TCDNet : tree crown detection from UAV optical images using uncertainty-aware one-stage network

Tree crown detection plays a vital role in forestry management, resource statistics and yields forecasting. RGB high-resolution aerial images have emerged as a cost-effective source of data for tree crown detection. To address the challenges in the detection using UAV optical images, we propose a one-stage object detection network, TCDNet. First, the network provides an attention enhancement feature extraction module to enable the model to distinguish between tree crowns and their complex backgrounds. Second, an efficient loss is introduced to enable it to be aware of the overlap between adjacent trees, thus effectively avoiding misdetection. The experimental results on two publicly available datasets show that the proposed network outperforms state-of-art networks in terms of precision, recall and mean average precision

Semi-supervised learning for forest fire segmentation using UAV imagery

Unmanned aerial vehicles (UAVs) are an efficient tool for monitoring forest fire due to its advantages, e.g., cost-saving, lightweight, flexible, etc. Semantic segmentation can provide a model aircraft to rapidly and accurately determine the location of a forest fire. However, training a semantic segmentation model requires a large number of labeled images, which is labor-intensive and time-consuming to generate. To address the lack of labeled images, we propose, in this paper, a semi-supervised learning-based segmentation network, SemiFSNet. By taking into account the unique characteristics of UAV-acquired imagery of forest fire, the proposed method first uses occlusion-aware data augmentation for labeled data to increase the robustness of the trained model. In SemiFSNet, a dynamic encoder network replaces the ordinary convolution with dynamic convolution, thus enabling the learned feature to better represent the fire feature with varying size and shape. To mitigate the impact of complex scene background, we also propose a feature refinement module by integrating an attention mechanism to highlight the salient feature information, thus improving the performance of the segmentation network. Additionally, consistency regularization is introduced to exploit the rich information that unlabeled data contain, thus aiding the semi-supervised learning. To validate the effectiveness of the proposed method, extensive experiments were conducted on the Flame dataset and Corsican dataset. The experimental results show that the proposed model outperforms state-of-the-art methods and is competitive to its fully supervised learning counterpart

A multiscale point-supervised network for counting maize tassels in the wild

Accurate counting of maize tassels is essential for monitoring crop growth and estimating crop yield. Recently, deep-learning-based object detection methods have been used for this purpose, where plant counts are estimated from the number of bounding boxes detected. However, these methods suffer from 2 issues: (a) The scales of maize tassels vary because of image capture from varying distances and crop growth stage; and (b) tassel areas tend to be affected by occlusions or complex backgrounds, making the detection inefficient. In this paper, we propose a multiscale lite attention enhancement network (MLAENet) that uses only point-level annotations (i.e., objects labeled with points) to count maize tassels in the wild. Specifically, the proposed method includes a new multicolumn lite feature extraction module that generates a scale-dependent density map by exploiting multiple dilated convolutions with different rates, capturing rich contextual information at different scales more effectively. In addition, a multifeature enhancement module that integrates an attention strategy is proposed to enable the model to distinguish between tassel areas and their complex backgrounds. Finally, a new up-sampling module, UP-Block, is designed to improve the quality of the estimated density map by automatically suppressing the gridding effect during the up-sampling process. Extensive experiments on 2 publicly available tassel-counting datasets, maize tassels counting and maize tassels counting from unmanned aerial vehicle, demonstrate that the proposed MLAENet achieves marked advantages in counting accuracy and inference speed compared to state-of-the-art methods. The model is publicly available at https://github.com/ShiratsuyuShigure/MLAENet-pytorch/tree/main

Semi-FCMNet : semi-supervised learning for forest cover mapping from satellite imagery via ensemble self-training and perturbation

Forest cover mapping is of paramount importance for environmental monitoring, biodiversity assessment, and forest resource management. In the realm of forest cover mapping, significant advancements have been made by leveraging fully supervised semantic segmentation models. However, the process of acquiring a substantial quantity of pixel-level labelled data is prone to time-consuming and labour-intensive procedures. To address this issue, this paper proposes a novel semi-supervised-learning-based semantic segmentation framework that leverages limited labelled and numerous unlabelled data, integrating multi-level perturbations and model ensembles. Our framework incorporates a multi-level perturbation module that integrates input-level, feature-level, and model-level perturbations. This module aids in effectively emphasising salient features from remote sensing (RS) images during different training stages and facilitates the stability of model learning, thereby effectively preventing overfitting. We also propose an ensemble-voting-based label generation strategy that enhances the reliability of model-generated labels, achieving smooth label predictions for challenging boundary regions. Additionally, we designed an adaptive loss function that dynamically adjusts the focus on poorly learned categories and dynamically adapts the attention towards labels generated during both the student and teacher stages. The proposed framework was comprehensively evaluated using two satellite RS datasets, showcasing its competitive performance in semi-supervised forest-cover-mapping scenarios. Notably, the method outperforms the fully supervised approach by 1–3% across diverse partitions, as quantified by metrics including mIoU, accuracy, and mPrecision. Furthermore, it exhibits superiority over other state-of-the-art semi-supervised methods. These results indicate the practical significance of our solution in various domains, including environmental monitoring, forest management, and conservation decision-making processes

PlantNet : transfer learning-based fine-grained network for high-throughput plants recognition

In high-throughput phenotyping, recognizing individual plant categories is a vital support process for plant breeding. However, different plant categories have different fine-grained characteristics, i.e., intra-class variation and inter-class similarity, making the process challenging. Existing deep learning-based recognition methods fail to effectively address this recognition task under challenging requirements, leading to technical difficulties such as low accuracy and lack of generalization robustness. To address these requirements, this paper proposes PlantNet, a fine-grained network for plant recognition based on transfer learning and a bilinear convolutional neural network, which achieves high recognition accuracy in high-throughput phenotyping requirements. The network operates as follows. First, two deep feature extractors are constructed using transfer learning. The outer product of the different spatial locations corresponding to the two features is then calculated, and the bilinear convergence is computed for the different spatial locations. Finally, the fused bilinear vectors are normalized via maximum expectation to generate the network output. Experiments on a publicly available Arabidopsis dataset show that the proposed bilinear model performed better than related state-of-the-art methods. The interclass recognition accuracy of the four different species of Arabidopsis Sf-2, Cvi, Landsberg and Columbia are found to be 98.48%, 96.53%, 96.79% and 97.33%, respectively, with an average accuracy of 97.25%. Thus, the network has good generalization ability and robust performance, satisfying the needs of fine-grained plant recognition in agricultural production

Application of Stabilization/Solidification (S/S) Method for Cadmium Pollution in Surface Sediments of the Dongjiaogou River in Kaifeng, China

Cd contamination of sediments poses a serious threat to the global environment human health. A detail and comprehensive investigation of cadmium (Cd) pollution in the surface sediments of Dongjiaogou River was carried out. Concentration analysis of Cd in various depth and locations was conducted based on 82 samples collected from the river surface sediments where the sediments is up to 353 mg/kg. Subsequently, stabilization/solidification (S/S) method, an effective method of improving the engineering properties of sediments and encapsulating contaminants, was applied in these sediments. According to the results, the Cd pollutant was treated effectively by S/S method, which verifies the feasibility to mitigate the hazards caused by Cd in those sediments from the river. Furthermore, the S/S sediments are favorable as filling material in the road for both recycling and construction

The geological factors affecting gas content and permeability of coal seam and reservoir characteristics in Wenjiaba block, Guizhou province

Abstract The gas content and permeability of coal reservoirs are the main factors affecting the productivity of coalbed methane. To explore the law of gas content and permeability of coal reservoirs in the Zhijin area of Guizhou, taking No.16, No.27 and No.30 coal seams in Wenjiaba mining area of Guizhou as the engineering background, based on the relevant data of coalbed methane exploration in Wenjiaba block, the geological structure, coal seam thickness, coal quality characteristics,coal seam gas content and permeability of the area were studied utilizing geological exploration, analysis of coal components and methane adsorption test. The results show that the average thickness of coal seams in this area is between 1.32 and 1.85 m; the average buried depth of the coal seam is in the range of 301.3–384.2 m; the gas content of No.16 and No.27 coal seams is higher in the syncline core. The gas content of the No.30 coal seam forms a gas-rich center in the south of the mining area. The buried depth and gas content of coal seams in the study area show a strong positive correlation. Under the same pressure conditions, the adsorption capacity of dry ash-free basis is significantly higher than that of air-dried coal. The permeability decreases exponentially with the horizontal maximum principal stress and the horizontal minimum principal stress. The horizontal maximum primary stress and the flat minimum prominent stress increase with the increase of the buried depth of the coal seam. The permeability and coal seam burial depth decrease exponentially. This work can provide engineering reference and theoretical support for selecting high-yield target areas for CBM enrichment in the block

Tenofovir versus entecavir on decreasing risk of HBV-related hepatocellular carcinoma recurrence after liver transplantation

Abstract Background Recent studies have proved that tenofovir disoproxil fumarate (TDF) is associated with a lower risk of hepatocellular carcinoma (HCC) occurrence in chronic hepatitis B (CHB) patients and HCC recurrence in patients who underwent hepatectomy when compared to ETV. However, it is unclear whether TDF and ETV treatment, which are both recommended as first-line antiviral agents to prevent the hepatitis B (HBV) recurrence after liver transplantation (LT), are associated with equivalent prognosis. We aim to compare risk of HCC recurrence and survival of patients recieving TDF or ETV after LT for HBV-related HCC. Method We performed a retrospective study including 316 patients who received treatment with ETV or TDF after LT for HBV-related HCC from 2015 January to 2021 Augest. The Recurrence-free survival (RFS) and overall survival (OS) of TDF and ETV groups were analyzed and compared by propensity score-matched (PSM), multivariable Cox regression analysis, competing risk analysis, sensitivity analyses and subgroup analyses. Result Compared with ETV, TDF therapy was associated with significantly higher RFS rates in the entire cohort (P < 0.01), PSM cohort (P < 0.01) and beyond-Milan cohort (P < 0.01). By multivariable analysis, TDF group was associated with significantly lower rates of HCC recurrence (HR, 0.33; 95%CI, 0.14–0.75; P < 0.01). In subgroup analyses, the similar results were observed in patients with following tumor characteristics: Maximum diameter plus number of viable tumor ≥ 5, with MIV or MAT, AFP at LT ≥ 20 ng/ml, and well or moderate tumor grade. Conclusion Tenofovir decrease risk of HBV-Related Hepatocellular Carcinoma recurrence after liver transplantation compared to Entecavir