam grown in the glasshouse and in the field. We took plant and leaf pictures with consumer cameras. We used the numbers of image pixels to derive the shoot biomass and the total leaf surface and calculated the ‘triangular greenness index’ (TGI) which is an indicator of the leaf chlorophyll content. Under glasshouse conditions, the number of pixels obtained from nadir view (view from the top) was positively correlated to shoot biomass, and total leaf surface, while the TGI was negatively correlated to the SPAD values and nitrogen (N) content of diagnostic leaves. Pictures taken from nadir view in the field showed an increase in soil surface cover and a decrease in TGI with time. TGI was negatively correlated to SPAD values measured on diagnostic leaves but was not correlated to leaf N content. In conclusion, these phenotyping techniques deliver relevant results but need to be further developed and validated for application in yam.
Why it matches plant phenotyping methodsヤムの成長・窒素栄養状態を画像から推定する手法の評価と妥当性検証が研究の中心であり、植物形質の取得方法を扱っている。
abstractOur objective was to determine the potential of image-based phenotyping methods to assess traits relevant for tuber yield formation in yam grown in the glasshouse and in the field.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
Management practices must be developed to improve yam production sustainability. Image-based phenotyping techniques could help developing such practices based on non-destructive analyses of important plant traits. Our objective was to determine the potential of image-based phenotyping methods to assess traits relevant for tuber yield formation in yam grown in glasshouse and field. We took plant and leaf pictures with consumer cameras. We used the numbers of image pixels to derive the shoot biomass and the total leaf surface and calculated the ‘triangular greenness index’ (TGI) which is an indicator of the plant nitrogen (N) nutritional status. Under glasshouse conditions, the number of pixels obtained from nadir view (image taken top down) was positively correlated to the shoot biomass, and the total leaf surface, while the TGI was negatively correlated to the N content of diagnostic leaves. Under field conditions, pictures taken from the nadir view showed an increase in soil surface cover and a decrease in TGI with time. TGI was negatively correlated to SPAD measured on specific leaves but was not correlated to the N content of these leaves. In conclusion, these phenotyping techniques deliver relevant results but need to be further developed and validated for application in yam.
Why it matches plant phenotyping methods画像ベース表現型解析によりヤムのバイオマス、葉面積、窒素栄養状態を推定し、温室・圃場で相関評価と妥当性検証を行っており、表現型取得法が中心である。
abstractOur objective was to determine the potential of image-based phenotyping methods to assess traits relevant for tuber yield formation in yam grown in glasshouse and field.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
The review aimed to identify the different high-throughput phenotyping (HTP) techniques that used for quality evaluation in cassava and yam breeding programmes, and this has provided insights towards the development of metrics and their application in cassava and yam improvements. A systematic review of the published research articles involved the use of NIRS in analysing the quality traits of cassava and yam was carried out, and Scopus, Science Direct, Web of Sciences and Google Scholar were searched. The results of the review established that NIRS could be used in understanding the chemical constituents (carbohydrate, protein, vitamins, minerals, carotenoids, moisture, starch, etc.) for high-throughput phenotyping. This study provides preliminary evidence of the application of NIRS as an efficient and affordable procedure for HTP. However, the feasibility of using mid-infrared spectroscopy (MIRS) and hyperspectral imaging (HSI) in combination with the NIRS could be further studied for quality traits phenotyping.
Why it matches plant phenotyping methodsキャッサバとヤムの育種におけるNIRSを用いた高スループット表現型解析を主題とするレビューであり、品質形質の測定技術を中心に扱っている。
titleNear-infrared spectroscopy applications for high-throughput phenotyping for cassava and yam: A review.
Methods: for high-quality DNA extraction and knowledge of sex expression and flowering time are essential for applying genomic-assisted breeding and improve the success with hybridization in Guinea yam. A dioecious or monoecious pattern of flowering and sometimes non-flowering is a common phenomenon within and between the Dioscorea species. The flowering in yam plants raised from botanical seeds often takes an extended period, mostly till the first clonal generation after propagation from the tubers. The prolonged process of testing required to identify plant sex and flowering intensity in yam breeding often poses a challenge to realize reduced breeding cycle and apply genomic selection. This study assessed sample preservation methods for DNA quality during extraction and potential of DNA marker to diagnose plant sex at the early seedling stage in white Guinea yam. The predicted sex at the seedling stage was further validated with the visual score for the sex phenotype at the flowering stage. DNA extracted from leaf samples preserved in liquid nitrogen, silica gel, dry ice, and oven drying methods was similar in quality with a high molecular weight than samples stored in ethanol solution. Yam plant sex diagnosis with the DNA marker (sp16) identified a higher proportion of ZW genotypes (female or monoecious phenotypes) than the ZZ genotypes (male phenotype) in the studied materials with 74% prediction accuracy. The results from this study provided valuable insights on suitable sample preservation methods for quality DNA extraction and the potential of DNA marker sp16 to predict sex in white Guinea yam.
Why it matches plant phenotyping methodsヤムの性表現型を幼苗段階でDNAマーカーにより予測し、開花期の視覚的性表現型で検証しているため、単なる生物学的測定ではなく植物表現型推定法の開発・検証が中心です。
abstractYam plant sex diagnosis with the DNA marker (sp16) identified a higher proportion of ZW genotypes (female or monoecious phenotypes) than the ZZ genotypes (male phenotype) in the studied materials with 74% prediction accuracy.
Crop phenotyping is a key process used to accelerate breeding programs in the era of high-throughput genotyping. However, most rapid phenotyping methods developed to date have focused on major cereals or legumes, and their application to minor crops has been delayed. In this study, we developed a non-destructive method to predict shoot biomass by measuring spectral reflectance in staking yam (Dioscorea rotundata). The normalized difference vegetation index (NDVI) was evaluated using a handheld sensor that was vertically scanned from the top to the bottom of a plant alongside the stake. A linear regression model was constructed to predict shoot biomass through Bayesian analysis using NDVI as a parameter. The model well predicted the observed values of shoot biomass, irrespective of the growth stage and genotypes. Conversely, the model tended to underestimate the shoot biomass when the actual shoot biomass exceeded 150 g plant−1; this was compensated for when the parameter green area, calculated from plant image, was included in the model. This method reduced the time, cost, effort, and field space needed for shoot biomass evaluation compared with that needed for the sampling method, enabling shoot biomass phenotyping for a large population of plants. A total of 210 cross-populated plants were evaluated, and a correlation analysis was performed between the predicted shoot biomass and tuber yield. In addition to the prediction of tuber yield, this method could also be applied for the evaluation of crop models and stress tolerance, as well as for genetic analyses.
Why it matches plant phenotyping methods携帯型NDVIセンサーと画像由来の緑色面積を用いてヤムの地上部バイオマスを非破壊推定する手法を開発・検証しており、植物表現型取得が研究の中心です。
abstractIn this study, we developed a non-destructive method to predict shoot biomass by measuring spectral reflectance in staking yam (Dioscorea rotundata).