Abstract ：

Powdery mildew significantly impacts the yield of natural rubber by being one of the predominant diseases that affect rubber trees. Accurate, non-destructive recognition of powdery mildew in the early stage is essential for the cultivation management of rubber trees. The objective of this study is to establish a technique for the early detection of powdery mildew in rubber trees by combining spectral and physicochemical parameter features. At three field experiment sites and in the laboratory, a spectroradiometer and a hand-held optical leaf-clip meter were utilized, respectively, to measure the hyperspectral reflectance data (350–2500 nm) and physicochemical parameter data of both healthy and early-stage powdery-mildew-infected leaves. Initially, vegetation indices were extracted from hyperspectral reflectance data, and wavelet energy coefficients were obtained through continuous wavelet transform (CWT). Subsequently, significant vegetation indices (VIs) were selected using the ReliefF algorithm, and the optimal wavelengths (OWs) were chosen via competitive adaptive reweighted sampling. Principal component analysis was used for the dimensionality reduction of significant wavelet energy coefficients, resulting in wavelet features (WFs). To evaluate the detection capability of the aforementioned features, the three spectral features extracted above, along with their combinations with physicochemical parameter features (PFs) (VIs + PFs, OWs + PFs, WFs + PFs), were used to construct six classes of features. In turn, these features were input into support vector machine (SVM), random forest (RF), and logistic regression (LR), respectively, to build early detection models for powdery mildew in rubber trees. The results revealed that models based on WFs perform well, markedly outperforming those constructed using VIs and OWs as inputs. Moreover, models incorporating combined features surpass those relying on single features, with an overall accuracy (OA) improvement of over 1.9% and an increase in F1-Score of over 0.012. The model that combines WFs and PFs shows superior performance over all the other models, achieving OAs of 94.3%, 90.6%, and 93.4%, and F1-Scores of 0.952, 0.917, and 0.941 on SVM, RF, and LR, respectively. Compared to using WFs alone, the OAs improved by 1.9%, 2.8%, and 1.9%, and the F1-Scores increased by 0.017, 0.017, and 0.016, respectively. This study showcases the viability of early detection of powdery mildew in rubber trees. © 2024 by the authors.

Keyword ：

Principal component analysis Classification (of information) Decision trees Forestry Rubber Random forests Reflection Radiometers Wavelet transforms Vegetation mapping Feature extraction Support vector machines Optical remote sensing Fungi Data mining

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Abstract ：

In order to solve the communication security problem between clusters and clusters. This paper proposes a data-trusted transmission mechanism based on Merkle authentication, which guarantees the correctness, integrity, confidentiality and reliability of intra-cluster transmission and inter-cluster transmission messages. It can resist security problems such as forgery attacks and anti-replay attacks to a certain degree. The method adopts a hierarchical multipath routing scheme based on Merkle tree authentication. The hierarchical multipath routing scheme routes data by using multiple paths, can bypass malicious nodes to a certain extent, improve the reliability of the network, and even if the malicious node intercepts the data, it cannot know the complete information of the data. © 2022 IEEE.

Keyword ：

Internet of things Network security Authentication Trees (mathematics) Routing protocols Forestry

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Abstract ：

Localizing the root cause of network faults is crucial to network operation and maintenance. Operational expenses will be saved if the root cause can be identified accurately. However, due to the complicated wireless environments and network architectures, accurate root cause localization of network falut meets the difficulties including missing data, hybrid fault behaviors, and short of well-labeled data. In this study, global and local features are constructed to make new feature representation for data sample, which can highlight the temporal characteristics and contextual information of the root cause analysis data. A hybrid tree model (HTM) ensembled by CatBoost, XGBoost and LightGBM is proposed to interpret the hybrid fault behaviors from several perspectives and discriminate different root causes. Based on the combination of global and local features, a semi-supervised training strategy is utilized to train the HTM for dealing with short of well-labeled data. The experiments are conducted on the real-world dataset from ICASSP 2022 AIOps Challenge, and the results show that the global and local feature based HTM achieves the best model performance comparing with other models. Meanwhile, our solution achieves third place in the competition leaderboard which shows the model effectiveness. © 2022 - IOS Press. All rights reserved.

Keyword ：

Forestry Wireless networks Network architecture

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Abstract ：

It is important to accurately identify the combustion state of the municipal solid waste incineration (MSWI) processes. Stable state not only can greatly improve the combustion efficiency, but also can ensure safety of the MSWI processes. What’s more, the pollution emission concentration would be greatly reduced. Aiming at the situation that domain experts identify the combustion state in terms of self-experience in the actual MSWI processes, this study proposes an efficient method based on improved deep forest (IDF). First, the image preprocessing methods such as defogging and denoising, were used to preprocess the combustion flame image to obtain a clear one. Then, the multi-source features (brightness, flame and color) were extracted. Finally, the multi-source features were used as the input of cascade forest module in terms of substituting multi-grained scanning module. Therefore, a combustion state recognition model of MSWI processes based on IDF was established. Based on actual flame images of industrial processes, many experiments has been done. The results showed that the constructed model can reach a recognition accuracy of 95.28%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keyword ：

Waste incineration Forestry Municipal solid waste State estimation Image processing

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Abstract ：

In order to realize the accurate prediction of fruit tree diseases and pests in the text description, this paper combines knowledge graph, representation learning, deep neural network and other methods to construct a fruit tree disease and pest's diagnosis model. The model first constructs a knowledge graph in the agricultural field, and encodes the knowledge in the agricultural field through the knowledge representation model, combines the description text provided by the user to obtain the representation vector of the fruit tree diseases and pests feature entity, and then passes the representation vector and the pest image representation vector through CNN-DNN-BiLSTM network recognizes fruit tree diseases and pests. Three kinds of diseases and pests of apple trees were selected in the experiment: Apple Ring Rot, Apple Scab and Adoxophyes orana. Compared with the VGG network and the BiLSTM network, the precision rate of the model in this paper has been improved by 19%, 4%, 3%, 20% and 25%, 2% on Apple Ring Rot, Apple Scab and Adoxophyes orana, respectively. It can fully integrate agricultural knowledge graph and deep learning technology, and play a positive role in improving the diagnosis of fruit tree diseases and pests. © Published under licence by IOP Publishing Ltd.

Keyword ：

Deep learning Orchards Trees (mathematics) Combines Character recognition Fruits Knowledge representation Diagnosis Agricultural robots Forestry Deep neural networks

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Flue gas emission is an inevitable procedure in the course of electricity generation, which would pose a severe threat to human health, and has an adverse effect on our environment. Due to the fact that the environment in practical flue gas desulfurization system fluctuates frequently, system parameters tend to vary constantly during the operating process, thus control performance with traditional strategies tends to be suboptimal in most cases. To address this problem, some insight into operating conditions must be gained prior to taking proper control strategy. Therefore, in this article, based on actual measurements in 1000 MW Unit Wet Limestone FGD System for a coal-fired power plant, a kind of intelligent operating condition partition method is combined with the multi-model adaptive control strategy. Specifically, analysis and partition of operating condition is carried out in the first place, then adaptive multi-model control is implemented with the combination of parallel dynamic neural network and partition results. Additionally, the applicability of proposed control mode is investigated through different simulation examples. At the same time, to further enhance the flexibility of multi-model control structure, some possible improvements on it is also discussed.

Keyword ：

Clustering Neural networks Slurries feature selection multiple models flue gas desulfurization Forestry neurocontrol Absorption Licenses Regression tree analysis Power generation

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Depressed patients release microblog information and pay attention to each other’s messages. After some depressed patients commit suicide or die for other reasons, there are still microblog messages gathering constantly, thus forming a 'tree hole' - a channel for depressed patients to express their despair and suicide wish. Among them, in 2012, a microblog user died of depression, forming the largest microblog 'tree hole', with more than 1.6 million messages. Based on the data of 'tree hole', this paper analyzes the behavior pattern of microblog 'tree hole' users according to the temporal characteristics of the message, so as to obtain the crowd behavior pattern characteristics of the potential risk persons of mental health and the potential depressed patients, it is found that the relative high incidence time of depression and suicide is related to the behavior pattern. More human and material resources can be deployed to monitor and rescue the depression suicide potential during the active period, and the results can be fed back to the relevant government departments and social rescue agencies, such as the microblog Internet police, etc., so as to make them pay attention to it and form a coordinated mode of the rescue of government-and-society. © 2020, Springer Nature Switzerland AG.

Keyword ：

Behavioral research Health risks Health Forestry Trees (mathematics)

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Forest fire has always been an important hidden danger in forestry management. It is necessary to monitor and manage forest fire through video monitoring system. However, due to the lack of real-time performance of traditional video monitoring system and the difficulty in data processing, an embedded intelligent video analysis system is designed. The system uses a software and hardware design method based on Zynq SoC, first, each functional module of the hardware architecture is divided, then the system software is designed, and finally the system is evaluated as a whole. Based on the application requirements of forest fire prevention, this paper focuses on the detailed description of the underlying hardware architecture and operating system software design, and provides some value and reference for the application scenarios of forest fire prevention. © 2019 Published under licence by IOP Publishing Ltd.

Keyword ：

System-on-chip Deforestation Intelligent computing Integrated circuit design Fireproofing Software design Signal processing Application programs Data handling Fires Computer operating systems Fire hazards Monitoring Computer architecture

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To improve the resource level of agricultural and forestry waste straws and fallen leaves, straw and fallen leaves fibres were used as organic thermal insulation components (TICs). Vitrified beads and closed-pore expanded perlite were used as inorganic TICs to prepare plant fibre cement-based composite thermal insulation mortar (TIM). The effects of TICs on physical and mechanical properties of TIM were studied by altering the types and contents of TICs. The results show that the fluidity of the TIM decreases with the incorporation of plant fibres in the vitrified bead + expanded perlite TIM. With the addition of wheat straw fibre and straw fibre, the homogeneity of TIM mixture is improved. Plant fibres have the characteristics of lightweight, porosity, water absorption and heat preservation, which increases water absorption and heat retention and a decrease in strength of the TIM. With the incorporation of three plant fibres of leaves, wheat straw and straw, the water absorption of TIM increased by 15.84, 5.47, and 4.54% respectively. At the same time, thermal conductivity decreased by 0.17, 0.19, and 0.20 W/(m.K), respectively, flexural strength decreased by 3.99, 2.63, and 2.44 MPa, and compressive strength decreased by 20.91, 17.13, and 19.25 MPa, respectively. After the expanded perlite and vitrified microbeads are modified with pure acrylic emulsion, the polymer film retains more pores inside the fibres, enhances the interface bonding with the cement-based material, which is helpful to improve the thermal insulation properties and strength of the TIM. (C) 2020 The Author(s). Published by Elsevier B.V.

Keyword ：

Composite mortar Plant fibre Thermal insulation component Cement base Performance Modification

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Abstract ：

With the development of wisdom forestry, wireless sensor networks play an increasingly important role. With its powerful ability of space-time information data management, spatial analysis and visualization, GIS technology will provide a scientific and visual geographic information support platform for the application of wireless sensor networks in forest environment. The paper makes use of solar radiation analysis, visibility analysis, and buffer analysis of GIS to help engineers scientifically selecting the sensing node location, and finally develops a 3D wireless sensor networks visualization system based on WebGIS. The system realizes the map display of the space-time information, which is beneficial to manage sensor nodes efficiently. © 2019 IEEE.

Keyword ：

Location Three dimensional computer graphics Visualization Wireless sensor networks Sensor nodes Geographic information systems Data visualization Flow visualization Timber Information management Forestry

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