Search Results (5,252)

Thinning plays a key role in regulating the stand spatial structure (SpS) to improve the development of stand quality, and the stand has different characteristics of stand structure (SS) at different growth and development stages (DSs), so it is most important to reasonably determine the stage of growth and development of the stand to optimize the stand structure. We applied the TWINSPAN two-way indicator species analysis method to classify the different development stages of mixed hard broadleaf forests. We provided a comprehensive stand spatial structure optimization model for three selected plots at different development stages, respectively, to optimize the SpS. The results demonstrated the classified DS of 29 mixed hard broadleaf plots for three forest stages: the establishment stage, competitive stage, and quality selection stage. We then applied the SpS optimization model to our three plots; the Q(x) increased by 124.04%, 333.74%, and 116.83% when compared with those with no harvest, in which, upon the removal of 10% of the trees from the three plots, the maximum RIP values were all observed. Our results indicated that the SpS optimization model could regulate the SS for different growth stages and DSs. Full article

The Camellia oleifera fruit harvester, a specialized agricultural device, is engineered for efficient operation within the densely planted C. oleifera groves of China’s undulating terrains. Its design features a notably small footprint to navigate the constrained spaces between trees. With the enhancement of the functionality and power of the harvester, the engine compartment becomes even more congested. This, while beneficial for performance, complicates heat dissipation and reduces harvesting efficiency. In this study, experiments were initially conducted to collect temperature data from the main heat-generating components and parts susceptible to high temperatures within the harvester’s engine compartment. Subsequently, a 3D model was developed for numerical simulations, leading to the proposal of optimization schemes for the engine compartment’s structure and the validation of these schemes’ feasibility. A comparison of the experimental data, both before and after optimization, revealed a significant reduction in the surface temperatures of components within the engine compartment following optimization. As a result, the heat dissipation of the engine compartment has been greatly optimized. The harvester has demonstrated prolonged normal operation, enhancing the reliability and economy of the harvester. Full article

Missing data and class imbalance hinder the accurate prediction of rare events such as dairy mastitis. Resampling and imputation are employed to handle these problems. These methods are often used arbitrarily, despite their profound impact on prediction due to changes caused to the data structure. We hypothesize that their use affects the performance of ML models fitted to automated milking systems (AMSs) data for mastitis prediction. We compare three imputations—simple imputer (SI), multiple imputer (MICE) and linear interpolation (LI)—and three resampling techniques: Synthetic Minority Oversampling Technique (SMOTE), Support Vector Machine SMOTE (SVMSMOTE) and SMOTE with Edited Nearest Neighbors (SMOTEEN). The classifiers were logistic regression (LR), multilayer perceptron (MLP), decision tree (DT) and random forest (RF). We evaluated them with various metrics and compared models with the kappa score. A complete case analysis fitted the RF (0.78) better than other models, for which SI performed best. The DT, RF, and MLP performed better with SVMSMOTE. The RF, DT and MLP had the overall best performance, contributed by imputation or resampling (SMOTE and SVMSMOTE). We recommend carefully selecting resampling and imputation techniques and comparing them with complete cases before deciding on the preprocessing approach used to test AMS data with ML models. Full article

Backgrounds: Sinojackia xylocarpa Hu is a deciduous tree in the Styracaceae family, and it is classified as a Class II endangered plant in China. Seed storage technology is an effective means of conserving germplasm resources, but the effects of different storage conditions on the quality and associated metabolism of S. xylocarpa seeds remain unclear. This study analyzed the physiological and metabolic characteristics of S. xylocarpa seeds under four storage conditions. Results: Our findings demonstrate that reducing seed moisture content and storage temperature effectively prolongs storage life. Seeds stored under that condition exhibited higher internal nutrient levels, lower endogenous abscisic acid (ABA) hormone levels, and elevated gibberellic acid (GA₃) levels. Additionally, 335 metabolites were identified under four different storage conditions. The analysis indicates that S. xylocarpa seeds extend seed longevity and maintain cellular structural stability mainly by regulating the changes in metabolites related to lipid, amino acid, carbohydrate, and carotenoid metabolic pathways under the storage conditions of a low temperature and low seed moisture. Conclusions: These findings provide new insights at the physiological and metabolic levels into how these storage conditions extend seed longevity while also offering effective storage strategies for preserving the germplasm resources of S. xylocarpa. Full article

The apple tree (Malus × domestica Borkh.) belongs to the Rosaceae. Due to its adaptability and tolerance to different soil and climatic conditions, it is cultivated worldwide for fresh consumption. The priorities of apple growers are high-quality fruits and stable yield for high production. About 90 to 95 percent of fruits should fall or be eliminated from apple trees to avoid overcropping and poor-quality fruits. Apple trees engage in a complex biological process known as yield self-regulation, which is influenced by several internal and external factors. Apple buds develop in different stages along the branches, and they can potentially give rise to new shoots, leaves, flowers, or fruit clusters. The apple genotype determines how many buds will develop into fruit-bearing structures and the capacity for yield self-regulation. Plant hormones such as ethylene, cytokinins, auxins, and gibberellins play a crucial role in regulating the fruit set, growth, and development, and the balance of these hormones influences the flowering intensity, fruit size, and fruit number on the apple tree. Apple growers often interfere in the self-regulation process by manually thinning fruit clusters. Different thinning methods, such as by hand, mechanical thinning, or applying chemical substances, are used for flower and fruit thinning. The most profitable in commercial orchards is the use of chemicals for elimination, but more environmentally sustainable solutions are required due to the European Green Deal. This review focuses on the biological factors and genetic mechanisms in apple yield self-regulation for a better understanding of the regulatory mechanism of fruitlet abscission for future breeding programs targeted at self-regulating yield apple varieties. Full article

The rapid expansion of Internet-of-Things (IoT) applications necessitates a thorough understanding of network configurations to address unique challenges across various use cases. This paper presents an in-depth analysis of three IoT network topologies: linear chain, structured tree, and dynamic transition networks, each designed to meet the specific requirements of industrial automation, home automation, and environmental monitoring. Key performance metrics, including round-trip time (RTT), server processing time (SPT), and power consumption, are evaluated through both simulation and hardware experiments. Additionally, this study introduces an enhanced UDP protocol featuring an acknowledgment mechanism and a power consumption evaluation, aiming to improve data transmission reliability over the standard UDP protocol. Packet loss is specifically measured in hardware experiments to compare the performance of standard and enhanced UDP protocols. The findings show that the enhanced UDP significantly reduces packet loss compared to the standard UDP, enhancing data delivery reliability across dynamic and structured networks, though it comes at the cost of slightly higher power consumption due to additional processing. For network topology performance, the linear chain topology provides stable processing but higher RTT, making it suitable for applications such as tunnel monitoring; the structured tree topology offers low energy consumption and fast communication, ideal for home automation; and the dynamic transition network, suited for industrial Automated Guided Vehicles (AGVs), encounters challenges with adaptive routing. These insights guide the optimization of communication protocols and network configurations for more efficient and reliable IoT deployments. Full article

With the global population on the rise and arable land diminishing, the need for sustainable and precision agriculture has become increasingly important. This study explores the application of unoccupied aerial systems (UAS) in precision agriculture, specifically focusing on Montmorency cherry orchards in Payson, Utah. Despite the widespread use of UAS for various crops, there is a notable gap in research concerning cherry orchards, which present unique challenges due to their physical structure. UAS data were gathered using an RTK-enabled DJI Mavic 3M, equipped with both RGB and multispectral cameras, to capture high-resolution imagery. This research investigates two primary applications of UAS in cherry orchards: tree height mapping and crop health assessment. We also evaluate the accuracy of tree height measurements derived from three UAS data processing software packages: Pix4D, Drone2Map, and DroneDeploy. Our results indicated that DroneDeploy provided the closest relationship to ground truth data with an R² of 0.61 and an RMSE of 31.83 cm, while Pix4D showed the lowest accuracy. Furthermore, we examined the efficacy of RGB-based vegetation indices in predicting leaf area index (LAI), a key indicator of crop health, in the absence of more expensive multispectral sensors. Twelve RGB-based indices were tested for their correlation with LAI, with the IKAW index showing the strongest correlation (R = 0.36). However, the overall explanatory power of these indices was limited, with an R² of 0.135 in the best-fitting model. Despite the promising results for tree height estimation, the correlation between RGB-based indices and LAI was underwhelming, suggesting the need for further research. Full article

Maintaining logical consistency in structured explanations is critical for understanding and troubleshooting the reasoning behind a system’s decisions. However, existing methods for entailment tree generation often struggle with logical consistency, resulting in erroneous intermediate conclusions and reducing the overall accuracy of the explanations. To address this issue, we propose case-based deduction (CBD), a novel approach that retrieves cases with similar logical structures from a case base and uses them as demonstrations for logical deduction. This method guides the model toward logically sound conclusions without the need for manually constructing logical rule bases. By leveraging a prototypical network for case retrieval and reranking them using information entropy, CBD introduces diversity to improve in-context learning. Our experimental results on the EntailmentBank dataset show that CBD significantly improves entailment tree generation, achieving performance improvements of 1.7% in Task 1, 0.6% in Task 2, and 0.8% in Task 3 under the strictest Overall AllCorrect metric. These findings confirm that CBD enhances the logical consistency and overall accuracy of AI systems in structured explanation tasks. Full article

The diameter at breast height (DBH) and crown base height (CBH) are important indicators in forest surveys. To enhance the accuracy and convenience of DBH and CBH extraction for standing trees, a method based on understory small motion clips (a series of images captured with slight viewpoint changes) has been proposed. Histogram equalization and quadtree uniformization algorithms are employed to extract image features, improving the consistency of feature extraction. Additionally, the accuracy of depth map construction and point cloud reconstruction is improved by minimizing the variance cost function. Six 20 m × 20 m square sample plots were selected to verify the effectiveness of the method. Depth maps and point clouds of the sample plots were reconstructed from small motion clips, and the DBH and CBH of standing trees were extracted using a pinhole imaging model. The results indicated that the root mean square error (RMSE) for DBH extraction ranged from 0.60 cm to 1.18 cm, with relative errors ranging from 1.81% to 5.42%. Similarly, the RMSE for CBH extraction ranged from 0.08 m to 0.21 m, with relative errors ranging from 1.97% to 5.58%. These results meet the accuracy standards required for forest surveys. The proposed method enhances the efficiency of extracting tree structural parameters in close-range photogrammetry (CRP) for forestry. A rapid and accurate method for DBH and CBH extraction is provided by this method, laying the foundation for subsequent forest resource management and monitoring. Full article

Vertex-degree-based (VDB) topological indices have been applied in the study of molecular structures and chemical properties. At present, the exponential VDB index has been studied extensively. Naturally, we began to consider the logarithmic VDB index lnTf. In this paper, we first discuss the necessity of a logarithmic VDB index, and then present sufficient conditions so that Pn and Sn are the only trees with the smallest and greatest values of lnTf(T). As applications, the minimal and maximal trees of some logarithmic VDB indices are determined. Through our work, we found that the logarithmic VDB index lnTf has excellent discriminability, but the relevant results are not completely opposite to the exponential VDB index. The study of logarithmic VDB indices is an interesting but difficult task that requires further resolution. Full article

Fine three-dimensional (3D) reconstruction of real forest scenes can provide a reference for forestry digitization and forestry resource management applications. Airborne LiDAR technology can provide valuable data for large-area forest scene reconstruction. This paper proposes a 3D reconstruction method for complex forest scenes with trees, shrubs, and grass, based on airborne LiDAR point clouds. First, forest vertical distribution characteristics are used to segment tree, shrub, and ground–grass points from an airborne LiDAR point cloud. For ground–grass points, a ground–grass grid model is constructed. For tree points, a method based on hierarchical canopy point fitting is proposed to construct a trunk model, and a crown model is constructed with the 3D α-shape algorithm. For shrub points, a shrub model is directly constructed based on the 3D α-shape algorithm. Finally, tree, shrub, and ground–grass models are spatially combined to achieve the reconstruction of real forest scenes. Taking six forest plots located in Hebei, Yunnan, and Guangxi provinces in China and Baden-Württemberg in Germany as study areas, experimental results show that the accuracy of individual tree segmentation reaches 87.32%, the accuracy of shrub segmentation reaches 60.00%, the height accuracy of the grass model is evaluated with an RMSE < 0.15 m, the volume accuracy of shrub and tree models is assessed with R² > 0.848 and R² > 0.904, respectively. Furthermore, we compared the model constructed in this study with simplified point cloud and voxel models. The results demonstrate that the proposed modeling approach can meet the demand for the high-accuracy and lightweight modeling of large-area forest scenes. Full article

Pinus yunnanensis Franch., one of the pioneer species of wild mountain afforestation in southwest China, plays an essential role in the economy, society and environment of Yunnan Province. Nonetheless, P. yunnanensis’ trunk twisting and bending phenomenon has become more common, which significantly restricts its use and economic benefits. In order to clarify the compositional differences between the straight and twisted trunk types of P. yunnanensis and to investigate the reasons for the formation of twisted stems, the present study was carried out to dissect the macroscopic and microscopic structure of the straight and twisted trunk types of P. yunnanensis, to determine the content of cell wall components (lignin, cellulose, hemicellulose), determine the content of endogenous hormones, and the expression validation of phytohormone-related differential genes (GA2OX, COI1, COI2) and cell wall-related genes (XTH16, TCH4). The results showed that the annual rings of twisted trunk types were unevenly distributed, eccentric growth, insignificant decomposition of early and late wood, rounding and widening of the tracheid cells, thickening of the cell wall, and reduction of the cavity diameter; the lignin and hemicellulose contents of twisted trunk types were higher; in twisted trunk types, the contents of gibberellin (GA) and jasmonic acid (JA) increased, and the content of auxin (IAA) was reduced; the GA2OX were significantly down-regulated in twisted trunk types, and the expressions of the genes associated with the cell wall, COI1, COI2, TCH4 and XTH16, were significantly up-regulated. In conclusion, the present study found that the uneven distribution of endogenous hormones may be an important factor leading to the formation of twisted trunk type of P. yunnanensis, which adds new discoveries to reveal the mechanism of the genesis of different trunk types in plants, and provides a theoretical basis for the genetic improvement of forest trees. Full article

Procypris mera (Lin, 1933), also known as the Chinese ink carp, currently has a second-class protection status in China. Understanding the structure and characteristics of mitochondrial genes provides essential information for resource conservation and phylogenetic studies of P. mera. Here, we sequenced the mitochondrial genomes of three P. mera (WYL1-3) from three sites and performed phylogenetic analysis. The generated three genomes were 16,587 bp in length, comprising 13 protein-coding genes (PCGs), 22 tRNAs, two rRNAs, and two non-coding regions (control region (CR), D-loop, and light-stranded replication start OL), with a preference for codons ending in A or C. The mitochondrial genomes of WYL2 and WYL3 were identical, differing from that of WYL1 by only five single-nucleotide polymorphisms (SNPs). All mitochondrial PCGs had Ka/Ks ratios of less than one, suggesting purifying selection. Phylogenetic tree analysis based on amino acid sequences suggested that the genus Puntioplites is sister to all other genera of the subfamily Cyprinidae of China; the genus Procypris forms a monophyletic group; and the genera Carassioides, Carassius, and Cyprinus form a monophyletic group. This study contributes to our understanding of the phylogenetic relationships in subfamily Cyprininae in China and lays the foundation for resource conservation and management of P. mera. Full article

With the advancement of computer vision technology, the demand for fruit recognition in agricultural automation is increasing. To improve the accuracy and efficiency of recognizing young red pears, this study proposes an improved model based on the lightweight YOLOv9s, termed YOLOv9s-Pear. By constructing a feature-rich and diverse image dataset of young red pears and introducing spatial-channel decoupled downsampling (SCDown), C2FUIBELAN, and the YOLOv10 detection head (v10detect) modules, the YOLOv9s model was enhanced to achieve efficient recognition of small targets in resource-constrained agricultural environments. Images of young red pears were captured at different times and locations and underwent preprocessing to establish a high-quality dataset. For model improvements, this study integrated the general inverted bottleneck blocks from C2f and MobileNetV4 with the RepNCSPELAN4 module from the YOLOv9s model to form the new C2FUIBELAN module, enhancing the model’s accuracy and training speed for small-scale object detection. Additionally, the SCDown and v10detect modules replaced the original AConv and detection head structures of the YOLOv9s model, further improving performance. The experimental results demonstrated that the YOLOv9s-Pear model achieved high detection accuracy in recognizing young red pears, while reducing computational costs and parameters. The detection accuracy, recall, mean precision, and extended mean precision were 0.971, 0.970, 0.991, and 0.848, respectively. These results confirm the efficiency of the SCDown, C2FUIBELAN, and v10detect modules in young red pear recognition tasks. The findings of this study not only provide a fast and accurate technique for recognizing young red pears but also offer a reference for detecting young fruits of other fruit trees, significantly contributing to the advancement of agricultural automation technology. Full article

A range of remote sensing platforms provide high spatial and temporal resolution insights which are useful for monitoring vegetation growth. Very few studies have focused on fruit orchards, largely due to the inherent complexity of their structure. Fruit trees are mixed with inter-rows that can be grassed or non-grassed, and there are no standard protocols for ground measurements suitable for the range of crops. The assessment of biophysical variables (BVs) for fruit orchards from optical satellites remains a significant challenge. The objectives of this study are as follows: (1) to address the challenges of extracting and better interpreting biophysical variables from optical data by proposing new ground measurements protocols tailored to various orchards with differing inter-row management practices, (2) to quantify the impact of the inter-row at the Sentinel pixel scale, and (3) to evaluate the potential of Sentinel 2 data on BVs for orchard development monitoring and the detection of key phenological stages, such as the flowering and fruit set stages. Several orchards in two pedo-climatic zones in southeast France were monitored for three years: four apricot and nectarine orchards under different management systems and nine cherry orchards with differing tree densities and inter-row surfaces. We provide the first comparison of three established ground-based methods of assessing BVs in orchards: (1) hemispherical photographs, (2) a ceptometer, and (3) the Viticanopy smartphone app. The major phenological stages, from budburst to fruit growth, were also determined by in situ annotations on the same fields monitored using Viticanopy. In parallel, Sentinel 2 images from the two study sites were processed using a Biophysical Variable Neural Network (BVNET) model to extract the main BVs, including the leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of green vegetation cover (FCOVER). The temporal dynamics of the normalised FAPAR were analysed, enabling the detection of the fruit set stage. A new aggregative model was applied to data from hemispherical photographs taken under trees and within inter-rows, enabling us to quantify the impact of the inter-row at the Sentinel 2 pixel scale. The resulting value compared to BVs computed from Sentinel 2 gave statistically significant correlations (0.57 for FCOVER and 0.45 for FAPAR, with respective RMSE values of 0.12 and 0.11). Viticanopy appears promising for assessing the PAI (plant area index) and FCOVER for orchards with grassed inter-rows, showing significant correlations with the Sentinel 2 LAI (R² of 0.72, RMSE 0.41) and FCOVER (R² 0.66 and RMSE 0.08). Overall, our results suggest that Sentinel 2 imagery can support orchard monitoring via indicators of development and inter-row management, offering data that are useful to quantify production and enhance resource management. Full article