Search Results (449)

As a unique woody oil crop in China, Camellia oleifera Abel. germplasm resources show significant genetic diversity in Ya’an City. This study measured 60 phenotypic traits (32 quantitative, 28 qualitative) of 302 accessions to analyze phenotypic variation, establish a classification system, and screen high-yield, high-oil germplasms. The phenotypic diversity index for fruit (H’ = 1.36–1.44) was significantly higher than for leaf (H’ = 1.31) and flower (H’ < 1), indicating genetic diversity concentrated in reproductive traits, suggesting potential genetic variability in these traits. Fruit quantitative traits (e.g., single fruit weight CV = 35.37%, fresh seed weight CV = 38.93%) showed high genetic dispersion. Principal component analysis confirmed the fruit factor and economic factor as main phenotypic differentiation drivers. Quantitative traits were classified morphologically, and correlation analysis integrated them into 13 key indicators classified using LSD and range methods. Finally, TOPSIS evaluation selected 10 excellent germplasms like TQ122 and TQ49, with fruit weight, fresh seed yield, and kernel oil content significantly exceeding the population average. This study provides data for C. oleifera DUS test guidelines and proposes a multi-trait breeding strategy, supporting high-yield variety selection and germplasm resource protection. Full article

Maize is an essential staple food, and its genetic diversity plays a central role in breeding programs aimed at developing climate-adapted cultivars. Constructing a representative core germplasm set is necessary for the efficient conservation and utilization of maize genetic resources. In this study, we analyzed 588 cultivated maize accessions using agronomic traits such as plant morphology and yield traits such as ear characteristics and single-nucleotide polymorphisms (SNPs) to assess molecular diversity and population structure and to construct a core collection. Nineteen phenotypic traits were evaluated, revealing high genetic diversity and significant correlations among most quantitative traits. The optimal sampling strategy was identified as “Mahalanobis distance + 20% + deviation sampling + flexible method.” Whole-genome genotyping was conducted using the Maize6H-60K liquid phase chip. Population structure analysis, principal component analysis, and cluster analysis divided the 588 accessions into six subgroups. A core collection of 172 accessions was selected based on both phenotypic and genotypic data. These were further evaluated for salt–alkali tolerance during germination, and cluster analysis classified them into five groups. Sixty-five accessions demonstrated salt–alkali tolerance, including 18 with high resistance. This core collection serves as a valuable foundation for germplasm conservation and utilization strategies. Full article

Existing frameworks for classifying the fidelity for object detection tasks do not consider false positive likelihood and object uniqueness. Inspired by the Detection, Recognition, Identification (DRI) framework proposed by Johnson 1958, we propose a new modified framework that defines three categories as visible anomaly, identifiable anomaly, and unique identifiable anomaly (AIU) as determined by human interpretation of imagery or geophysical data. These categories are designed to better capture false positive rates and emphasize the importance of identifying unique versus non-unique targets compared to the DRI Index. We then analyze visual, thermal, and multispectral UAV imagery collected over a seeded minefield and apply the AIU Index for the landmine detection use-case. We find that RGB imagery provided the most value per pixel, achieving a 100% identifiable anomaly rate at 125 pixels on target, and the highest unique target classification compared to thermal and multispectral imaging for the detection and identification of surface landmines and UXO. We also investigate how the AIU Index can be applied to machine learning for the selection of training data and informing the required action to take after object detection bounding boxes are predicted. Overall, the anomaly, identifiable anomaly, and unique identifiable anomaly index prescribes essential context for false-positive-sensitive or resolution-poor object detection tasks with applications in modality comparison, machine learning, and remote sensing data acquisition. Full article

The introduction of exotic forage species to new environments for livestock purposes is a common practice to increase productivity. Unfortunately, the population dynamics of introduced species as well as that of native species that persist in grasslands has been poorly studied. In Sonora, the introduction of exotic buffelgrass pasture has caused substantial modifications in the structure of desert scrublands. In this study, an evaluation of the population dynamics of buffelgrass pasture in two grasslands with different times (10 and 50 years) was carried out using classification by size category according to the total number of stems per plant. For each size category of stems, the probabilities of permanence, transition, and regression, and for estimating seed establishment and fecundity were evaluated. The results obtained indicate that in both grasslands, the population growth values (λ) were slightly greater than 1 (λ > 1), which indicates that the populations are stable. The results of this study suggest that the permanence of individual buffelgrass plants in established grasslands is the determining factor in λ. Likewise, our results suggest that in both grasslands, pasture management plays an important role in the permanence or deterioration of buffelgrass pastures. Full article

Precise quality screening of tea tree seeds is crucial for the development of the tea industry. This study proposes a high-precision quality classification method for tea tree seeds by integrating mid-infrared (MIR) spectroscopy with an improved deep learning model. Four types of tea tree seeds in different states were prepared, and their spectral data were collected and preprocessed using Savitzky–Golay (SG) filtering and wavelet transform. Aiming at the deficiencies of DenseNet121 in one-dimensional spectral processing, such as insufficient generalization ability and weak feature extraction, the ECA-DenseNet model was proposed. Based on DenseNet121, the Batch Channel Normalization (BCN) module was introduced to reduce the dimensionality via 1 × 1 convolution while preserving the feature extraction capabilities, the Attention–Convolution Mix (ACMix) module was integrated to combine convolution and self-attention, and the Efficient Channel Attention (ECA) mechanism was utilized to enhance the feature discriminability. Experiments show that ECA-DenseNet achieves 99% accuracy, recall, and F1-score for classifying the four seed quality types, outperforming the original DenseNet121, machine learning models, and deep learning models. This study provides an efficient solution for tea tree seeds detection and screening, and its modular design can serve as a reference for the spectral classification of other crops. Full article

Maize is a vital global crop, serving as a fundamental component of global food security. To support sustainable maize production, the accurate classification of maize seeds—particularly distinguishing haploid from diploid types—is essential for enhancing breeding efficiency. Conventional methods relying on manual inspection or simple machine learning are prone to errors and unsuitable for large-scale data. To overcome these limitations, we propose Slot-Maize, a novel deep learning architecture that integrates Convolutional Neural Networks (CNN), Slot Attention, Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM) layers. The Slot-Maize model was evaluated using two datasets: the Maize Seed Dataset and the Maize Variety Dataset. The Slot Attention module improves feature representation by focusing on object-centric regions within seed images. The GRU captures short-term sequential patterns in extracted features, while the LSTM models long-range dependencies, enhancing temporal understanding. Furthermore, Grad-CAM was utilized as an explainable AI technique to enhance the interpretability of the model’s decisions. The model demonstrated an accuracy of 96.97% on the Maize Seed Dataset and 92.30% on the Maize Variety Dataset, outperforming existing methods in both cases. These results demonstrate the model’s robustness, generalizability, and potential to accelerate automated maize breeding workflows. In conclusion, the Slot-Maize model provides a robust and interpretable solution for automated maize seed classification, representing a significant advancement in agricultural technology. By combining accuracy with explainability, Slot-Maize provides a reliable tool for precision agriculture. Full article

Maize seed variety classification has become essential in agriculture, driven by advancements in non-destructive sensing and machine learning techniques. This study introduced an efficient method for maize variety identification by combining hyperspectral imaging with a framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Spectral data were acquired by hyperspectral imaging technology from five maize varieties and processed using Savitzky–Golay (SG) smoothing, along with standard normal variate (SNV) preprocessing. To enhance feature selection, the competitive adaptive reweighted sampling (CARS) algorithm was applied to reduce redundant information, identifying 100 key wavelengths from an initial set of 774. This method successfully minimized data dimensionality, reduced variable collinearity, and boosted the model’s stability and computational efficiency. A CNN-LSTM model, built on the selected wavelengths, achieved an accuracy of 95.27% in maize variety classification, outperforming traditional chemometric models like partial least squares discriminant analysis, support vector machines, and extreme learning machines. These results showed that the CNN-LSTM model excelled in extracting complex spectral features and offering strong generalization and classification capabilities. Therefore, the model proposed in this study served as an effective tool for maize variety identification. Full article

Keeping drinking water safe is a critical aspect of protecting public health. Traditional laboratory-based methods for evaluating water potability are often time-consuming, costly, and labour-intensive. This paper presents a comparative analysis of four transformer-based deep learning models in the development of automatic classification systems for water potability based on physicochemical attributes. The models examined include the enhanced tabular transformer (ETT), feature tokenizer transformer (FTTransformer), self-attention and inter-sample network (SAINT), and tabular autoencoder pretraining enhancement (TAPE). The study utilized an open-access water quality dataset that includes nine key attributes such as pH, hardness, total dissolved solids (TDS), chloramines, sulphate, conductivity, organic carbon, trihalomethanes, and turbidity. The models were evaluated under a unified protocol involving 70–15–15 data partitioning, five-fold cross-validation, fixed random seed, and consistent hyperparameter settings. Among the evaluated models, the enhanced tabular transformer outperforms other models with an accuracy of 95.04% and an F1 score of 0.94. ETT is an advanced model because it can efficiently model high-order feature interactions through multi-head attention and deep hierarchical encoding. Feature importance analysis consistently highlighted chloramines, conductivity, and trihalomethanes as key predictive features across all models. SAINT demonstrated robust generalization through its dual-attention mechanism, while TAPE provided competitive results with reduced computational overhead due to unsupervised pretraining. Conversely, FTTransformer showed limitations, likely due to sensitivity to class imbalance and hyperparameter tuning. The results underscore the potential of transformer-based models, especially ETT, in enabling efficient, accurate, and scalable water quality monitoring. These findings support their integration into real-time environmental health systems and suggest approaches for future research in explainability, domain adaptation, and multimodal fusion. Full article

This research assessed 42 lentil genotypes developed by ICARDA along with a local variety over three growing seasons (2019–2022) in Southeastern Türkiye. Phenological, morphological, and yield attributes were determined to observe earliness, yield stability, and adaptation properties. Genotype G3771 showed outstanding performance in grain yield (2579 kg ha⁻¹), 1000-seed weight (54.9 g), and harvest index (37.3%), although it had lower stability under more severe drought conditions. Early-maturing genotypes like G3744, G3715, and G3716 consistently flowered and matured sooner, making them better suited for escaping terminal drought stress areas. The highest yields were recorded during the 2019–2020 season, which experienced favorable rainfall and soil nutrient levels, while the lowest yields occurred due to changing climatic conditions in the 2020–2021 season, highlighting the crop’s sensitivity to climate. Principal component analysis, hierarchical clustering, the Modified Multi-Trait Stability Index (MTSI), and the Multi-Trait Genotype-Ideotype Distance Index (MGIDI) aided in effective genotype classification. Although G3771 was the most productive, genotypes G3687, G3715, and G3689 proved to be the most stable and early maturing based on MGIDI scores. Strong relationships between grain yield, biological yield, and seed size identified these as key selection criteria. This study underscores the value of multi-trait selection tools like MGIDI and MTSI in consistently pinpointing lentil genotypes that balance earliness, productivity, and adaptability, laying a strong foundation for developing climate-resilient varieties suited to semi-arid climates. Full article

The common bean is a widely cultivated food source. Many domesticated species of common bean varieties, known as landraces, are cultivated in Mexico by local farmers, exhibiting various colorations and seed mixtures as part of agricultural practices. In this work, we propose a methodology for classifying bean landrace samples using three two-dimensional histograms with data in the CIE L*a*b* color space while additionally integrating chroma (C*) and hue (h°) to develop a new proposal from histograms, employing deep learning for the classification task. The results indicate that utilizing three histograms based on L*, C*, and h° brings an average accuracy of 85.74 ± 2.37 compared to three histograms using L*, a*, and b*, which reported an average accuracy of 82.22 ± 2.84. In conclusion, the new color characterization approach presents a viable solution for classifying common bean landraces of both homogeneous and heterogeneous colors. Full article

Appropriate deep sowing holds significant potential in enhancing wheat production, particularly in dry and low-rainfall regions. Henan Province is a major winter wheat-producing area in China; evaluating the adaptability of wheat varieties to deep sowing through scientific methods is crucial to improve wheat production. This study investigates 26 wheat cultivars in Henan. By assessing key traits of seeds and seedlings at various sowing depths, we analyzed the effects of sowing depth on seed germination and seedlings. A comprehensive index for deep sowing tolerance was established using principal component analysis (PCA) and the membership function method, followed by the classification of the varieties according to their tolerance to deep sowing. The results indicated that, with increased sowing depth, seedling emergence time, coleoptile length, and coleoptile internode length increased, while seedling emergence rate, seedling height, leaf area, and shoot dry weight per unit area decreased. Based on PCA and membership function values, the 26 wheat varieties were classified into three categories: deep sowing tolerant, moderately tolerant, and intolerant, comprising 3, 19, and 4 varieties. This study provides valuable insights for optimizing wheat variety selection and improving sowing practices in Henan Province, offering both theoretical and practical contributions to local wheat production. Full article

The classification of bean landraces based on their coloration is of particular interest, as the color of these plants is associated with the nutritional components present in their seeds. In this paper, the authors propose a procedure to identify the colors of heterogeneous color bean landraces based on the information from their digital images. The proposed methodology employs a three-dimensional histogram representation of the estimated color, expressed in the CIE L*a*b* color space, with an unsupervised learning method called the Gaussian Mixture Model. This approach facilitates the acquisition of representative information for the colors of a bean landrace, represented as points in the CIE L*a*b* color space. Furthermore, the K-$nn$ method can be trained with these punctual representations to identify colors, yielding satisfactory results on landraces with homogeneous and heterogeneous seeds. 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

This study resulted from the demand for seeds with physiological qualities and studies in germination tests applied for seed improvement aimed at productive and homogeneous harvests. The objective of this study was to improve the classification of seeds in germination tests by introducing YOLO as a classification tool for germinated or nongerminated seeds to specify the results and optimize the analysis period. Germination tests were performed for Glycine max (soybean) seeds, and the capture of images from the tests and conventional categorization was performed by uncorrelated individuals, for the processing of these images and application to YOLO. Subsequently, graphical analyses of the YOLO results and comparison metrics with conventional categorization were performed to determine the accuracy of YOLO as a seed categorization tool. The results derived from the analysis of the graphs and comparisons to the conventional methodology of seed classification showed the effectiveness of YOLO for classifying seeds as germinated or nongerminated, reaching 95% accuracy in seed classification, beyond the range of 0–0.110 of the prediction errors, determined by the application of the methodology of mean square error, highlighting the efficiency of YOLO. Full article

The accurate identification of melon seed varieties is essential for improving seed purity and the overall quality of melon production. In this study, hyperspectral imaging was used to identify six varieties of melon seeds. Both hyperspectral images and RGB images were generated during hyperspectral image acquisition. The spectral features of seeds were extracted from the hyperspectral images. The image features of the corresponding seeds were manually extracted from the RGB images. Five different datasets were formed using the spectral features and RGB images of the seeds, including seed spectral features, manually extracted seed image features, seed images, the fusion of seed spectral features with manually extracted features, and the fusion of seed spectral features with seed images. Logistic Regression (LR), Support Vector Classification (SVC), and Extreme Gradient Boosting (XGBoost) were used to establish classification models using spectral features and the manually extracted image features. Convolutional Neural Network (CNN) models were established using the five datasets. The results indicated that the CNN models achieved good performance in all five datasets, with classification accuracies exceeding 90% for the training, validation, and test sets. Also, CNN using the fused datasets obtained optimal performance, achieving classification accuracies exceeding 97% for the training, validation, and test sets. The results indicated that both spectral features and image features can be used to identify the six varieties of melon seeds, and their fusion of spectral features and image features can improve classification performance. These findings provide an alternative approach for melon seed variety identification, which can also be extended to other seed types. Full article