Search Results (1,377)

Soybean is a crucial crop globally, serving as a significant source of unsaturated fatty acids and protein in the human diet. However, further enhancements are required for the related genes that regulate soybean oil synthesis. In this study, 155 soybean germplasms were cultivated under three different environmental conditions, followed by phenotypic identification and genome-wide association analysis using simplified sequencing data. Genome-wide association analysis was performed using SLAF-seq data. A total of 36 QTLs were significantly associated with oil content (−log10(p) > 3). Out of the 36 QTLs associated with oil content, 27 exhibited genetic overlap with previously reported QTLs related to oil traits. Further transcriptome sequencing was performed on extreme high–low oil soybean varieties. Combined with transcriptome expression data, 22 candidate genes were identified (|log2FC| ≥ 3). Further haplotype analysis of the potential candidate genes showed that three potential candidate genes had excellent haplotypes, including Glyma.03G186200, Glyma.09G099500, and Glyma.18G248900. The identified loci harboring beneficial alleles and candidate genes likely contribute significantly to the molecular network’s underlying marker-assisted selection (MAS) and oil content. Full article

In China, saline–alkali lands constitute 5.01% of the total land area, having a significant impact on both domestic and international food production. Rapeseed (Brassica napus L.), as one of the most important oilseed crops in China, has garnered considerable attention due to its potential adaptability to saline conditions. Breeding and improving salt-tolerant varieties is a key strategy for the effective utilization of saline lands. Hence, it is important to conduct comprehensive research into the adaptability and salt tolerance mechanisms of Brassica napus in saline environments as well as to breed novel salt-tolerant varieties. This review summarizes the molecular mechanism of salt tolerance, physiological and phenotypic indexes, research strategies for the screening of salt-tolerant germplasm resources, and genetic engineering tools for salt stress in Brassica napus. It also introduces various agronomic strategies for applying exogenous substances to alleviate salt stress and provide technological tools and research directions for future research on salt tolerance in Brassica napus. Full article

Maize, an important cereal crop and crucial industrial material, is widely used in various fields, including food, feed, and industry. Maize is also a highly adaptable crop, capable of thriving under various climatic and soil conditions. Against the backdrop of intensified climate change, studying the classification of maize growth stages can aid in adjusting planting strategies to enhance yield and quality. Accurate classification of the growth stages of maize breeding materials is important for enhancing yield and quality in breeding endeavors. Traditional remote sensing-based crop growth stage classifications mainly rely on time series vegetation index (VI) analyses; however, VIs are prone to saturation under high-coverage conditions. Maize phenotypic traits at different growth stages may improve the accuracy of crop growth stage classifications. Therefore, we developed a method for classifying maize growth stages during the vegetative growth phase by combining maize phenotypic traits with different classification algorithms. First, we tested various VIs, texture features (TFs), and combinations of VI and TF as input features to estimate the leaf chlorophyll content (LCC), leaf area index (LAI), and fractional vegetation cover (FVC). We determined the optimal feature inputs and estimation methods and completed crop height (CH) extraction. Then, we tested different combinations of maize phenotypic traits as input variables to determine their accuracy in classifying growth stages and to identify the optimal combination and classification method. Finally, we compared the proposed method with traditional growth stage classification methods based on remote sensing VIs and machine learning models. The results indicate that (1) when the VI+TFs are used as input features, random forest regression (RFR) shows a good estimation performance for the LCC (R²: 0.920, RMSE: 3.655 SPAD units, MAE: 2.698 SPAD units), Gaussian process regression (GPR) performs well for the LAI (R²: 0.621, RMSE: 0.494, MAE: 0.397), and linear regression (LR) exhibits a good estimation performance for the FVC (R²: 0.777, RMSE: 0.051, MAE: 0.040); (2) when using the maize LCC, LAI, FVC, and CH phenotypic traits to classify maize growth stages, the random forest (RF) classification method achieved the highest accuracy (accuracy: 0.951, precision: 0.951, recall: 0.951, F1: 0.951); and (3) the effectiveness of the growth stage classification based on maize phenotypic traits outperforms that of traditional remote sensing-based crop growth stage classifications. Full article

The number of grains per sea rice panicle is an important parameter directly related to rice yield, and it is also a very important agronomic trait in research related to sea rice breeding. However, the grain number per sea rice panicle still mainly relies on manual calculation, which has the disadvantages of being time-consuming, error-prone, and labor-intensive. In this study, a novel method was developed for the automatic calculation of the grain number per rice panicle based on a deep convolutional neural network. Firstly, some sea rice panicle images were collected in complex field environment and annotated to establish the sea rice panicle image data set. Then, a sea grain detection model was developed using the Faster R-CNN embedded with a feature pyramid network (FPN) for grain identification and location. Also, ROI Align was used to replace ROI pooling to solve the problem of relatively large deviations in the prediction frame when the model detected small grains. Finally, the mAP (mean Average Precision) and accuracy of the sea grain detection model were 90.1% and 94.9%, demonstrating that the proposed method had high accuracy in identifying and locating sea grains. The sea rice grain detection model can quickly and accurately predict the number of grains per panicle, providing an effective, convenient, and low-cost tool for yield evaluation, crop breeding, and genetic research. It also has great potential in assisting phenotypic research. Full article

The dimensions of organs such as flowers, leaves, and seeds are governed by processes of cellular proliferation and expansion. In soybeans, the dimensions of these organs exhibit a strong correlation with crop yield, quality, and other phenotypic traits. Nevertheless, there exists a scarcity of research concerning the regulatory genes influencing flower size, particularly within the soybean species. In this study, 309 samples of 3 soybean types (123 cultivar, 90 landrace, and 96 wild) were re-sequenced. The microscopic phenotype of soybean flower organs was photographed using a three-eye microscope, and the phenotypic data were extracted by means of computer vision. Pearson correlation analysis was employed to assess the relationship between petal and seed phenotypes, revealing a strong correlation between the sizes of these two organs. Through GWASs, SNP loci significantly associated with flower organ size were identified. Subsequently, haplotype analysis was conducted to screen for upstream and downstream genes of these loci, thereby identifying potential candidate genes. In total, 77 significant SNPs associated with vexil petals, 562 significant SNPs associated with wing petals, and 34 significant SNPs associated with keel petals were found. Candidate genes were screened by candidate sites, and haplotype analysis was performed on the candidate genes. Finally, the present investigation yielded 25 and 10 genes of notable significance through haplotype analysis in the vexil and wing regions, respectively. Notably, Glyma.07G234200, previously documented for its high expression across various plant organs, including flowers, pods, leaves, roots, and seeds, was among these identified genes. The research contributes novel insights to soybean breeding endeavors, particularly in the exploration of genes governing organ development, the selection of field materials, and the enhancement of crop yield. It played a role in the process of material selection during the growth period and further accelerated the process of soybean breeding material selection. Full article

Crop diseases pose a major threat to agricultural production, quality, and sustainable development, highlighting the importance of early disease risk prediction for effective disease control. Tea anthracnose can easily occur in Yunnan under high-temperature and high-humidity environments, which seriously affects the ecosystem of tea gardens. Therefore, the establishment of accurate, non-destructive, and rapid prediction models has a positive impact on the conservation of biodiversity in tea plantations. Because of the linear relationship between disease occurrence and environmental conditions, the growing environmental conditions can be effectively used to predict crop diseases. Based on the climate data collected by Internet of Things devices, this study uses LASSO-COX-NOMOGRAM to analyze the expression of tea anthracrum to different degrees through Limma difference analysis, and it combines Cox single-factor analysis to study the influence mechanism of climate and environmental change on tea anthracrum. Modeling factors were screened by LASSO regression, 10-fold cross-validation and Cox multi-factor analysis were used to establish the basis of the model, the nomogram prediction model was constructed, and a Shiny- and DynNOM-visualized prediction system was built. The experimental results showed that the AUC values of the model were 0.745 and 0.731 in the training set and 0.75 and 0.747 in the verification set, respectively, when the predicted change in tea anthracnose disease risk was greater than 30% and 60%, and the calibration curve was in good agreement with the ideal curve. The accuracy of external verification was 83.3% for predicting tea anthracnose of different degrees. At the same time, compared with the traditional prediction method, the method is not affected by the difference in leaf background, which provides research potential for early prevention and phenotypic analysis, and also provides an effective means for tea disease identification and harm analysis. Full article

Cassava (Manihot esculenta Crantz) was introduced to Southeast Asia in the 16th–17th centuries and has since flourished as an industrial crop. Since the 1980s, Thailand has emerged as the leading producer and exporter of cassava products. This growth coincided with the initiation of cassava breeding programs in collaboration with the International Center for Tropical Agriculture (CIAT), focusing on root yield and starch production. The success of Thai cassava breeding programs can be attributed to the incorporation of valuable genetic diversity from international germplasm resources to cross with the local landraces, which has become the genetic foundation of many Thai commercial varieties. Effective evaluation under diverse environmental conditions has led to the release of varieties with high yield stability. A notable success is the development of Kasetsart 50. However, extreme climate change poses significant challenges, including abiotic and biotic stresses that threaten cassava root yield and starch content, leading to a potential decline in starch-based industries. Future directions for cassava breeding must include hybrid development, marker-assisted recurrent breeding, and gene editing, along with high-throughput phenotyping and flower induction. These strategies are essential to achieve breeding objectives focused on drought tolerance and disease resistance, especially for CMD and CBSD. Full article

Drought stress, which is becoming more prevalent due to climate change, is a significant abiotic factor that adversely impacts crop production and yield stability. Cultivated soybean (Glycine max), a versatile crop for humans and animals, exhibits sensitivity to drought, resulting in reduced growth and development under drought conditions. However, few genetic studies have assessed wild soybean’s (Glycine soja) response to drought stress. In this work, we conducted a genome-wide association study (GWAS) and analysis of wild soybean accessions to identify loci responsible for drought tolerance at the vegetative (n = 187) and the germination stages (n = 135) using the available resequencing data. The GWAS analysis of the leaf wilting score (LWS) identified eight single-nucleotide polymorphisms (SNPs) on chromosomes 10, 11, and 19. Of these, wild soybeans with both SNPs on chromosomes 10 (adenine) and 11 (thymine) produced lower LWS, indicating that these SNPs have an important role in the genetic effect on LWS for drought tolerance at the vegetative stage. At the germination stage, nine SNPs associated with five phenotypic measurements were identified on chromosomes 6, 9, 10, 13, 16, and 17, and the genomic regions identified at the germination stage were different from those identified for the LWS, supporting our previous finding that there may not be a robust correlation between the genes influencing phenotypes at the germination and vegetative stages. This research will benefit marker-assisted breeding programs aimed at enhancing drought tolerance in soybeans. Full article

The relationship between metabolic changes occurring in the developing internodes of sugarcane and the final yield and sugar characteristics is poorly understood due to the lack of integration between phenotypic and metabolic data. To address this issue, a study was conducted where sugarcane metabolism was modeled based on the measurement of cellular components in the top internodes, at two stages of crop development. The study also looked at the effects of Trinexapac-ethyl (Moddus^®) on growth inhibition. The metabolome was measured using GC-analysis, while LC-MS/MS was used to measure proteome changes in the developing internodes. These data were then integrated with the metabolic rates. Regardless of the growth rate, internode elongation was restricted to the top five internodes. In contrast, sucrose and lignin accumulation was sensitive to the growth rate. Crossover plots showed that sucrose accumulation only occurred once the cell wall synthesis had slowed down. These data suggest that sucrose accumulation controlled a reduction in sucrose breakdown for metabolic activity and a reduction in demand for carbon for cell wall polysaccharide synthesis. This study also found that nucleotide sugar metabolism appears to be a key regulator in regulating carbon flow during internode development. Full article

The accurate instance segmentation of individual crop plants is crucial for achieving a high-throughput phenotypic analysis of seedlings and smart field management in agriculture. Current crop monitoring techniques employing remote sensing predominantly focus on population analysis, thereby lacking precise estimations for individual plants. This study concentrates on maize, a critical staple crop, and leverages multispectral remote sensing data sourced from unmanned aerial vehicles (UAVs). A large-scale SAM image segmentation model is employed to efficiently annotate maize plant instances, thereby constructing a dataset for maize seedling instance segmentation. The study evaluates the experimental accuracy of six instance segmentation algorithms: Mask R-CNN, Cascade Mask R-CNN, PointRend, YOLOv5, Mask Scoring R-CNN, and YOLOv8, employing various combinations of multispectral bands for a comparative analysis. The experimental findings indicate that the YOLOv8 model exhibits exceptional segmentation accuracy, notably in the NRG band, with bbox_mAP50 and segm_mAP50 accuracies reaching 95.2% and 94%, respectively, surpassing other models. Furthermore, YOLOv8 demonstrates robust performance in generalization experiments, indicating its adaptability across diverse environments and conditions. Additionally, this study simulates and analyzes the impact of different resolutions on the model’s segmentation accuracy. The findings reveal that the YOLOv8 model sustains high segmentation accuracy even at reduced resolutions (1.333 cm/px), meeting the phenotypic analysis and field management criteria. Full article

In agriculture, especially in crop breeding, innovative approaches are required to address the urgent issues posed by climate change and global food security. Artificial intelligence (AI) is a revolutionary technology in wheat breeding that provides new approaches to improve the ability of crops to withstand and produce higher yields in response to changing climate circumstances. This review paper examines the incorporation of artificial intelligence (AI) into conventional wheat breeding methods, with a focus on the contribution of AI in tackling the intricacies of contemporary agriculture. This review aims to assess the influence of AI technologies on enhancing the efficiency, precision, and sustainability of wheat breeding projects. We conduct a thorough analysis of recent research to evaluate several applications of artificial intelligence, such as machine learning (ML), deep learning (DL), and genomic selection (GS). These technologies expedite the swift analysis and interpretation of extensive datasets, augmenting the process of selecting and breeding wheat varieties that are well-suited to a wide range of environmental circumstances. The findings from the examined research demonstrate notable progress in wheat breeding as a result of artificial intelligence. ML algorithms have enhanced the precision of predicting phenotypic traits, whereas genomic selection has reduced the duration of breeding cycles. Utilizing artificial intelligence, high-throughput phenotyping allows for meticulous examination of plant characteristics under different stress environments, facilitating the identification of robust varieties. Furthermore, AI-driven models have exhibited superior predicted accuracies for crop productivity and disease resistance in comparison to conventional methods. AI technologies play a crucial role in the modernization of wheat breeding, providing significant enhancements in crop performance and adaptability. This integration not only facilitates the growth of wheat cultivars that provide large yields and can withstand stressful conditions but also strengthens global food security in the context of climate change. Ongoing study and collaboration across several fields are crucial to improving and optimizing these AI applications, ultimately enhancing their influence on sustainable agriculture. Full article

To quickly obtain rice plant phenotypic traits, this study put forward the computational process of six rice phenotype features (e.g., crown diameter, perimeter of stem, plant height, surface area, volume, and projected leaf area) using terrestrial laser scanning (TLS) data, and proposed the extraction method for the tiller number of rice plants. Specifically, for the first time, we designed and developed an automated phenotype extraction tool for rice plants with a three-layer architecture based on the PyQt5 framework and Open3D library. The results show that the linear coefficients of determination (R²) between the measured values and the extracted values marked a better reliability among the selected four verification features. The root mean square error (RMSE) of crown diameter, perimeter of stem, and plant height is stable at the centimeter level, and that of the tiller number is as low as 1.63. The relative root mean squared error (RRMSE) of crown diameter, plant height, and tiller number stays within 10%, and that of perimeter of stem is 18.29%. In addition, the user-friendly automatic extraction tool can efficiently extract the phenotypic features of rice plant, and provide a convenient tool for quickly gaining phenotypic trait features of rice plant point clouds. However, the comparison and verification of phenotype feature extraction results supported by more rice plant sample data, as well as the improvement of accuracy algorithms, remain as the focus of our future research. The study can offer a reference for crop phenotype extraction using 3D point clouds. Full article

Cotton is a pivotal global commodity underscored by its economic value and widespread use. In the face of climate change, breeding resilient cultivars for variable environmental conditions becomes increasingly essential. However, the process of phenotyping, crucial to breeding programs, is often viewed as a bottleneck due to the inefficiency of traditional, low-throughput methods. To address this limitation, this study utilizes hyperspectral remote sensing, a promising tool for assessing crucial crop traits across forty cotton varieties. The results from this study demonstrated the effectiveness of four vegetation indices (VIs) in evaluating these varieties for water-use efficiency (WUE). The prediction accuracy for WUE through VIs such as the simple ratio water index (SRWI) and normalized difference water index (NDWI) was higher (up to R² = 0.66), enabling better detection of phenotypic variations (p < 0.05) among the varieties compared to physiological-related traits (from R² = 0.21 to R² = 0.42), with high repeatability and a low RMSE. These VIs also showed high Pearson correlations with WUE (up to r = 0.81) and yield-related traits (up to r = 0.63). We also selected high-performing varieties based on the VIs, WUE, and fiber quality traits. This study demonstrated that the hyperspectral-based proximal sensing approach helps rapidly assess the in-season performance of varieties for imperative traits and aids in precise breeding decisions. Full article

The survival and growth of young plants hinge on various factors, such as seed quality and environmental conditions. Assessing seedling potential/vigor for a robust crop yield is crucial but often resource-intensive. This study explores cost-effective imaging techniques for rapid evaluation of seedling vigor, offering a practical solution to a common problem in agricultural research. In the first phase, nine lettuce (Lactuca sativa) cultivars were sown in trays and monitored using chlorophyll fluorescence imaging thrice weekly for two weeks. The second phase involved integrating embedded computers equipped with cameras for phenotyping. These systems captured and analyzed images four times daily, covering the entire growth cycle from seeding to harvest for four specific cultivars. All resulting data were promptly uploaded to the cloud, allowing for remote access and providing real-time information on plant performance. Results consistently showed the ‘Muir’ cultivar to have a larger canopy size and better germination, though ‘Sparx’ and ‘Crispino’ surpassed it in final dry weight. A non-linear model accurately predicted lettuce plant weight using seedling canopy size in the first study. The second study improved prediction accuracy with a sigmoidal growth curve from multiple harvests (R² = 0.88, RMSE = 0.27, p < 0.001). Utilizing embedded computers in controlled environments offers efficient plant monitoring, provided there is a uniform canopy structure and minimal plant overlap. Full article

The economic significance of hemp (Cannabis sativa L.) as a source of grain, fiber, and flower is rising steadily. However, due to the lack of registered herbicides effective in hemp cultivation, growers have limited weed management options. Plant height, biomass, and canopy architecture may affect crop–weed competition. Greenhouse experiments conducted at the joint Clemson University Coastal Research and Education Center and USDA-ARS research facility at Charleston, SC, USA used 27 hemp varieties, grown under controlled temperature and light conditions. Weekly plant scans using a digital multispectral phenotyping system, integrated with machine learning algorithms of the PlantEye F500 instrument, (Phenospex, Heerlen, Netherlands) captured high-resolution 3D models and spectral data of the plants. Manual and scanner-based measurements were validated and analyzed using statistical methods to assess plant growth and morphology. This study included validation tests showing a significant correlation (p < 0.001) between digital and manual measurements (R² = 0.89 for biomass, R² = 0.94 for height), indicating high precision. The use of 3D multispectral scanning significantly reduces the time-intensive nature of manual measurements, allowing for a more efficient assessment of morphological traits. These findings suggest that digital phenotyping can enhance integrated weed management strategies and improve hemp crop productivity by facilitating the selection of competitive hemp varieties. Full article