Journal of Agrocomplex and Engineering E-ISSN 3109-9955 is a peer-reviewed academic journal that publishes original research, reviews, and technical papers across a wide range of disciplines. The journal aims to promote interdisciplinary studies and innovative solutions in the fields of agriculture, plantations, fisheries, forestry, marine, environment, agricultural engineering, architectural engineering, industrial engineering, civil engineering, geology, computer engineering, electrical engineering, and marine engineering. It serves as a platform for researchers, academics, and industry professionals to share knowledge and advancements that contribute to sustainable development and technological innovation. Published Month: January, March, May, July, September & November
Development of an Artificial Intelligence-Integrated School Financial Management Application
Purpose: This study aims to address persistent inefficiencies in school financial management, particularly in recording accounts payable and receivable, by developing an integrated desktop-based financial management application that supports accuracy, efficiency, and transparency. Subjects and Methods: The study focuses on school financial administration processes, with school treasurers as the primary users of the system. A desktop-based application was developed using Visual Basic .NET with SQLite as the database, following the Waterfall development model. To enhance usability, the system integrates an Artificial Intelligence (AI) Support feature based on Natural Language Processing (NLP). This feature allows users to input multiple financial transactions simultaneously using natural language commands, which are processed through a Rule-Based algorithm combined with Regular Expressions. System testing was conducted to evaluate functionality, efficiency, security, and output accuracy. Results: The testing results show that the application significantly accelerates transaction recording, reduces human errors in data entry, and improves data security through encryption mechanisms. The AI-assisted input feature enables more flexible and intuitive interaction, while automated financial report generation ensures consistency and accuracy. These findings demonstrate that integrating AI into school financial systems can effectively streamline administrative workflows. Conclusions: The study concludes that the proposed system provides a practical and scalable solution for improving school financial management. AI-assisted transaction input reduces workload and operational delays, while the structured database enhances traceability and long-term financial record management.
Copyright (c) 2026 Journal of Agrocomplex and Engineering https://creativecommons.org/licenses/by-sa/4.0/
Aeroponic System Engineering to Optimize Horticultural Plant Growth
Purpose: This study aims to evaluate the effectiveness of aeroponic systems in optimizing horticultural plant growth compared to conventional soil-based cultivation methods, with a focus on vegetative growth, biomass accumulation, and water use efficiency. Subjects and Methods: The research subjects were lettuce horticultural plants cultivated for eight weeks in two different systems, aeroponic and conventional. In the aeroponic system, plants were grown with roots suspended in the air and sprayed with nutrient solution in the form of mist at certain intervals, while in the conventional system, plants were grown in soil media with standard fertilization. Parameters observed included plant height, number of leaves, wet and dry weight, and water use efficiency. Data were analyzed descriptively and comparatively by calculating the average, standard deviation, and percentage increase for each treatment. Results: The results showed that the aeroponic system provided significant improvements in all growth parameters compared to the conventional method. Aeroponic plants reached an average height of 35.4 cm in the 8th week, higher than 27.8 cm in the conventional system. The number of leaves in the aeroponic system reached 30, while the conventional system had 23. The wet and dry weights of aeroponic were 245.3 g and 45.6 g, respectively, greater than 178.4 g and 32.7 g in the conventional system. The water use efficiency of aeroponics was also much higher, namely 19.6 g/L compared to 5.6 g/L in the conventional system. Conclusions: Aeroponic systems have proven superior in enhancing horticultural plant growth while simultaneously conserving water. These findings support aeroponics as a sustainable, efficient, and relevant alternative modern agricultural technology for application in areas with limited land and water resources.
Copyright (c) 2024 Journal of Agrocomplex and Engineering https://creativecommons.org/licenses/by-sa/4.0/
Microhydro Water Turbine Design for Energy Supply in Remote Rural Areas
Purpose: This study investigates the design and validation of a microhydro turbine system as a decentralized energy solution for rural communities in remote areas. The research aims to demonstrate the technical, economic, and social feasibility of microhydro as a sustainable alternative to diesel-based electricity Subjects and Methods: The study was conducted in a rural catchment with an average discharge of 120 L/s and a net head of 14.8 m. Hydrological data were collected over a 60-day period using flow and pressure loggers to establish flow duration curves. A 2 kW crossflow turbine prototype was constructed and tested to evaluate efficiency, voltage stability, and frequency regulation. Economic performance was assessed through Levelized Cost of Energy (LCOE) analysis, incorporating capital and operating expenditures. Social acceptance was examined through surveys and focus group discussions with 42 households, focusing on willingness to pay, perceived benefits, and maintenance concerns. Results: The hydrological assessment revealed a hydraulic potential of 17.4 kW at mean flow, with Q60 selected as the design discharge. Prototype testing achieved a peak efficiency of 68% and stable voltage (220 V ± 4%) and frequency (49.7–50.3 Hz). The LCOE was calculated at USD 0.14/kWh, significantly below the local diesel benchmark (USD 0.32–0.38/kWh), and remained viable under sensitivity scenarios. Social surveys showed high acceptance, with 83% of households willing to pay and 91% reporting positive benefits. Conclusions: Microhydro, when designed with context-specific hydrological, technical, and social considerations, represents a reliable and cost-effective pathway for sustainable rural electrification in remote communities.
Copyright (c) 2025 Journal of Agrocomplex and Engineering https://creativecommons.org/licenses/by-sa/4.0/
Utilization of Palm Oil Waste as Raw Material for Making Environmentally Friendly Biobriquettes
Purpose: This study aims to analyze the potential of palm oil waste specifically shells, fibers, and empty fruit bunches (EFB) as raw materials for producing environmentally friendly biobriquettes. The research evaluates their chemical composition, mechanical properties, and thermal performance to determine the most suitable biomass source for high-quality fuel production. Subjects and Methods: The materials used were palm kernel shells, fibers, and EFB obtained from palm oil mills. Each biomass type underwent cleaning, drying, milling, carbonization at 400–500°C, and briquette formation using 5% tapioca starch adhesive. Laboratory analyses included moisture, lignin, cellulose, hemicellulose, ash content, calorific value, emissions, mechanical strength, and thermal behavior. Data were processed using descriptive-comparative analysis to identify performance differences. Results: Shells showed the highest lignin and cellulose levels, improved compaction, and superior calorific value (18.2 MJ/kg). They also produced the lowest emissions and exhibited excellent mechanical durability. Fiber briquettes demonstrated moderate quality, with acceptable strength and combustion performance but higher moisture and ash content. EFB briquettes recorded the lowest density, highest ash content, weakest strength, and lowest calorific value (12.3 MJ/kg), making them less efficient unless blended with other biomass. Conclusions: Palm kernel shells are the most promising raw material for biobriquette production, while fibers remain viable alternatives. EFB is better used in mixed formulations. Overall, palm oil waste presents significant potential for renewable energy development and sustainable waste management.
Customer Data Management Information System at PT. Wifi Ceria Using PIECES
Purpose: This research was conducted with the aim of exploring, developing, and improving the customer data processing system. This is done to improve efficiency, accuracy, and responsiveness in facing increasingly dynamic market competition. Subjects and Methods: Currently, PT. Wifi Ceria still faces several problems in the customer data processing process, such as recording new customers, installing WiFi services, managing complaints, processing payments, and preparing company reports. All of these processes still use conventional methods. Results: In this study, we apply the PIECES concept (Performance, Information, Economy, Control, Efficiency, and Service) in developing a customer data processing system. The main goal is to increase the effectiveness and efficiency in managing and analyzing customer data. Conclusions: In designing a website using the Unified Modeling Language (UML) method and implementing it with the Html programming language. To test the system, we apply the Black Box Testing method. The result of this study is a web-based information system that makes it easier for PT. Wifi Ceria to provide services to customers.
Microhydro Water Turbine Design and Construction for Energy Supply in Remote Rural Areas
Purpose: The design and development of microhydro turbines represent an effective solution for addressing energy access challenges in remote rural areas. This study focuses on the design, simulation, and laboratory testing of a Crossflow microhydro turbine tailored for rural electrification. Subjects and Methods: Hydrological data from a representative rural site were analyzed to determine flow rate and head potential. Based on these parameters, a Crossflow turbine prototype was designed using locally available materials. The design was validated through Computational Fluid Dynamics (CFD) simulations and prototype laboratory testing. Results: The results indicate that the turbine operates with an efficiency range of 55–70%, with optimal performance achieved at a flow rate of 0.15 m³/s and a head of 12 meters, producing up to 8 kW of output power. While the efficiency is slightly lower than industrial-scale designs, the system remains sufficient to meet household and community-level electricity needs. Conclusions: This research highlights the potential of locally fabricated microhydro systems to provide reliable, low-cost, and environmentally friendly energy solutions for rural communities. Limitations include the absence of field testing and economic feasibility analysis, which should be addressed in future studies.
Innovation of Smart Irrigation System Based on Internet of Things (IoT) for Efficient Water Use on Agricultural Land
Purpose: This study aims to analyze the effectiveness of a smart irrigation system based on the Internet of Things (IoT) in improving water use efficiency, maintaining soil stability, increasing crop productivity, and reducing operational costs in agricultural practices. Subjects and Methods: The research was conducted on chili plants (Capsicum annuum) using a field experiment approach with two treatments: conventional irrigation (manual, schedule-based) and IoT-based smart irrigation. The IoT system consisted of soil moisture and temperature sensors integrated with a microcontroller to automatically regulate water supply according to real-time soil conditions. Data were collected on water consumption, soil moisture and temperature stability, crop productivity, labor costs, and farmer satisfaction. Quantitative descriptive analysis was applied to compare the two systems, while an independent sample t-test was used to test the significance of differences in water efficiency and productivity. Farmer satisfaction was measured using a Likert scale questionnaire. Results: The findings showed that the IoT-based irrigation system reduced water usage by 63% compared to txhe conventional method, while maintaining soil moisture within the optimal range of 40–55%. Crop productivity increased by 27% in terms of average yield per plant, and fruit quality also improved. Operational costs decreased by 25% and working hours for irrigation were reduced by 60%. Farmers reported higher satisfaction with the IoT system, rating it more practical and efficient. Conclusions: IoT-based smart irrigation effectively improves water efficiency, stabilizes soil conditions, enhances crop productivity, and reduces costs. This innovation demonstrates strong potential to support sustainable agriculture and address water resource challenges.
Analysis of the Application of Precision Farming Technology in Increasing Rice Productivity in Indonesia
Purpose: This study aims to analyze the impact of precision farming technology on the productivity and sustainability of rice cultivation in Indonesia. The research focuses on evaluating improvements in water efficiency, nutrient management, yield performance, and socio-economic outcomes when compared with conventional farming practices. Subjects and Methods: The study was conducted on selected rice fields across different regions of Indonesia. A comparative experimental design was employed, where one group of fields used conventional irrigation and nutrient practices, while the other applied precision farming technologies, including automated irrigation, soil moisture sensors, and site-specific fertilizer management. Data collection included field measurements, farmer surveys, and documentation from agricultural offices. Descriptive statistics were used to provide an overview, while inferential analyses (t-tests, ANOVA, and regression) tested the significance of differences in efficiency and productivity. Results: The findings indicate that precision farming reduced water consumption by 37% per cycle and fertilizer input by 22%, while maintaining soil stability. Rice yields increased by 23%, and the proportion of high-quality grains improved by 15%. Socio-economic benefits included a 30% reduction in labor costs, a 50% decrease in irrigation-related work time, and higher farmer satisfaction scores. These results confirm that precision farming not only enhances agronomic efficiency but also strengthens farmer livelihoods. Conclusions: Precision farming demonstrates strong potential for modernizing Indonesian rice cultivation by improving resource efficiency, increasing crop productivity, and supporting sustainable agriculture. Its large-scale adoption, however, requires supportive policies, affordable access to technology, and continuous farmer training to maximize long-term benefits.
Study on the Effectiveness of Environmentally Friendly Breakwater Development in High Erosion Coastal Areas
Purpose: This study investigates the effectiveness of environmentally friendly breakwaters as a sustainable solution to mitigate severe coastal erosion. The research aims to evaluate not only the engineering performance of the structure but also its ecological impacts and community acceptance, thereby addressing the multidimensional nature of coastal protection. Subjects and Methods: A Before–After–Control–Impact (BACI) design was employed to compare shoreline conditions at intervention and control sites. Hydrodynamic data were collected using Acoustic Doppler Current Profilers (ADCPs), while shoreline changes were monitored with UAV-based photogrammetry and analyzed in DSAS. Sediment samples, ecological indicators (benthic diversity, vegetation cover, water quality), and socio-economic perceptions (n = 64 respondents) were assessed. Numerical modeling with SWAN and XBeach complemented field observations. Results: The breakwater reduced significant wave height by 42%, with an average transmission coefficient (Kt) of 0.58. Shoreline trajectories shifted from net retreat (–1.1 m/year) to modest accretion (+0.4 m/year), and the sediment budget indicated a net gain of +6,250 m³. Ecological responses included increased benthic diversity (H’ rising from 1.62 to 2.05), expanded vegetation cover (+12.5%), and improved water clarity (turbidity reduced from 12.5 NTU to 7.9 NTU). Community surveys revealed strong acceptance, with 78% perceiving reduced coastal risk and 65% reporting improved fishing conditions, though concerns regarding maintenance costs persisted. Conclusions: nvironmentally friendly breakwaters proved effective in reducing erosion, enhancing ecological functions, and gaining community support. These findings highlight their potential as viable alternatives to conventional hard defenses, particularly when integrated with broader nature-based solutions.
Development of a Wireless Sensor-Based Shrimp Pond Water Quality Monitoring System
Purpose: The study aims to design, develop, and validate a wireless sensor-based monitoring system for shrimp ponds, addressing the limitations of conventional manual monitoring in capturing dynamic fluctuations of critical water quality parameters. Subjects and Methods: The system was equipped with low-power sensors to measure dissolved oxygen (DO), pH, temperature, and salinity, integrated with solar-assisted power, wireless transmission, and a cloud-based dashboard. Calibration and validation were first conducted under laboratory conditions, followed by an eight-week field deployment across three shrimp ponds. Accuracy was evaluated against standard reference instruments, while network performance and energy autonomy were continuously monitored. Results: Laboratory calibration achieved high accuracy with RMSE values of 0.08 for pH, 0.18 mg/L for DO, 0.15 °C for temperature, and 0.42 ppt for salinity. Field trials generated more than 190,000 valid measurements, revealing consistent diurnal water quality dynamics. Sensor data showed strong concordance with manual reference measurements (concordance correlation coefficient > 0.95). The wireless communication network achieved a packet delivery ratio of 96.7% with median latency of 2.3 seconds. Solar-assisted nodes maintained uninterrupted operation for more than 30 days, ensuring system robustness in outdoor aquaculture conditions. Conclusions: The developed wireless sensor-based monitoring system proved reliable for real-time aquaculture applications, offering both technical accuracy and practical benefits for farm management. The system enabled proactive interventions, such as timely aeration during pre-dawn oxygen depletion, thereby reducing shrimp stress and mortality risk. Future improvements will focus on enhancing sensor resistance to biofouling and integrating predictive analytics for decision support. Overall, the study demonstrates the feasibility of Internet of Things (IoT)-enabled solutions to advance sustainable and precision shrimp aquaculture.
Evaluation of Agroforestry Strategies in Climate Change Mitigation in Tropical Forest Areas
Purpose: This study examines the role of agroforestry as a climate change mitigation strategy in tropical forest regions by integrating biophysical and socio-economic perspectives. It aims to quantify carbon sequestration potential, evaluate the consistency between field-based and remote sensing methods, and assess the livelihood benefits associated with agroforestry adoption. Subjects and Methods: The research was conducted in three tropical landscapes using 150 permanent sample plots representing multistrata agroforestry, shaded monocultures, and conventional monocropping systems. Carbon stocks were estimated through tree inventories and soil organic carbon sampling, complemented by remote sensing data from Landsat 8, Sentinel-2, and airborne LiDAR calibrated with field measurements. Socio-economic data were collected from 320 farming households, and econometric approaches, including propensity score matching and difference-in-differences, were applied to evaluate livelihood impacts. Uncertainty was addressed using Bayesian hierarchical modeling and Monte Carlo simulations. Results: Multistrata agroforestry systems stored substantially higher carbon stocks than shaded monocultures and monocropping systems. At the landscape scale, agroforestry expansion contributed significant cumulative carbon sequestration over a ten-year period. Remote sensing estimates showed strong agreement with field-based measurements. Economically, agroforestry adopters achieved higher farm income while maintaining elevated carbon stocks. Conclusions: Agroforestry represents an effective and inclusive climate mitigation strategy that simultaneously enhances carbon sequestration and rural livelihoods. These findings support the integration of agroforestry into climate policies, emphasizing the importance of land tenure security, extension services, and access to carbon financing mechanisms.
Bu Murni Restaurant Sales Information System Based on Website
Purpose: The purpose of the research on the sales information system of Bu Murni's restaurant based on the website is to increase the efficiency of the sales process, facilitate data management, provide easy access for customers and expand marketing reach. Subjects and Methods: Qualitative Method is a research approach used to understand the phenomenon, perspective, or meaning of a social event in depth. This method usually focuses on collecting non-numerical data such as interviews, observations, or documents to obtain a richer and more complex picture of a problem or situation. UML (Unified Modeling Language) is a standard modeling language designed to document the description of software systems. UML also helps analysts and other stakeholders understand, design and communicate the structure and behavior of the system. UML consists of system requirements analysis, activity diagrams, usecase diagrams, databases, creating a Database Using MySQL, Table Design, Determining Relationships Between Tables and Using Normalization Results: This information system provides features for managing food menu data, sales transactions, and financial reports that can be accessed in real-time through a MySQL-based web interface. The system successfully meets the needs of restaurants by automating the ordering and reporting process, which was previously done manually. With this system, the time needed to process orders and errors in recording raw material stock are successfully minimized. The test results show that the system can process orders with accuracy and effective time. Conclusions: The website-based sales information system for Rumah Makan Bu Murni was successfully designed with the aim of increasing efficiency and accuracy in managing sales data. This system provides features that support integrated management of menus, orders, customers, and employees. With this website, customers can easily place orders, either directly on the spot or online, thus providing more flexibility in service.
Sentiment Analysis Towards Rohingya on Instagram Using Support Vector Machine Method
Purpose: This study investigates the application of the Support Vector Machine (SVM) method for sentiment analysis of Instagram posts related to the Rohingya crisis. This study investigates the application of the Support Vector Machine (SVM) method for sentiment analysis of Instagram posts related to the Rohingya crisis. The primary aim was to assess the effectiveness of SVM in classifying sentiments expressed in social media content, particularly focusing on the emotional complexity and nuances inherent in such sensitive topics. Subjects and Methods: Data were collected from Instagram posts using hashtags related to the Rohingya crisis, and the SVM model was applied to classify sentiments into categories such as positive, negative, and neutral. Results: The results demonstrated that the SVM method outperformed traditional sentiment analysis techniques, achieving higher accuracy, precision, recall, and F1 scores. This suggests that machine learning techniques, specifically SVM, offer a more accurate and reliable approach for analyzing public opinion on social media platforms, which is crucial for shaping humanitarian discourse and policy. Conclusions: Future research should consider the integration of deep learning models for further improvements in sentiment analysis performance.
Redesigning the Layout of Plastic Waste Processing Production Facilities at PT KIMA Using the Computerized Relationship Layout Planning (CORELAP) Method
Purpose: This study explores the effectiveness of the Computerized Relationship Layout Planning (CORELAP) method in optimizing the layout of plastic waste processing production facilities at PT KIMA. Subjects and Methods: A quasi-experimental design was employed, involving a pre-test and post-test assessment of key performance metrics such as material transfer distances, production time, efficiency, and equipment downtime. Results: The results demonstrated significant improvements in operational efficiency for the experimental group using CORELAP, with material transfer distances reduced by 31.75%, production time decreased by 30%, and equipment downtime lowered by 13.8%. In comparison, the control group, which followed traditional layout practices, showed minimal improvements. These findings highlight the potential of the CORELAP method to enhance workflow efficiency, reduce operational costs, and improve overall productivity in industrial settings. Conclusions: The study underscores the importance of modern layout optimization techniques in achieving both immediate and long-term operational gains, especially in resource-intensive industries such as waste management and recycling.
Design Application Service E-Commerce Web Based Ornamental Plants
Purpose: The development of the internet today has had a major impact on the progress of various industries. The sales system used in the ornamental plant sales process goods are still manual which are accessed by customers by visiting the location and seeing the product directly. The purpose of this study is to design a web-based ornamental plant e-commerce service application system to facilitate sales and purchase transactions. Subjects and Methods: The testing method used is the black-box testing method, where the system is executed through trials and checking the functionality of the software. Results: The results of testing this application are that customers register, so that customers get an account then customers log into the application to choose ornamental plants offered in the application, after finding the desired plant, then fill in the shipping address and type of payment which then forms information with the order status in process. Conclusions: The conclusion of the testing of the ornamental plant e-commerce application system is that it is in accordance with what customers want in providing easy access to the application practically.
Automation and Robotics in the Agrocomplex for Increased Agricultural Efficiency
Purpose: The elevation of the population standards of living, coupled with the need for feeding people all over the world, has led to the expansion of the agrocomplex together with the recognition of automation and robotics as the solution to the industry’s many issues. The paper explores automation and robotics and its effects on agriculture in terms of rates of production; relative cost and effectiveness of automations and robotics in agricultural operations; and completing the set, efficiency rates of enterprises engaged in agricultural activities. As part of the data collection two research questions were posed and quantitative data was obtained from 120 respondents engaged in automated farming. Subjects and Methods: As part of the data collection two research questions were posed and quantitative data was obtained from 120 respondents engaged in automated farming. To test the correlation between automation and agricultural performance indicators, descriptive and correlational statistics tools that include Pearson correlation coefficient, multiple regression analysis and analysis of variance were used. Results: This study shows that through the proper demonstration of how automation enhances agricultural boosts sustainability and calls for better measures to embrace the change in the developing countries. Conclusions: the findings the work established a high level of significant this Positive of the level of automation on the efficiency of agriculture, costs, and time of automation systems. Furthermore, the study reveals gaps such as high initial cost required for investments and a dearth of technical know-how as pulling factors that make it difficult for many farmers especially the smallholder farmers to embrace it. The findings are useful in filling the existing literature void on the monetisable effect of automation in farming, providing a quantitative research reference point in the future.
Designing and Implementing Advanced Machinery for the Agrocomplex to Boost Agricultural Output
Purpose: This study examines the influence of modern machinery adoption on agricultural productivity, labor efficiency, and farm profitability. Subjects and Methods: Using a quantitative approach, data were gathered from 100 farms utilizing technologies such as automated harvesters, GPS-guided tractors, and precision irrigation systems. Statistical analyses, including regression models, were applied to identify key predictors of improvements in farm performance. Results: Findings show a notable rise in agricultural productivity, with crop yields increasing by 26.67% after machinery adoption. Labor efficiency improved significantly, demonstrated by a 31.25% reduction in labor hours per crop. Farm profitability rose by 30%, indicating that long-term financial gains outweigh initial investment costs. The analysis also revealed that machinery use, farm size, and crop type strongly influence productivity outcomes. These results align with existing mechanization literature but extend knowledge by assessing the combined impact of multiple agricultural technologies. The study provides important insights for policymakers seeking to promote sustainable and efficient farming. Conclusions: Advanced machinery significantly enhances productivity, reduces labor demands, and improves profitability in agriculture. The evidence underscores the importance of continued modernization and highlights the need for targeted policy support to expand machinery adoption, particularly for smallholder farmers, to strengthen agricultural performance and sustainability.
AI-Enabled Predictive Control for Tropical Greenhouse Farming: Environmental Stability, Resource Efficiency, and Yield Improvements
Purpose: This study aims to evaluate the effectiveness of an AI-enabled predictive control system in enhancing environmental stability, resource efficiency, and crop productivity within tropical greenhouse farming. Subjects and Methods: The research was conducted using a 4 × 6-meter greenhouse prototype integrating IoT sensors and machine learning algorithms, including Random Forest and Artificial Neural Networks. Lettuce (Lactuca sativa) served as the model crop. The AI-driven greenhouse was compared with a conventional manually operated system across a full cultivation cycle. Data collected included microclimate parameters, water and energy consumption, plant growth indicators, and final yield. Results: The AI-enabled greenhouse maintained more consistent environmental conditions, keeping temperature, humidity, light intensity, and soil moisture within optimal ranges. Water use was reduced by approximately 38%, and energy consumption decreased by 13% compared to the conventional system. Plants grown under predictive control exhibited stronger vegetative growth, with notable increases in height, leaf number, and canopy size. Yield improved by nearly 30%, accompanied by higher marketable quality. Predictive models demonstrated strong accuracy, supporting reliable real-time decision-making. Conclusions: The results confirm that AI-based predictive control substantially improves greenhouse performance in tropical environments, offering a sustainable and efficient solution for modern horticultural production.
The Effect of Fermentation Technology on the Quality of Fish Feed from Agricultural Waste
Purpose: This study aimed to assess the impact of microbial fermentation on the nutritive value of rice bran and cassava peel and to test its effects on growth, feed use, and gut traits of Nile tilapia. Subjects and Methods: Two agro-waste types, rice bran and cassava peel, were fermented with Saccharomyces cerevisiae and Aspergillus niger. Proximate traits, fiber, and anti-nutrient levels were tested pre- and post-ferment. A feeding trial used 240 Nile tilapia (mean weight 12.5 g) in triplicate groups for 8 weeks. Diets included: control, unfermented rice bran, fermented rice bran, and fermented cassava peel. Growth, feed intake, feed gain ratio, and protein digest were recorded. Gut histology was done to test villus traits. Results: ermentation raised crude protein by 22–35% and cut fiber by up to 40%. Anti-nutrient levels fell to safe limits. Fish fed fermented rice bran grew faster (SGR 2.10%/day), had better feed ratio (1.25), and higher protein digest (89%) than control (SGR 1.45%/day, FCR 1.65, digest 74%). Fermented cassava peel gave moderate gains. Gut villus height was higher in fish fed fermented diets (270–285 µm) than in control (210 µm) or unfermented group (215 µm). Conclusions: Fermentation improved agro-waste quality, enhanced growth, raised feed use, and supported gut health in tilapia. Fermented rice bran was most effective, showing promise as a low-cost, safe, and eco-friendly feed input. This method may reduce reliance on fishmeal and support sustainable aquaculture.
Application of Machine Learning Methods to Predict Corn Harvest Yields Based on Climate Data
Purpose: The study aims to evaluate the effectiveness of machine learning (ML) methods in predicting corn yields under climate variability, addressing the limitations of traditional statistical models in capturing nonlinear and dynamic crop environment interactions. Subjects and Methods: Machine learning algorithms including Random Forest (RF), Gradient Boosting Machines (GBM), Gaussian Process Regression (GPR), and Support Vector Regression (SVR) were applied to datasets comprising climatic, soil, and vegetation index (VI) variables. Model performance was assessed using standard evaluation metrics such as the coefficient of determination (R²), root mean square error (RMSE), and normalized RMSE (nRMSE). Comparative analyses were conducted across different crop growth stages (V1–R6). Results: Ensemble and hybrid models outperformed single algorithms, with GBM achieving the highest overall accuracy (R² ≈ 0.85; RMSE ≈ 0.45 t/ha). RF consistently served as a robust baseline across datasets. Multimodal integration of VIs, soil, and climatic variables significantly improved accuracy, particularly during early growth stages where VI-only models underperformed. At maturity, GPR and RF achieved strong performance (RMSE ≈ 1.80 Mg/ha; nRMSE ≈ 13.5%). SVR demonstrated resilience under conditions of reduced data availability, making it effective for in-season forecasts. Conclusions: Machine learning provides a powerful and adaptive framework for corn yield prediction. By integrating diverse datasets and leveraging ensemble and hybrid models, forecasting accuracy can be improved for both early-season decision-making and end-of-season yield estimation. These results highlight the potential of ML to enhance agricultural resilience and inform climate adaptation strategies.
Development of an Artificial Intelligence-Based Smart Greenhouse System to Optimize Vegetable Production
Purpose: This study aims to assess the effesctiveness of an Artificial Intelligence (AI)-based smart greenhouse system in improving vegetable growth and yield, particularly lettuce, compared to conventional greenhouse methods. The primary focus of the study is to evaluate the system's effect on microenvironmental stability, vegetative growth, and crop yield. Subjects and Methods: The research subjects were lettuce plants cultivated for eight weeks in two treatments: an AI-based smart greenhouse and a conventional greenhouse. The smart greenhouse system uses temperature, humidity, and light sensors, as well as AI algorithms to automatically regulate environmental and nutritional conditions. Observed parameters included average temperature and humidity, plant height, leaf number, and wet and dry harvest weights. Data were analyzed descriptively and comparatively by calculating the average, standard deviation, and percentage increase between treatments. Results: The results showed that the smart greenhouse was able to maintain a more stable temperature (25.8–26.2 °C) compared to the conventional greenhouse (28.3–29.4 °C) and higher humidity (68–69% vs. 58–60%). Lettuce growth was also more optimal, indicated by an average height of 36.2 cm and a leaf count of 29.2 in the smart greenhouse, higher than the conventional greenhouse (28.7 cm and 23.8 leaves). Harvest weight in the smart greenhouse reached 325.7 g (wet) and 47.8 g (dry), higher than the conventional greenhouse (245.3 g and 36.4 g). Conclusions: AI-based smart greenhouse systems have proven more effective in optimizing vegetable growth and yields. This technology can be a strategic solution for increasing agricultural productivity in a sustainable, efficient, and environmentally friendly manner.