Plant Maintenance Research Articles

Cassava Cultivation (Manihot esculenta) in the Rimbun Glugur Land, Sampecita Village, Kutalimbaru District, Deli Serdang

Cassava is one of the food crops widely cultivated by farmers in Glugur Rimbun, Sampecita Village. This study aims to compare cassava cultivation techniques, the influence of production factors (seedling area, labor and fertilizer) and to determine the feasibility of cassava farming in Glugur Rimbun land. Cassava plant cultivation is generally carried out traditionally and as is, without utilizing modern planting technology. Cassava plants are not cultivated with intensive care, both in terms of fertilization, watering, weeding or pests and diseases. The results of the study showed that cassava cultivation in Glugur Rimbun land, Sampecita Village, Kutalimbaru District was carried out conventionally. Cassava cultivation is carried out with a monoculture system starting from seed preparation, land processing, planting, fertilization and plant maintenance. Farmers have applied the correct cassava cultivation techniques, but cassava pest and disease control are not carried out by farmers. Feasibility analysis of cassava farming on 5 ha of land. Production costs incurred amounted to Rp. 98,000,000, - the total revenue was Rp. 146,281,250, - and the income was Rp. 48,281,250, -. The R/C value of 1.49. This shows that the cassava business on Glugur Rimbun land is feasible to cultivate. Keywords: Cultivation Techniques, Cassava

Deep Learning Mask R-CNN and Template Matching Algorithm For Tree Counting Analysis of Oil Palm Trees (Case Study: East Tanjung Jabung District, Jambi Province)

Abstract Indonesia is one of the largest producers and exporters of palm oil in the world. According to the Central Bureau of Statistics (BPS) in 2022 palm oil production has increased to 46.82 million tons. Palm oil has great potential; thus, technology is needed to analyze its productivity. Therefore, calculating palm tree percentages is an important aspect of land monitoring, plant maintenance, and efficient production planning. However, manual palm tree counting is time-consuming and labor-intensive and tends to be less efficient in large plantations. In addition, the lack of accuracy in estimating the number of trees and the age of crops can affect land planning and management. Algorithm development in geomatics science can automatically calculate oil palm trees using aerial photo data combined with template matching and deep learning methods, which are expected to provide efficient and accurate solutions. In this study, 620 samples of oil palm trees were trained. From the visual interpretation results, 6212 trees were obtained; from the processing results using the deep learning method, 6359 trees were obtained, and using the template matching method, 6756 trees were obtained. Through the validation test using the confusion matrix, the overall accuracy of the deep learning method was 95.15%, and the overall accuracy obtained using the template matching method was 87.17%.

Assessing transformer health through analysis of dissolved gases in cooling oil

Gases can form within the insulation system for various reasons while a transformer is in operation. If these gases are not promptly and properly managed, they can negatively impact the transformer's performance. Using the dissolved gas analysis (DGA) method to identify and assess the type and quantity of dissolved gases in transformer oil can uncover potential issues within the transformer. This information is crucial for guiding preventive maintenance and necessary repairs. Dissolved gas analysis testing was conducted by extracting transformer oil samples to identify signs of disturbances in the transformer based on the dissolved gas content. This research was conducted at the Paiton plant operations and maintenance services division. The condition of transformers was assessed by analyzing dissolved gases using the Rogers ratio method. Results indicate that the transformer at Paiton 9 is in good condition but overheating has occurred and requires treatment. Conversely, the transformers at Paiton 1 and 2 are in poor condition, showing signs of electrical faults and excessive heat. Despite several attempts to add inhibitors and conduct frequent testing, the transformers remained in poor condition, necessitating their replacement or repair.

Strategic plant maintenance planning in agriculture by integrating lean principles and optimization

Strategic plant maintenance planning in agriculture by integrating lean principles and optimization

Smart Planter: Simplifying Plant Care with AI Sensors

Abstract: This paper presents a smart plant care system developed to address the challenges of indoor plant maintenance for busy or novice plant owners. Leveraging AI sensors and IoT technology, the system continuously monitors crucial environmental parameters—soil moisture, temperature, and light exposure—to optimize plant health. Real-time data analytics and machine learning algorithms analyze this data, predicting care needs and automating responses like watering adjustments, with minimal user input. The user-friendly mobile app interface provides actionable insights, enabling remote monitoring and proactive care. This report covers the system’s layered architecture, practical benefits, implementation challenges, and potential for integration with smart home systems. It also explores future advancements in AI-driven plant care, emphasizing the potential of this technology to transform indoor gardening by making it more accessible, efficient, and precise.

PKM Institutional Strengthening of the Women's Farm Labor Group in Belantih Village, Kintamani District, Bangli Regency

Women farmers have almost the same role as male farmers, in fact they are directly involved in various stages of activities, especially during the planting and post-harvest period. The development of women farmers in rural areas is fostered through a group forum. To strengthen the institution of the Women Farmers group and focus on coaching, the implementation of the activity was carried out at the Mabi Subak Hall, Belantih Village. It began with a Coordination Meeting of the Farmer Women Group which was opened by the head of Belantih Village who was accompanied by the head of Banjar Mabi hamlet. The Women's Farm Workers Group (KWBT) is an agricultural institution created with the aim of facilitating the activities of women agricultural workers from the orange plant maintenance sector, farm worker equipment assistance, capital to innovation in processing post-harvest orange products into innovative products that have a selling value.

Cover crops influence the physical hydric quality of a tropical sandy soil under no-tillage cotton cropping

ABSTRACT Reduced stability and structural resilience expose sandy soils to physical degradation under intensive production systems. This study aimed to evaluate the influence of different cover crops (CC) under no-tillage system (NTS) cotton cropping, grown either individually or in combination, on the soil physical quality (SPQ) of a sandy loam Oxisol (Latossolo) in southeastern Brazil. To quantify the effects of CC on soil physical quality indicators, an experiment in randomized blocks design was implemented with the treatments: CC1: single cultivation of Ruzi grass; CC2: Intercropping of Pearl Millet + Ruzigrass; CC3: Intercropping of Pearl Millet + Velvet bean; CC Mixture : Intercropping of Ruzi grass, Pearl Millet and Velvet bean; and Control: maintenance of spontaneous plants. Undisturbed samples were taken from layers 0.00-0.10, 0.10-0.20, 0.20-0.40 and 0.40-0.60 m, in which were determined the soil bulk density (Bd), total porosity (TP), available water (AW), soil penetration resistance (PR) and the least limiting water range (LLWR). According to the multivariate analyses, the CC 2 treatment resulted in better SPQ compared to the other treatments. The LLWR, air-filled porosity (θ AFP ), and AW were responsible for differentiating the CC 2 treatment compared to the other treatments evaluated. Regardless of treatments, values of Bd> 1.75 Mg m -3 result in loss of SPQ due to the significant increase in PR and decreases in AW and LLWR. In the CC 3 and Control, there was a physical impediment caused by the increase in Bd in the 0.10-0.20 m layer, which could result in the confinement of cotton roots to the surface, making them more susceptible to water stress. Soil PR reached values greater than 2.5 MPa for water contents lower than the θ PWP in most treatments. In treatment CC3, for water contents equivalent to the θ PWP , the PR did not exceed 6 MPa, while for the other treatments, the PR reached values greater than 10 MPa. Only the PR was influenced using CC. Soil moisture is considered crucial to maintain PR below impeding values, determining the control of the physical quality of this sandy soil.

The photosynthetic traits of dominant species drive the multifunctionality of Liaodong oak (Quercus wutaishanica) communities in northern China

Ecosystem multifunctionality means that the ecosystem has the ability to provide multiple functions simultaneously. The study of the ecosystem multifunctionality provides an important basis for the understanding of the ecosystem function and management. Despite the plant community restoration is an important driver of changes in biodiversity and ecosystem multifunctionality, we still little know about the scaling effects the relationship between different dimensions of biodiversity and ecosystem multifunctionality. In this study, we investigated the relative contributions of different dimensions of plant diversity (e.g., species diversity, functional diversity and phylogenetic diversity) changes in ecosystem multifunctionality under different restoration stages (10, 30 and 40 years) in a human-damaged Liaodong oak (Quercus wutaishanica) plant communities in northern China. The results found that (1) ecosystem multifunctionality index was significantly higher in the middle (30 years) and late (40 years) stages of restoration than the early stage (10 years) of restoration. (2) Species richness and phylogenetic diversity were significantly higher in the early stage (10 years) of restoration than in the middle (30 years) and late (40 years) stages of restoration, however, functional dispersion was significantly higher in the later stages (40 years) of restoration than in the early (10 years) and middle stages (30 years) of restoration. (3) Ecosystem multifunctionality is primarily driven by photosynthetic traits of dominant species. The results of this study deepen the under-standing of the relationship between plant diversity and ecosystem multifunctionality in the forests of northern China by considering natural restoration after destruction, and contribute to the conservation of plant diversity and maintenance of ecosystem multifunctionality.

Autonomous Intelligent Monitoring of Photovoltaic Systems: An In‐Depth Multidisciplinary Review

ABSTRACTThis study presents a comprehensive multidisciplinary review of autonomous monitoring and analysis of large‐scale photovoltaic (PV) power plants using enabling technologies, namely artificial intelligence (AI), machine learning (ML), deep learning (DL), internet of things (IoT), unmanned aerial vehicle (UAV), and big data analytics (BDA), aiming to automate the entire condition monitoring procedures of PV systems. Autonomous monitoring and analysis is a novel concept for integrating various techniques, devices, systems, and platforms to further enhance the accuracy of PV monitoring, thereby improving the performance, reliability, and service life of PV systems. This review article covers current trends, recent research paths and developments, and future perspectives of autonomous monitoring and analysis for PV power plants. Additionally, this study identifies the main barriers and research routes for the autonomous and smart condition monitoring of PV systems, to address the current and future challenges of enabling the PV terawatt (TW) transition. The holistic review of the literature shows that the field of autonomous monitoring and analysis of PV plants is rapidly growing and is capable to significantly improve the efficiency and reliability of PV systems. It can also have significant benefits for PV plant operators and maintenance staff, such as reducing the downtime and the need for human operators in maintenance tasks, as well as increasing the generated energy.

Comprehensive Strategies for Addressing Tube and Tubesheet Joint Leaks in Shell-and-Tube Heat Exchangers

Heat exchanger tube leakage severely affects the ability of the heat exchanger to maintain its process temperature and energy efficiency. A comprehensive strategy for heat exchanger repair is presented, focusing on addressing leaks of varying types and locations within the tubesheet. The approach considers the historical context of recurring leaks to formulate repair decisions effectively. Techniques such as tube plugging, installation of weld plug, and double plug for tubesheet joint leaks are demonstrated. Moreover, the implementation of individual tube hydro testing and insurance plugging is highlighted to ensure sustained heat exchanger functionality. A novel aspect of this paper is the inclusion of a decision-making flowchart, offering practical guidance to plant personnel encountering diverse leak scenarios in the industrial process. By integrating historical data and considering leak specifics, plant maintenance teams can streamline repair processes, thereby optimizing heat exchanger performance and longevity in various industrial applications.

Trend‐Based Predictive Maintenance and Fault Detection Analytics for Photovoltaic Power Plants

Optimized predictive maintenance in photovoltaic (PV) systems is crucial for ensuring prolonged operational performance and cost‐effective operation and maintenance (O&M). Even though failure detection methods have already been developed, the main challenge remains the lack of predictive maintenance strategies to accurately forecast underperformance conditions. The scope of this work is to develop a predictive maintenance and failure detection routine for assessing the health status of PV systems. The workflow consists of the eXtreme gradient boosting algorithm for modeling the PV performance, the one‐class support vector machine algorithm for fault detection, and the Facebook Prophet algorithm for forecasting PV performance trends and generating maintenance alerts. The developed data‐driven routine analyzes performance trend deviations and it is validated using a historical dataset from a utility‐scale PV power plant in Greece. The obtained results show the effectiveness of the developed workflow in detecting fault conditions, achieving a sensitivity of 96.9%. Additionally, the results demonstrate the workflow's ability to generate predictive maintenance alerts up to 7 days in advance, yielding a sensitivity of 92.9%. Finally, the study provides useful insights that enhance operators’ efficiency in conducting O&M activities.

PENINGKATAN KETERAMPILAN PETERNAK MELALUI PELATIHAN BUDIDAYA HIJAUAN MAKANAN TERNAK UNGGUL DI KELURAHAN WATUMOTOBE, KECAMATAN KAPONTORI, KABUPATEN BUTON

Watumotobe Village in Kapontori District, Buton Regency, has the potential to develop forage (HMT) to support the availability of quality feed for local livestock. However, the limited knowledge of local farmers regarding superior HMT cultivation techniques is a challenge. This community service program aims to improve the skills of farmers through training in planting HMT such as odot grass, molato, red elephant grass, pakchong, and indigofera. The training method involves delivering interactive materials, field practice in demonstration plots, and technical assistance in plant maintenance. The results of the activities showed an increase in understanding and skills of farmers in cultivating HMT, which is expected to support independent and sustainable livestock feed security. This program also encourages cooperation between farmers in managing local forage resources as a step towards feed self-sufficiency in Buton Regency.

Membrane-informed multi-mechanistic predictive maintenance for MBR plants: Early determination of membrane cleaning with biologically driven, physically deposited, and chemically induced fouling model

Membrane bioreactors (MBRs) are widely employed in wastewater treatment for their superior performance, though maintaining membrane efficiency remains costly and energy-intensive because of fouling accumulation. This study introduces a novel membrane-informed predictive maintenance (membrane-PM) system that accurately predicts cleaning intervals for membrane fouling in a full-scale MBR plant. By integrating biologically informed, physically deposited, and chemically induced fouling data via an activated sludge model, resistance-in-series model, and multiple linear regression model, we captured the complex dynamics of fouling. A day-to-day calibration approach, utilizing global sensitivity analysis and a genetic algorithm (GA), improves model precision by reflecting temporal fouling changes. Additionally, membrane-informed multivariate statistical monitoring (membrane-MSM), based on Hotelling's T2 statistic, was developed to predict optimal chemical cleaning intervals, helping to prevent MBR operational failures. Results indicate that the membrane-PM system effectively estimated membrane fouling progress via transmembrane pressure (TMP) with an R2 of 88.4 %, achieving high accuracy and extending membrane operational lifespan by an average of 17.5 %.

Strategies for sustainability local sawang durian in North Aceh District

Abstract With this existing potential, there are still fundamental problems that are still faced in the process. Most of the farming patterns are still multicultural systems using traditional technology. This research aims to: formulate strategies that can be implemented in the development of local Sawang Durian in North Aceh Regency. The research method used is SWOT analysis to obtain a strategy for the development of local Sawang Durian. The results of determining internal factor indicators (strengths-weaknesses), for strength factors, namely farmers’ experience, potential regional conditions or topography, land area owned, unique quality of durian fruit, easy plant maintenance process. Meanwhile, the weaknesses are the adoption of technology which is still simple, there is no guarantee of availability per season, lack of capital or is limited, the production capacity is still lacking or the quality and quantity is not yet continuous, there is no activity of farmer groups, while determining external factor indicators (opportunities-threats) for opportunity factors, namely import restriction policy, high market demand for durian, selling prices, availability of production facilities. Meanwhile, the threat factors are the presence of competitors from other regions, the threat of pests and diseases, unstable weather changes.

Evaluating Policy Shifts on Perceived Greenspace Quality: Applying Regression Discontinuity During the COVID-19 Reopening Period

Abstract: Urban greenspaces have been essential in supporting residents’ well-being during the COVID-19 pandemic, particularly under strict lockdown measures. However, the impact of changing containment policies on residents’ perceived greenspace quality remains insufficiently explored. This study utilized online survey data collected between 11 October and 29 December 2022, in Shanghai, coinciding with the major policy shift on 5 December 2022. A probability proportionate to size sampling was adopted to survey residents aged 18 and above who had lived in the city for at least six months, yielding a total of 577 valid responses. We assessed residents’ perceived greenspace quality using 20 park- and community-level variables, focusing on both overall quality and specific features of greenspaces. A regression discontinuity design (RDD) was applied to evaluate how the lifting of the COVID-19 policies influenced residents’ perceptions of parks and community greenspaces. Our RDD estimation indicates no statistically significant change in residents’ overall perceived quality of parks after the policy shift, except for increased satisfaction with specific features such as plant diversity, maintenance, seating areas, trails, and large open spaces. In contrast, residents who responded after the policy shift reported a significantly higher perceived quality of community greenspaces compared to those who completed the survey before the shift (0.609 score difference, p < 0.01), with notable increases in satisfaction regarding plant diversity, maintenance, and seating areas. Perception of plant quantity remained unchanged in both types of greenspaces. Residents expressed greater satisfaction with sports facilities in parks, while community greenspaces were preferred for their water features and esthetic qualities. By adopting an RDD with a unique dataset, this study contributes empirical evidence to the current ongoing debate on the role of urban greenspace during the later stages of COVID-19. Specifically, it examines how changes in public health policy and the resulting increase in mobility might affect residents’ perceived greenspace quality. The findings can assist decision-makers and urban planners in developing more adaptive strategies to address the diverse needs of residents for greenspaces during the transitional period of a public health crisis.

Hazard Identification and Risk Assessment During Simultaneous Operations in Industrial Plant Maintenance Based on Job Safety Analysis

The risk of accidents during simultaneous operations (SIMOPS) in plant maintenance has been increasing. However, research on methods to prevent such accidents has been limited. This study aims to develop a novel framework, hazard identification and risk assessment of simultaneous operations (HIRAS), for identifying and evaluating potential hazards during concurrent tasks. The framework developed herein is expected to be an effective safety management tool that can help prevent accidents during these operations. To this end, the job location and hazard information in job safety analysis (JSA) were standardized into four attributes. The standardized information was then synchronized spatially and temporally to develop a HIRAS model that identifies and assesses the impact of hazards between operations. The model was tested using 40 JSA documents corresponding to maintenance operations at Company P, a South Korean steel-making company. The model was tested in two scenarios: one with planned operations and the other with unplanned operations in addition to planned operations. The performance evaluation results of the first scenario showed an F1-score of 98.33%. In this case, a recall of 97.52% means that the model identified 97.52% of the hazard-inducing factors. The second scenario was compared with the results of a review by six subject matter experts (SMEs). The comparison of the results identified by the SMEs and the model showed an accuracy of 89.3%. This study demonstrates the potential of JSA, which incorporates the domain knowledge of workers and can be used not only for individual tasks but also as a safety management tool for surrounding operations. Furthermore, by improving the plant maintenance work environment, it is expected to prevent accidents, protect workers’ lives and health, and contribute to the long-term sustainable management of companies.

Advancing photovoltaic panel temperature forecasting: A comparative study of numerical simulation and machine learning in two types of PV power plant

Photovoltaic (PV) panel temperature dynamic monitoring and forecasting is important for managing and maintaining of PV power plant. However, it is uncommon to use a variety of methods to predict and evaluate the panel temperature of different types of PV power plants. Therefore, this study aims to advance PV panel temperature forecasting through a comparative analysis of numerical simulation and machine learning models in two types of PV power plants: land- and water-mounted. The results indicate that PV panel temperature condition for two types of PV power plants can be well captured by the numerical simulation (NS) and machine learning, except for the NS in water-mounted PV power plant (R2 with 0.66). Models perform better in land-mounted PV power plants, with Random Forest Regression (RFR) and ResNet models demonstrating superior accuracy across both environments. While all models exhibit some degree of deviation from normal distribution in residuals, RFR and ResNet show the least deviation and lowest prediction errors, highlighting their robustness in forecasting PV panel temperatures. This study can provide valuable insights for predicting PV panel temperatures and for the management and maintenance of PV power plants.

A Novel Ultrasonic Leak Detection System in Nuclear Power Plants Using Rigid Guide Tubes with FCOG and SNR.

Leak detection in nuclear reactor coolant systems is crucial for maintaining the safety and operational integrity of nuclear power plants. Traditional leak detection methods, such as acoustic emission sensors and spectroscopy, face challenges in sensitivity, response time, and accurate leak localization, particularly in complex piping systems. In this study, we propose a novel leak detection approach that incorporates a rigid guide tube into the insulation layer surrounding reactor coolant pipes and combines this with an advanced detection criterion based on Frequency Center of Gravity shifts and Signal-to-Noise Ratio analysis. This dual-method strategy significantly improves the sensitivity and accuracy of leak detection by providing a stable transmission path for ultrasonic signals and enabling robust signal analysis. The rigid guide tube-based system, along with the integrated criteria, addresses several limitations of existing technologies, including the detection of minor leaks and the complexity of installation and maintenance. By enhancing the early detection of leaks and enabling precise localization, this approach contributes to increased reactor safety, reduced downtime, and lower operational costs. Experimental evaluations demonstrate the system's effectiveness, focusing on its potential as a valuable addition to the current array of nuclear power plant maintenance technologies. Future research will focus on optimizing key parameters, such as the threshold frequency shift (Δf) and the number of randomly selected frequencies (N), using machine learning techniques to further enhance the system's accuracy and reliability in various reactor environments.

Economic Overhaul Mode of Steam Engine Professional Equipment in Thermal Power Plant

To ensure that the daily production activities of thermal power plants can produce their due effect in the production and business activities, it is necessary to carry out efficient and orderly maintenance work on the professional equipment of steam engines. However, the maintenance work of steam engine professional equipment in thermal power plants usually uses high-cost expenditures. Therefore, how to take effective measures to reduce the cost of professional equipment maintenance in thermal power plants has become a problem that needs to be solved before such maintenance can proceed. Among them, through the application of economic maintenance equipment in thermal power plants, the actual production and operation costs can be effectively reduced. Based on this, the author will analyze the application of the model of economic maintenance of steam engine professional equipment in thermal power plants.

Foliar solution hydrophobicity influenced by plant species, product, and concentration

AbstractFoliar application of nutrients and pesticides is a popular practice for plant maintenance and weed control. Understanding how different products and adjuvants interact with the intended plant leaf surface will improve uptake and decrease losses. Various fertilizer, adjuvant, and pesticide solutions were tested to determine solution hydrophobicity on St. Augustinegrass, tall fescue, and white clover. A 1 µL droplet of each solution was placed on the adaxial leaf surface and imaged with a stereomicroscope to determine average solution contact angle. For all solutions groups, plant species had the most consistent effect, with white clover having the highest average contact angle followed by St. Augustinegrass and tall fescue, respectively. All product solutions significantly reduced the average contact angle compared to water. The urea solution resulted in the largest decrease for the fertilizer group at 7.6%. The largest decrease was found with the siloxane nonionic surfactant, which resulted in a decrease of 87.5% compared to water alone. Significant interactions were found between plant species and product solutions, indicating product solutions interact differently dependent upon the leaf surface characteristics. The results suggest that plant leaf surface, product solution and concentration, and their combination should be accounted for to increase wettability of a foliar application and improve product absorption.