Plant Phenomics最新文献

Study on the Optimal Leaf Area-to-Fruit Ratio of Pear Trees on the Basis of Bearing Branch Girdling and Machine Learning. 基于结果枝束缚和机器学习的梨树最佳叶面积与果实比研究

IF 7.6 1区农林科学

Plant Phenomics Pub Date : 2024-08-14 eCollection Date: 2024-01-01 DOI: 10.34133/plantphenomics.0233

Fanhang Zhang, Qi Wang, Haitao Li, Qinyang Zhou, Zhihao Tan, Xiaochao Zu, Xin Yan, Shaoling Zhang, Seishi Ninomiya, Yue Mu, Shutian Tao

{"title":"Study on the Optimal Leaf Area-to-Fruit Ratio of Pear Trees on the Basis of Bearing Branch Girdling and Machine Learning.","authors":"Fanhang Zhang, Qi Wang, Haitao Li, Qinyang Zhou, Zhihao Tan, Xiaochao Zu, Xin Yan, Shaoling Zhang, Seishi Ninomiya, Yue Mu, Shutian Tao","doi":"10.34133/plantphenomics.0233","DOIUrl":"10.34133/plantphenomics.0233","url":null,"abstract":"The leaf area-to-fruit ratio (LAFR) is an important factor affecting fruit quality. Previous studies on LAFR have provided some recommendations for optimal values. However, these recommendations have been quite broad and lack effectiveness during the fruit thinning period. In this study, data on the LAFR and fruit quality of pears at 5 stages were collected by continuously girdling bearing branches throughout the entire fruit development process. Five different clustering algorithms, including KMeans, Agglomerative clustering, Spectral clustering, Birch, and Spectral biclustering, were employed to classify the fruit quality data. Agglomerative clustering yielded the best results when the dataset was divided into 4 clusters. The least squares method was utilized to fit the LAFR corresponding to the best quality cluster, and the optimal LAFR values for 28, 42, 63, 91, and 112 days after flowering were 12.54, 18.95, 23.79, 27.06, and 28.76 dm2 (the corresponding leaf-to-fruit ratio values were 19, 29, 36, 41, and 44, respectively). Furthermore, field verification experiments demonstrated that the optimal LAFR contributed to improving pear fruit quality, and a relatively high LAFR beyond the optimum value did not further increase quality. In summary, we optimized the LAFR of pear trees at different stages and confirmed the effectiveness of the optimal LAFR in improving fruit quality. Our research provides a theoretical basis for managing pear tree fruit load and achieving high-quality, clean fruit production.","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11322523/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141983038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-Resolution Disease Phenotyping Reveals Distinct Resistance Mechanisms of Tomato Crop Wild Relatives against Sclerotinia sclerotiorum. 高分辨率病害表型分析揭示番茄作物野生近缘种对硬核菌的不同抗性机制

IF 7.6 1区农林科学

Plant Phenomics Pub Date : 2024-08-05 eCollection Date: 2024-01-01 DOI: 10.34133/plantphenomics.0214

Severin Einspanier, Christopher Tominello-Ramirez, Mario Hasler, Adelin Barbacci, Sylvain Raffaele, Remco Stam

{"title":"High-Resolution Disease Phenotyping Reveals Distinct Resistance Mechanisms of Tomato Crop Wild Relatives against Sclerotinia sclerotiorum.","authors":"Severin Einspanier, Christopher Tominello-Ramirez, Mario Hasler, Adelin Barbacci, Sylvain Raffaele, Remco Stam","doi":"10.34133/plantphenomics.0214","DOIUrl":"10.34133/plantphenomics.0214","url":null,"abstract":"Besides the well-understood qualitative disease resistance, plants possess a more complex quantitative form of resistance: quantitative disease resistance (QDR). QDR is commonly defined as a partial but more durable form of resistance and, therefore, might display a valuable target for resistance breeding. The characterization of QDR phenotypes, especially of wild crop relatives, displays a bottleneck in deciphering QDR's genomic and regulatory background. Moreover, the relationship between QDR parameters, such as infection frequency, lag-phase duration, and lesion growth rate, remains elusive. High hurdles for applying modern phenotyping technology, such as the low availability of phenotyping facilities or complex data analysis, further dampen progress in understanding QDR. Here, we applied a low-cost (<1.000 €) phenotyping system to measure lesion growth dynamics of wild tomato species (e.g., Solanum pennellii or Solanum pimpinellifolium). We provide insight into QDR diversity of wild populations and derive specific QDR mechanisms and their cross-talk. We show how temporally continuous observations are required to dissect end-point severity into functional resistance mechanisms. The results of our study show how QDR can be maintained by facilitating different defense mechanisms during host-parasite interaction and that the capacity of the QDR toolbox highly depends on the host's genetic context. We anticipate that the present findings display a valuable resource for more targeted functional characterization of the processes involved in QDR. Moreover, we show how modest phenotyping technology can be leveraged to help answer highly relevant biological questions.","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11298253/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141894083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition. 用于植物病害识别的局部和全局特征感知双分支网络

IF 7.6 1区农林科学

Plant Phenomics Pub Date : 2024-07-31 eCollection Date: 2024-01-01 DOI: 10.34133/plantphenomics.0208

Jianwu Lin, Xin Zhang, Yongbin Qin, Shengxian Yang, Xingtian Wen, Tomislav Cernava, Quirico Migheli, Xiaoyulong Chen

{"title":"Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition.","authors":"Jianwu Lin, Xin Zhang, Yongbin Qin, Shengxian Yang, Xingtian Wen, Tomislav Cernava, Quirico Migheli, Xiaoyulong Chen","doi":"10.34133/plantphenomics.0208","DOIUrl":"10.34133/plantphenomics.0208","url":null,"abstract":"Accurate identification of plant diseases is important for ensuring the safety of agricultural production. Convolutional neural networks (CNNs) and visual transformers (VTs) can extract effective representations of images and have been widely used for the intelligent recognition of plant disease images. However, CNNs have excellent local perception with poor global perception, and VTs have excellent global perception with poor local perception. This makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition tasks. In this paper, we propose a local and global feature-aware dual-branch network, named LGNet, for the identification of plant diseases. More specifically, we first design a dual-branch structure based on CNNs and VTs to extract the local and global features. Then, an adaptive feature fusion (AFF) module is designed to fuse the local and global features, thus driving the model to dynamically perceive the weights of different features. Finally, we design a hierarchical mixed-scale unit-guided feature fusion (HMUFF) module to mine the key information in the features at different levels and fuse the differentiated information among them, thereby enhancing the model's multiscale perception capability. Subsequently, extensive experiments were conducted on the AI Challenger 2018 dataset and the self-collected corn disease (SCD) dataset. The experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the AI Challenger 2018 dataset and the SCD dataset, with accuracies of 88.74% and 99.08%, respectively.","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11315374/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141917345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Phenotyping of Drought-Stressed Poplar Saplings Using Exemplar-Based Data Generation and Leaf-Level Structural Analysis. 利用基于范例的数据生成和叶片级结构分析对干旱胁迫杨树幼苗进行表型。

IF 7.6 1区农林科学

Plant Phenomics Pub Date : 2024-07-29 eCollection Date: 2024-01-01 DOI: 10.34133/plantphenomics.0205

Lei Zhou, Huichun Zhang, Liming Bian, Ye Tian, Haopeng Zhou

{"title":"Phenotyping of Drought-Stressed Poplar Saplings Using Exemplar-Based Data Generation and Leaf-Level Structural Analysis.","authors":"Lei Zhou, Huichun Zhang, Liming Bian, Ye Tian, Haopeng Zhou","doi":"10.34133/plantphenomics.0205","DOIUrl":"10.34133/plantphenomics.0205","url":null,"abstract":"Drought stress is one of the main threats to poplar plant growth and has a negative impact on plant yield. Currently, high-throughput plant phenotyping has been widely studied as a rapid and nondestructive tool for analyzing the growth status of plants, such as water and nutrient content. In this study, a combination of computer vision and deep learning was used for drought-stressed poplar sapling phenotyping. Four varieties of poplar saplings were cultivated, and 5 different irrigation treatments were applied. Color images of the plant samples were captured for analysis. Two tasks, including leaf posture calculation and drought stress identification, were conducted. First, instance segmentation was used to extract the regions of the leaf, petiole, and midvein. A dataset augmentation method was created for reducing manual annotation costs. The horizontal angles of the fitted lines of the petiole and midvein were calculated for leaf posture digitization. Second, multitask learning models were proposed for simultaneously determining the stress level and poplar variety. The mean absolute errors of the angle calculations were 10.7° and 8.2° for the petiole and midvein, respectively. Drought stress increased the horizontal angle of leaves. Moreover, using raw images as the input, the multitask MobileNet achieved the highest accuracy (99% for variety identification and 76% for stress level classification), outperforming widely used single-task deep learning models (stress level classification accuracies of <70% on the prediction dataset). The plant phenotyping methods presented in this study could be further used for drought-stress-resistant poplar plant screening and precise irrigation decision-making.","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11283870/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141793178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CSNet: A Count-supervised Network via Multiscale MLP-Mixer for Wheat Ear Counting CSNet：通过多尺度 MLP-Mixer 的计数监督网络进行麦穗计数

IF 7.6 1区农林科学

Plant Phenomics Pub Date : 2024-07-25 DOI: 10.34133/plantphenomics.0236

Yaoxi Li, Xingcai Wu, Qi Wang, Zhixun Pei, Kejun Zhao, Panfeng Chen, Gefei Hao

引用次数: 0

Deep learning methods using imagery from a smartphone for recognizing sorghum panicles and counting grains at a plant level 利用智能手机图像的深度学习方法，识别高粱圆锥花序并在植株层面计算谷粒数量

IF 7.6 1区农林科学

Plant Phenomics Pub Date : 2024-07-24 DOI: 10.34133/plantphenomics.0234

Gustavo Nocera Santiago, Pedro Henrique Magalhaes Cisdeli, Ana J. P. Carcedo, L. Marziotte, Laura Mayor, Ignacio A. Ciampitti

引用次数: 0

MTSC-Net: A Semi-Supervised Counting Network for Estimating the Number of Slash Pine New Shoots MTSC-Net：用于估算斜纹松新芽数量的半监督计数网络

IF 7.6 1区农林科学

Plant Phenomics Pub Date : 2024-07-24 DOI: 10.34133/plantphenomics.0228

Zhaoxu Zhang, Yanjie Li, Yue Cao, Yu Wang, Xuchao Guo, Xia Hao

引用次数: 0

Phenomic Selection for Hybrid Rapeseed Breeding. 杂交油菜籽育种的表型选择。

IF 7.6 1区农林科学

Plant Phenomics Pub Date : 2024-07-24 eCollection Date: 2024-01-01 DOI: 10.34133/plantphenomics.0215

Lennard Roscher-Ehrig, Sven E Weber, Amine Abbadi, Milka Malenica, Stefan Abel, Reinhard Hemker, Rod J Snowdon, Benjamin Wittkop, Andreas Stahl

{"title":"Phenomic Selection for Hybrid Rapeseed Breeding.","authors":"Lennard Roscher-Ehrig, Sven E Weber, Amine Abbadi, Milka Malenica, Stefan Abel, Reinhard Hemker, Rod J Snowdon, Benjamin Wittkop, Andreas Stahl","doi":"10.34133/plantphenomics.0215","DOIUrl":"https://doi.org/10.34133/plantphenomics.0215","url":null,"abstract":"Phenomic selection is a recent approach suggested as a low-cost, high-throughput alternative to genomic selection. Instead of using genetic markers, it employs spectral data to predict complex traits using equivalent statistical models. Phenomic selection has been shown to outperform genomic selection when using spectral data that was obtained within the same generation as the traits that were predicted. However, for hybrid breeding, the key question is whether spectral data from parental genotypes can be used to effectively predict traits in the hybrid generation. Here, we aimed to evaluate the potential of phenomic selection for hybrid rapeseed breeding. We performed predictions for various traits in a structured population of 410 test hybrids, grown in multiple environments, using near-infrared spectroscopy data obtained from harvested seeds of both the hybrids and their parental lines with different linear and nonlinear models. We found that phenomic selection within the hybrid generation outperformed genomic selection for seed yield and plant height, even when spectral data was collected at single locations, while being less affected by population structure. Furthermore, we demonstrate that phenomic prediction across generations is feasible, and selecting hybrids based on spectral data obtained from parental genotypes is competitive with genomic selection. We conclude that phenomic selection is a promising approach for rapeseed breeding that can be easily implemented without any additional costs or efforts as near-infrared spectroscopy is routinely assessed in rapeseed breeding.","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11268845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141763988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating Neural Radiance Fields (NeRFs) for 3D Plant Geometry Reconstruction in Field Conditions 评估神经辐射场 (NeRF) 在田间条件下用于三维植物几何重建的效果

IF 7.6 1区农林科学

Plant Phenomics Pub Date : 2024-07-24 DOI: 10.34133/plantphenomics.0235

Muhammad Arbab Arshad, T. Jubery, James Afful, Anushrut Jignasu, Aditya Balu, B. Ganapathysubramanian, Soumik Sarkar, A. Krishnamurthy

引用次数: 0

Characterization and Identification of NPK Stress in Rice Using Terrestrial Hyperspectral Images. 利用陆地高光谱图像对水稻氮磷钾胁迫进行特征描述和识别

IF 7.6 1区农林科学

Plant Phenomics Pub Date : 2024-07-24 eCollection Date: 2024-01-01 DOI: 10.34133/plantphenomics.0197

Jinfeng Wang, Yuhang Chu, Guoqing Chen, Minyi Zhao, Jizhuang Wu, Ritao Qu, Zhentao Wang

{"title":"Characterization and Identification of NPK Stress in Rice Using Terrestrial Hyperspectral Images.","authors":"Jinfeng Wang, Yuhang Chu, Guoqing Chen, Minyi Zhao, Jizhuang Wu, Ritao Qu, Zhentao Wang","doi":"10.34133/plantphenomics.0197","DOIUrl":"https://doi.org/10.34133/plantphenomics.0197","url":null,"abstract":"Due to nutrient stress, which is an important constraint to the development of the global agricultural sector, it is now vital to timely evaluate plant health. Remote sensing technology, especially hyperspectral imaging technology, has evolved from spectral response modes to pattern recognition and vegetation monitoring. This study established a hyperspectral library of 14 NPK (nitrogen, phosphorus, potassium) nutrient stress conditions in rice. The terrestrial hyperspectral camera (SPECIM-IQ) collected 420 rice stress images and extracted as well as analyzed representative spectral reflectance curves under 14 stress modes. The canopy spectral profile characteristics, vegetation index, and principal component analysis demonstrated the differences in rice under different nutrient stresses. A transformer-based deep learning network SHCFTT (SuperPCA-HybridSN-CBAM-Feature tokenization transformer) was established for identifying nutrient stress patterns from hyperspectral images while being compared with classic support vector machines, 1D-CNN (1D-Convolutional Neural Network), and 3D-CNN. The total accuracy of the SHCFTT model under different modeling strategies and different years ranged from 93.92% to 100%, indicating the positive effect of the proposed method on improving the accuracy of identifying nutrient stress in rice.","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11266478/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141760503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0