Plant Management Research Articles

Первенство российских практик управления персоналом на примере горнорудного дела в Республике Башкортостан

The article discusses the management activities of the first Bashkir entrepreneurs in the mining industry in the Southern Urals are considered. Social policy and work with personnel at mining factories of the 18th century are analyzed. The primacy of Russian management practices in comparison with the school of scientific management of F. Winslow Taylor is argued. The research method is a comparative analysis of approaches to management, including leadership styles, practices of motivation and stimulation of personnel. The purpose of the study is based on a comparative analysis of the main provisions of the teachings of F. Winslow Taylor, Henri Fayol (20th century) and the management practice of I. B. Tverdyshev (18th century) to show that the management activities of the miners of the Southern and Middle Urals for many decades and even centuries anticipated the scientific achievements of the classics of management theory. The practical significance of the study is the study of Russian experience in personnel management using the example of mining, a historically developing industry in Bashkortostan; research into the activities of I. B. Tverdyshev as the founder of the Bashkortostan mining plant management system, based on the structural principles of division of labor, standards of control, hierarchy, consistency and delegation of authority, socially responsible management.

Under Long-Term Agricultural Systems, the Role of Mycorrhizae in Climate Change and Food Security

Over the past 100 years, the rapid growth in population from 2 billion to 8 billion has significantly impacted the environment and climate change. In addition, food consumption has skyrocketed, and there are widespread worries about global food security. Due to inadequate soil and plant management techniques, including high soil tillage, chemical fertilizers, inappropriate irrigation, and genetically engineered crops, this spike has made it more difficult to guarantee food security for everyone on the planet. These actions have resulted in societal unrest, climatic change, and land degradation. With organic carbon mineralization, more CO2 is released into the atmosphere because of atmospheric heating and climate change. Long-term greenhouse gasses released into the atmosphere cause global climate change. Increasing climate changes and the inefficiency of soil productivity result in the natural effects of the rhizosphere on plant growth and food security. One of the most effective mechanisms of the rhizosphere is mycorrhizal fungi, which are injured microorganisms. Frequently disregarded mycorrhizal fungi present a potential solution. While sequestering carbon from the atmosphere, they can increase agricultural yields, plant health, and soil fertility. For sustainable agriculture and environmental preservation, it is essential to understand and take advantage of the potential of mycorrhizal fungi. A crucial area for study and practical application is the function of mycorrhizal fungi in reducing these difficulties and enhancing food security. Considering rising environmental challenges, understanding their contributions and researching their relationships may help create a more stable and secure global food system..

Lightweight convolutional neural network-based plant disease identification for protection and landscape design

Plant diseases significantly impact landscape design, necessitating comprehensive consideration and effective management measures to ensure the health, aesthetics, and sustainability of landscapes. Early detection and timely control of plant diseases are crucial, but traditional monitoring methods, which rely on manual observation and sample collection, are inadequate for covering large garden areas and may delay necessary treatments. This study addresses these challenges by constructing a small Rosa chinensis disease dataset through field collection and data augmentation techniques. We propose MixResCoAtNet, an improved model based on the lightweight MixNet framework, to identify and categorize diseases from plant leaf images using convolutional neural networks (CNNs). Comparison experiments with various classical convolutional network models demonstrate that MixResCoAtNet outperforms these models, offering more competitive performance. Additionally, due to its lighter structure, MixResCoAtNet shows greater potential for deployment on mobile devices, facilitating real-time and efficient plant disease monitoring and management in landscape design.

Anglo-German Relations in German Samoa as Reflected in German Reports from the Early Stages of World War I

Contemporary reports by German residents of Samoa to the German Colonial Office indicate that the opening stages of World War I in German Samoa played out in an atmosphere of Anglo-German camaraderie, highlighted by four developments: the petition presented by British residents of Samoa to Colonel Logan requesting that the Germans be treated with the same respect as had been accorded the British in Samoa; Logan’s invitation to all German civil servants to continue their governmental duties; the deployment of a division of New Zealand Engineers to put down the Chinese rebellion against the German plantation managers at Tapatapao; and Admiral Graf Spee’s decision on his arrival in Samoa not to pursue any hostilities against the British occupying forces.

Editorial: Impacts of invasive plant management on forest biodiversity and ecosystem services

Editorial: Impacts of invasive plant management on forest biodiversity and ecosystem services

LCA applied to comparative environmental evaluation of aggregate production from recycled waste materials and virgin sources

Nowadays, all productive sectors, including the construction industry, are facing the challenge of reducing their environmental impact. To achieve this objective, numerous actions are being carried out to access greater levels of environmental and economic sustainability. Techniques as Life Cycle Assessment contribute to quantifying environmental impacts, promoting a circular economy in a sector that consumes a high volume of resources, materials, and energy while generating large amounts of gaseous, liquid, or solid emissions. The present study aims to deepen our understanding of aspects that demonstrate the benefits of using RA instead of natural aggregates. This study not only quantifies the environmental impact but also explores the effects of potential improvements in the productive system and their impact on reducing environmental harm. The Life Cycle Assessment methodology is applied to quantify and compare the environmental impacts generated in the production of a ton of mixed recycled aggregates (MRA) from construction and demolition wastes, based on the data provided by plant managers. This is compared to the environmental impacts generated in the production of one ton of natural aggregates extracted from a quarry. The results revealed that the production of mixed recycled aggregate is more environmentally beneficial, confirming a reduction of 70.66% in environmental impacts during the production of recycled aggregates, in comparison to the natural aggregates extraction. Furthermore, the economic analysis demonstrates the economic advantage since the cost of producing recycled aggregates is over 30% cheaper than natural aggregates, being more competitive even when the transportation distances from the plant to the work sites exceed those of natural aggregates.Graphical

A method for measuring banana pseudo-stem phenotypic parameters based on handheld mobile LiDAR and IMU fusion.

Diameter and height are crucial morphological parameters of banana pseudo-stems, serving as indicators of the plant's growth status. Currently, in densely cultivated banana plantations, there is a lack of applicable research methods for the scalable measurement of phenotypic parameters such as diameter and height of banana pseudo-stems. This paper introduces a handheld mobile LiDAR and Inertial Measurement Unit (IMU)-fused laser scanning system designed for measuring phenotypic parameters of banana pseudo-stems within banana orchards. To address the challenges posed by dense canopy cover in banana orchards, a distance-weighted feature extraction method is proposed. This method, coupled with Lidar-IMU integration, constructs a three-dimensional point cloud map of the banana plantation area. To overcome difficulties in segmenting individual banana plants in complex environments, a combined segmentation approach is proposed, involving Euclidean clustering, Kmeans clustering, and threshold segmentation. A sliding window recognition method is presented to determine the connection points between pseudo-stems and leaves, mitigating issues caused by crown closure and heavy leaf overlap. Experimental results in banana orchards demonstrate that, compared with manual measurements, the mean absolute errors and relative errors for banana pseudo-stem diameter and height are 0.2127 cm (4.06%) and 3.52 cm (1.91%), respectively. These findings indicate that the proposed method is suitable for scalable measurements of banana pseudo-stem diameter and height in complex, obscured environments, providing a rapid and accurate inter-orchard measurement approach for banana plantation managers.

Advancing short-term solar irradiance forecasting accuracy through a hybrid deep learning approach with Bayesian optimization

The optimization of solar energy integration into the power grid relies heavily on accurate forecasting of solar irradiance. In this study, a new approach for short-term solar irradiance forecasting is introduced. This method combines Bayesian Optimized Attention-Dilated Long Short-Term Memory and Savitzky-Golay filtering. The methodology is implemented to analyze data obtained from a solar irradiance probe situated in Douala, Cameroon. Initially, the unprocessed data is augmented by integrating distinctive solar irradiation variables, and the Savitzky-Golay filter with Bayesian Optimization is used to enhance its quality. Subsequently, multiple deep learning models, including Long Short-Term Memory, Bidirectional Long Short-Term Memory, Artificial Neural Networks, Bidirectional Long Short-Term Memory with Additive Attention Mechanism, and Bidirectional Long Short-Term Memory with Additive Attention Mechanism and Dilated Convolutional layers, are trained and evaluated. Out of all the models considered, the proposed approach, which combines the attention mechanism and dilated convolutional layers, demonstrates exceptional performance with the best convergence and accuracy in forecasting. Bayesian Optimization is further utilized to fine-tune the polynomial and window size of the Savitzky-Golay filter and optimize the hyperparameters of the deep learning models. The results show a Symmetric Mean Absolute Percentage Error of 0.6564, a Normalized Root Mean Square Error of 0.2250, and a Root Mean Square Error of 22.9445, surpassing previous studies in the literature. Empirical findings highlight the effectiveness of the proposed methodology in enhancing the accuracy of short-term solar irradiance forecasting. This research contributes to the field by introducing novel data pre-processing techniques, a hybrid deep learning architecture, and the development of a benchmark dataset. These advancements benefit both researchers and solar plant managers, improving solar irradiance forecasting capabilities.

Effects of thinning and understory removal on soil phosphorus fractions in subtropical pine plantations.

Forest management changes the physical environments and nutrient dynamics and then regulates the forest productivity. Soil phosphorus (P) availability is critical for productivity in tropical and subtropical forests. However, it was still poorly understood how soil P content and fraction respond to various forest management practices in these regions. Here, we measured the soil total P, available P, and Hedley's P fractions, including inorganic and organic P (Pi and Po), in subtropical pine plantations treated with understory removal (UR), non-dominant species thinning (NDST) and dominant species thinning (DST) after nine years. Compared to plantations without management (CK), treatments such as UR, NDST, and DST decreased soil total P at 0-10 cm and soil available P at 0-10 cm and 10-20 cm. Increases in resin-Pi, NaOH-Pi, and C.HCl-Pi resulted in a higher total Pi in 0-10 cm (p < 0.05) in treated plots (UR, NDST, and DST) than in CK plots. UR, NDST, and DST treatments increased NaHCO3-Po and NaOH-Po (p < 0.05) but decreased C.HCl-Po at a depth of 0-10 cm. Regardless of management treatments, soil total P, available P, and P fractions in 0-10 cm showed higher contents than those in 10-20 cm. There were positive relationships between total P and total Po (p < 0.01) and between available P and total Pi. There were also positive relationships between total P, available P, NaHCO3-Pi, and NaOH-Pi (p < 0.05). In conclusion, forest management such as UR, NDST, and DST decreased soil total P and available P, and transforming soil P fractions to available P will meet the P demand following management in the pine plantations of subtropical China.

Distributed differentially private energy management of virtual power plants

Prosumers with flexible distributed energy resources (DERs) can be aggregated as a virtual power plant (VPP) to participate in the electricity market. However, the VPP’s energy management requires the prosumers to share individual data, which poses privacy concerns. This paper proposes a distributed differentially private energy management strategy for the VPP to maximize its profit by coordinating the prosumers and at the same time mitigate the privacy risks of local prosumers. Specifically, the coordination of prosumers is first formulated as an optimization problem, which includes the operation constraints of each individual prosumer and the VPP coordination process. On this basis, this paper proposes to solve the optimization problem with a two-level privacy protection strategy. For the individual level, a distributed solution framework is proposed to keep the private information of each prosumer preserved locally. For the communication level, a differential privacy mechanism is integrated into the information exchange process thus reducing the privacy leakage risk. Both theoretical and practical results are provided to verify the performance of the proposed method in terms of optimality and privacy protection levels.

Real-Time Object Classification on an Enamel Paint Coating Conveyor Line Using Mask R-CNN

The rising demand to efficiently acquire live production data has added more significance to automated monitoring and reporting within the industrial manufacturing sector. Real-time parts screening requiring repetitive human intervention for data input may not be a feasible solution to meet the demands of modern industrial automation. The objective of this study is to automatically classify and report on manufactured metal sheet parts. The metal components are mechanically suspended on an enamel paint-coating conveyor line in a household appliance manufacturing plant. At any given instant, the parts may not be in the exact coordinates within the desired area of interest and the classes of objects vary based on changing production requirements. To mitigate these challenges, this study proposes the use of a trained Mask R-CNN model to detect the objects and their associated class. Images are acquired in real-time using a video camera located next to the enamel coating line which are subsequently processed using the object detection algorithm for automated entry into the plant management information system. The highest achieved average precision obtained from the model was 98.27% with an overall accuracy of 98.24% using the proposed framework. The results surpassed the acceptable standard for the average precision of 97.5% as set by the plant production quality engineers.

Tracking the Dynamics of Spartina alterniflora with WorldView-2/3 and Sentinel-1/2 Imagery in Zhangjiang Estuary, China

The invasion of Spartina alterniflora (S. alterniflora) has posed serious threats to the sustainability, quality and biodiversity of coastal wetlands. To safeguard coastal ecosystems, China has enacted large-scale S. alterniflora removal projects, which set the goal of effectively controlling S. alterniflora throughout China by 2025. The accurate monitoring of S. alterniflora with remote sensing is urgent and requisite for the scientific eradication, control and management of this invasive plant. In this study, we combined multi-temporal WorldView-2/3 (WV-2/3) and Sentinel-1/2 imagery to monitor the S. alterniflora dynamics before and after the S. alterniflora removal projects in Zhangjiang Estuary. We put forward a new method for S. alterniflora detection with eight-band WV-2/3 imagery. The proposed method first used NDVI to discriminate S. alterniflora from water, mud flats and mangroves based on Ostu thresholding and then used the red-edge, NIR1 and NIR2 bands and support vector machine (SVM) classifier to distinguish S. alterniflora from algae. Due to the contamination of frequent cloud cover and tidal inundation, the long revisit time of high-resolution satellite sensors and the short-term S. alterniflora removal projects, we combined Sentinel-1 SAR time series and Sentinel-2 optical imagery to monitor the S. alterniflora removal project status in 2023. The overall accuracies of the S. alterniflora detection results here are above 90%. Compared with the traditional SVM method, the proposed method achieved significantly higher identification accuracy. The S. alterniflora area was 115.19 hm2 in 2015, 152.40 hm2 in 2017 and 15.29 hm2 in 2023, respectively. The generated S. alterniflora maps clearly show the clonal growth of S. alterniflora in Zhangjiang Estuary from 2015 to 2017, and the large-scale S. alterniflora eradication project has achieved remarkable results with a removal rate of about 90% in the study area. With the continuous implementation of the “Special Action Plan for the Prevention and Control of Spartina alterniflora (2022–2025)” which aims to eliminate more than 90% of S. alterniflora in all provinces in China by 2025, the continual high-spatial resolution monitoring of S. alterniflora is crucial to control secondary invasion and restore coastal wetlands.

Effect of Soil pH on the Uptake of Essential Elements by Tea Plant and Subsequent Impact on Growth and Leaf Quality

Tea plant is an acidophilic plant, and soil pH has an important effect on the absorption and enrichment of elements, tea plant growth and quality. In this study, rhizosphere soils and leaves of tea plants from 30 tea plantations were collected to determine soil pH and multi-element content of soil and leaves of tea plants, to obtain and validate key elements that are enriched by pH affecting tea plants, and to analyze the effects of pH on the growth and quality of tea plants. The results showed that soil pH significantly affected the enrichment of 15 elements by tea plants, and the enrichment coefficients of 11 elements (C, Mg, Si, N, P, Mn, Sr, Cd, S, Ca and Sb) tended to increase significantly with the increase of soil pH, while the opposite was true for the other four elements (Cu, Rb, Ba and Al). TOPSIS analysis showed that soil pH had the greatest effect on tea plant enrichment of seven elements, namely N (100%), Mn (43.32%), C (39.22%), P (27.66%), Sr (15.30%), Mg (13.41%) and Ba (10.47%). Pot experiments with tea seedlings also verified that soil pH significantly affected the enrichment of tea leaves for seven key elements. Moreover, with the increase of soil pH, the growth indexes, photosynthesis indexes and quality indexes of tea seedlings showed a significant upward trend. Interaction analysis showed that the enhanced enrichment of N, Mn, C, P, Sr and Mg by tea plants was beneficial to increase the photosynthetic capacity of tea plants, promote the growth of tea plants and improve the quality of tea leaves. This study provides an important theoretical basis for the cultivation and management of tea plants.

Multigenerational Adaptation Can Enhance the Pathogen Resistance of Plants via Changes in Rhizosphere Microbial Community Assembly.

Plants withstand pathogen attacks by recruiting beneficial bacteria to the rhizosphere and passing their legacy on to the next generation. However, the underlying mechanisms involved in this process remain unclear. In our study, we combined microbiomic and transcriptomic analyses to reveal how the rhizosphere microbiome assembled through multiple generations and defense-related genes expressed in Arabidopsis thaliana under pathogen attack stress. Our results showed that continuous exposure to the pathogen Pseudomonas syringae pv tomato DC3000 led to improved growth and increased disease resistance in a third generation of rps2 mutant Arabidopsis thaliana. It could be attributed to the enrichment of specific rhizosphere bacteria, such as Bacillus and Bacteroides. Pathways associated with plant immunity and growth in A. thaliana, such as MAPK signaling pathways, phytohormone signal transduction, ABC transporter proteins, and flavonoid biosynthesis, were activated under the influence of rhizosphere bacterial communities. Our findings provide a scientific basis for explaining the relationship between beneficial microbes and defense-related gene expression. Understanding microbial communities and the mechanisms involved in plant responses to disease can contribute to better plant management and reduction of pesticide use.

Winter wheat ear counting based on improved YOLOv7x and Kalman filter tracking algorithm with video streaming.

Accurate and real-time field wheat ear counting is of great significance for wheat yield prediction, genetic breeding and optimized planting management. In order to realize wheat ear detection and counting under the large-resolution Unmanned Aerial Vehicle (UAV) video, Space to depth (SPD) module was added to the deep learning model YOLOv7x. The Normalized Gaussian Wasserstein Distance (NWD) Loss function is designed to create a new detection model YOLOv7xSPD. The precision, recall, F1 score and AP of the model on the test set are 95.85%, 94.71%, 95.28%, and 94.99%, respectively. The AP value is 1.67% higher than that of YOLOv7x, and 10.41%, 39.32%, 2.96%, and 0.22% higher than that of Faster RCNN, SSD, YOLOv5s, and YOLOv7. YOLOv7xSPD is combined with the Kalman filter tracking and the Hungarian matching algorithm to establish a wheat ear counting model with the video flow, called YOLOv7xSPD Counter, which can realize real-time counting of wheat ears in the field. In the video with a resolution of 3840×2160, the detection frame rate of YOLOv7xSPD Counter is about 5.5FPS. The counting results are highly correlated with the ground truth number (R2=0.99), and can provide model basis for wheat yield prediction, genetic breeding and optimized planting management.

Funciones alométricas de altura total para pinos tropicales en Puebla, México

Tropical pines are important because of the amount of wood they produce in short periods. However, it requires allometric functions to predict the total tree height, since it is one of the critical variables to estimate both, it is the total and commercial volume. Accordingly, we aimed to develop a local and a generalized allometric function for model the total height-diameter (h-d) relationship of Pinus caribaea var. hondurensis and hybrid Pinus caribaea var. hondurensis × Pinus tecunumanii in the Sierra Norte of Puebla, México. In order to analyze the relationships, 1 232 h-d pairs were used. The allometric models were fitted with three parameters totaling 80% of the data and validated with 20%. The best model had the lowest average bias, root mean square error, and Akaike’s Information Criterion, as well as of the highest adjusted coefficient of determination. The models explained over 58.55% of the variability in observed total tree height. Nonetheless, the Gompertz and Weibull models obtained the best performances in fit and validation, and estimated h of tropical pines with less uncertainty. In consequence, the fitted allometric functions are reliable for predicting tree h; therefore, it can contribute to the sustainable management of commercial tropical pine plantations in the study region.

Safety Management and Accident-Control Strategy for a Commercial-Scale Plant for Supercritical Water Oxidation of Sludge

Supercritical water oxidation is a promising technology for decomposition of industrial wastewater and sludge. However, the system is operated under high temperature and pressure (usually higher than 500 °C and 25 MPa). Corrosion of component materials and salt deposition may lead to leakage or even the burst of the pressure vessel in the SCWO system, resulting in a high level of worry about the safe operation of the system. In this paper, the safety management and accident- control strategies are introduced according to a commercial-scale SCWO plant in China. The safety management strategy refers to the special design and operation strategies of some facilities. Different types of potential accidents are analyzed and the coping strategies for accidents of different levels of severity are described in detail. The strategies are valuable for the safe operation of commercial SCWO plants and other plants operated in high temperature and high pressure.

Maximizing economic potential and sustainability: Replanting waste management in West Kalimantan's oil palm plantations

Oil palm plantations in West Kalimantan cover the largest land area and yield considerable output. While palm oil is the primary product, these plantations also generate by-products, such as production waste, posing environmental risks. Consequently, replanting initiatives are undertaken to mitigate ecological impacts. However, independent replanting efforts still generate significant waste, including oil palm trunks and other biomass with substantial economic potential. Hence, it is crucial to estimate the potential waste from replanting activities and assess the economic benefits achievable through waste processing. Estimations reveal that annually, processing replanting waste into wood pellets could yield an economic value of IDR 125,376,725,586.59 for independent plantations, while maintaining carbon neutrality.

Environmental risk assessment and management of nuclear power plants based on big data analysis

In traditional research, monitoring data and samples are limited, and it is difficult to achieve ideal results in real-time monitoring and rapid response to environmental risks. By leveraging extensive environmental data gathered from nuclear power plants, the research employed machine learning methodologies for accurate feature selection and extraction of environmental parameters. An efficient environmental risk assessment model was successfully established by using a random forest algorithm. The 95% confidence interval for the area under the curve value spanned from 0.6894 to 0.9292. This provided a more dynamic and effective means for assessing and managing the environmental risks of nuclear power plants.

Impact of Fieldwork Courses on the Students’ Learning Experience and Academic Performance

Fieldwork courses offer hands-on learning experiences beyond traditional classrooms, aiming to merge theory with practice. This research seeks to identify the benefits of fieldwork courses and the impact of these benefits on the learning experiences and academic performance of undergraduate students enrolled in the Plantation Management Programme at Universiti Teknologi MARA (UiTM). To collect data, quantitative research utilising a survey methodology was employed. A series of questionnaires were distributed randomly using Google Forms, with responses received from 111 students. Statistical analyses, including descriptive, correlation, and regression analyses, were conducted using Statistical Package for Social Science version 28 to interpret the gathered data. Findings reveal that respondents strongly agree on the benefits of fieldwork, particularly in experiential learning (µ=4.13) and personal growth (µ=3.75). Correlation analysis indicates a significant relationship between experiential learning and personal growth (r (109) = .432, p = .001), with a 14.3% impact as per regression analysis. ANOVA confirms the significant effect of experiential learning on personal growth (p &lt; 0.001). Thus, the study confirms the hypothesis, demonstrating that fieldwork courses positively impact students' personal growth and confidence by exposing them to experiential learning.