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INDONESIA
Agricultural Power Journal
Published by CV. HEI Publishing Indonesia
ISSN : -     EISSN : 30628563     DOI : http://dx.doi.org/10.70076/apj
Core Subject : Agriculture,
Agriculture, Biological Sciences & Forestry
Agricultural Power Journal (APJ), encourages submission of manuscripts dealing with all aspects to optimizing the quality and quantity of both plant, including agricultural economics and management, agricultural engineering and mechanization, agronomy and crop science, biotechnology, ecology and ecophysiology, food science and technology, genetic diversity and breeding, molecular biology, land resources, land use and remote sensing, microbiology, virology and bacteriology, organic agriculture, physiology and nutrition, phytoremediation, plant nutrition, plant pathology and pest management, post-harvest technology, soil sciences, soilless culture, tissue culture technology, and water management.
Arjuna Subject : Ilmu Pertanian dan Biologi (Semua) - Ilmu Pertanian dan Biologi (Lain-Lain)
Articles 40 Documents
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Analysis Of The Success Of Digital-Based Agricultural Extension Programs In Rural Areas Agmamsyahri
Agriculture Journal Vol 2 No 1 (2025): February, 2025
Publisher : CV. HEI PUBLISHING INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70076/apj.v2i1.63

Abstract

An inventive way to raise farmers' knowledge and proficiency in rural regions is through digital-based agricultural extension. The purpose of this study is to evaluate the extension program's effectiveness and how it affects farmers' satisfaction. 50 farmers participating in the initiative were given questionnaires and conducted in-depth interviews as part of the methodology. According to the findings, 75% of farmers learned new skills and 82% of farmers gained more information; on a Likert scale of 5, the average satisfaction level was 4.2. Technology adoption is nevertheless hampered by issues including low digital literacy and inadequate internet infrastructure, despite the program's effectiveness. According to the study's findings, enhancing digital literacy and building infrastructure are critical to the long-term viability of Indonesia's digitally based extension initiatives.
Optimization of Nitrogen Fertilization in Corn Plants to Increase Yields on Marginal Land Pongky Sandra
Agriculture Journal Vol 2 No 1 (2025): February, 2025
Publisher : CV. HEI PUBLISHING INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70076/apj.v2i1.65

Abstract

This study aims to optimize nitrogen fertilization in corn (Zea mays) to increase yields on marginal land. Marginal land often experiences nutrient deficiencies, especially nitrogen, which have a negative impact on plant growth and productivity. In this study, an experiment was conducted using a Randomized Block Design (RBD) involving five nitrogen fertilizer dose treatments, namely 0 kg N/ha (control), 75 kg N/ha, 150 kg N/ha, 225 kg N/ha, and 300 kg N/ha. The parameters observed included plant height, stem diameter, leaf area, fresh weight of cobs, and yield. The results showed that nitrogen fertilization significantly increased all growth parameters and yields of corn. The highest dose (300 kg N/ha) produced an average plant height of 180 cm, stem diameter of 4.5 cm, leaf area of ​​550 cm², fresh weight of cobs of 0.9 kg, and yields reaching 8 tons/ha. These findings support the hypothesis that increased nitrogen availability positively contributes to vegetative growth and yield of corn. This study provides practical recommendations for farmers to apply nitrogen fertilization optimally on marginal land to increase corn productivity. Further research directions are suggested to explore the long-term effects of nitrogen fertilization and the combination of organic and inorganic fertilizers for agricultural sustainability.
Genomic and Agronomic Innovations for Enhancing Abiotic Stress Tolerance in Global Food Crops under Climate Change Scenarios Muhammad Rafi
Agriculture Journal Vol 1 No 3 (2024): August, 2024
Publisher : CV. HEI PUBLISHING INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70076/apj.v1i3.71

Abstract

Climate change poses a significant threat to global food security by increasing the frequency and intensity of abiotic stresses such as drought, salinity, and extreme temperatures on major food crops. This study aimed to evaluate and integrate genomic and agronomic innovations to enhance abiotic stress tolerance in key global crops. A combination of laboratory and field experiments was conducted using diverse crop varieties, including wild relatives and genetically modified lines, under controlled abiotic stress conditions. Advanced phenotyping, next-generation sequencing, and CRISPR-Cas9 gene editing were employed to identify and validate candidate genes associated with stress tolerance. The results demonstrated that wild-derived and genome-edited varieties exhibited superior physiological performance and yield stability under stress compared to conventional cultivars. Key genes such as DREB2, AREB1, and AVP1 were identified as crucial regulators of stress response. Integrating adaptive agronomic practices with genomic innovations resulted in synergistic improvements, increasing yield by up to 25% under stress. These findings underscore the importance of a multidisciplinary approach for developing resilient crop varieties and sustaining food production under climate change. However, further research is needed to assess long-term ecological impacts and ensure broad adoption of these technologies.
Precision Drones in Hilly Lands: Optimizing Crop Health Monitoring and Adaptive Fertilization for Sustainable Agriculture Siska Almaniar
Agriculture Journal Vol 1 No 2 (2024): May, 2024
Publisher : CV. HEI PUBLISHING INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70076/apj.v1i2.79

Abstract

Precision agriculture in mountainous regions faces challenges due to complex topography and limited accessibility, making traditional crop monitoring and fertilization inefficient. This study proposes and evaluates a drone-based precision system to optimize adaptive fertilization and crop health monitoring in such environments. A quadcopter drone equipped with RGB and multispectral sensors captured high-resolution aerial imagery, which was processed into Digital Surface Models (DSM) and NDVI maps for accurate land mapping and plant health assessment. Spatial NDVI data enabled targeted liquid fertilizer application, improving precision and reducing waste. Results showed that NDVI-based monitoring allowed early detection of plant stress, and drone-generated maps exhibited high accuracy with minimal deviation from manual observations. Compared to traditional methods, drone-assisted adaptive fertilization improved time and labor efficiency, and reduced fertilizer usage by up to 25% without compromising yields. Statistical analysis confirmed the system's effectiveness in optimizing inputs and enhancing operational efficiency. Despite the advantages, adoption remains limited among smallholder farmers due to cost and accessibility concerns. This research contributes to sustainable agriculture by demonstrating how precision drones can enhance resource management and productivity in hilly terrains, supporting the broader adoption of digital agricultural practices.
Integrative Application of Deep Learning and Multispectral Remote Sensing for Predictive Crop Management in Precision Agriculture Zuhra Hariati
Agriculture Journal Vol 1 No 2 (2024): May, 2024
Publisher : CV. HEI PUBLISHING INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70076/apj.v1i2.80

Abstract

This study introduces an innovative approach to predictive crop management in precision agriculture by integrating deep learning with multispectral remote sensing technologies. The research aims to develop a framework that combines multispectral data from field sensors, UAVs, and satellites with a deep learning model based on a multimodal architecture incorporating adaptive transfer learning and attention mechanisms. Data were collected over two growing seasons and underwent preprocessing, vegetation feature extraction, and model training and validation. The proposed deep learning model significantly outperformed traditional machine learning algorithms such as Random Forest and Support Vector Machines, achieving up to 97.8% accuracy in crop classification. Predicted crop conditions and yield estimates showed a strong correlation with actual field data (r = 0.89; RMSE = 0.12). Field implementation of the predictive system indicated potential increases in crop yield by 18% and reductions in agricultural input usage by 28%. These results highlight the potential of deep learning and multispectral data integration to enhance decision-making, resource efficiency, and sustainability in precision farming. Furthermore, the approach demonstrates strong scalability for different crop types and geographical regions, providing a solid foundation for the digital transformation of agriculture toward a more adaptive and sustainable food production system.
Food Security Transformation: Impact Analysis of IoT-Based Irrigation Water Consumption Patterns in Climate Change Adaptation in Modern Agricultural Systems Fikri Putra
Agriculture Journal Vol 1 No 3 (2024): August, 2024
Publisher : CV. HEI PUBLISHING INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70076/apj.v1i3.81

Abstract

The transformation of modern agricultural systems through the application of Internet of Things (IoT) technology is a strategic solution in facing the challenges of food security due to climate change. This study aims to analyze the impact of IoT-based irrigation water consumption patterns on water use efficiency, crop productivity, and soil quality in tropical agricultural land. The study was conducted by designing an automatic irrigation system based on ESP32 and soil moisture sensors, and testing its effectiveness during one planting season on 1.5 hectares of land. The results showed that the IoT irrigation system was able to reduce water consumption by up to 35% compared to conventional methods, maintain soil moisture within the optimal range, and increase crop yields by 12%. Furthermore, despite ongoing difficulties with technical assistance and initial investment expenditures, the quality of soil nutrients is better maintained, and farmer satisfaction with this system is high.  IoT-based irrigation can be a flexible and sustainable way to improve food security, according to this study, but its deployment calls on sufficient legislative backing, education, and digital infrastructure. These results offer recommendations for more study in diverse agro-climatic settings as well as a scientific foundation for the advancement of precision agriculture.
Smart Urban Farming 5.0: Integration of Vertical Farming Automation and Solar Panels for Maximum Energy Efficiency and Productivity in Major Cities in Indonesia Farhan Zikri
Agriculture Journal Vol 1 No 3 (2024): August, 2024
Publisher : CV. HEI PUBLISHING INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70076/apj.v1i3.82

Abstract

This study explores the development of Smart Urban Farming 5.0 as an innovative solution to address energy efficiency and food security challenges in Indonesia’s major urban centers. The research is driven by rapid urbanization, which limits agricultural land and increases the need for space- and energy-efficient farming systems. The main objective is to design and evaluate the integration of solar panels with automated vertical farming to enhance agricultural productivity while reducing reliance on conventional electricity sources. The methodology includes building an IoT- and AI-based automated farming prototype powered by solar energy, and testing its performance over three months by monitoring energy consumption, plant growth, and harvest quality. The results reveal a significant reduction in operational costs, a 62% decrease in traditional electricity usage, and a 28% increase in crop yields compared to conventional systems. While the system requires technical training, user feedback indicates high acceptance. Overall, Smart Urban Farming 5.0 proves to be a viable and effective approach for sustainable urban agriculture, contributing to clean energy and food-related Sustainable Development Goals (SDGs). This research opens pathways for future advancements in intelligent and environmentally friendly urban farming technologies.
Pest Prediction Revolution: Integrating Artificial Intelligence and Micro Weather Sensors for RealTime Data Driven Tropical Agriculture Hana Risa
Agriculture Journal Vol 1 No 3 (2024): August, 2024
Publisher : CV. HEI PUBLISHING INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70076/apj.v1i3.83

Abstract

Tropical agriculture in emerging nations has a significant challenge from more unpredictable insect infestations brought on by climate change and environmental dynamics. In order to facilitate real-time agricultural decision-making, this project intends to create a pest attack prediction system based on the combination of artificial intelligence (AI) with microweather sensors. Installing microweather sensors in tropical agricultural areas, gathering environmental data and insect photos, and creating a hybrid CNN-LSTM model for analyzing and forecasting pest attacks are some of the techniques employed. Over the course of two growing seasons, the system was evaluated for forecast accuracy, agronomic effect, and economic analysis in a variety of tropical agroecosystems. In comparison to traditional approaches, the findings demonstrated that the AI-sensor system could lower the intensity of assaults by 58%, enhance the accuracy of pest attack prediction (F1-score 0.91; AUC-ROC 0.96), and reduce the consumption of pesticides by 67%. Furthermore, there was a notable rise in both economic efficiency and crop output. This study came to the conclusion that while large-scale deployment still necessitates infrastructure adaption and training, the combination of AI with real-time micro weather sensors has the potential to completely transform pest management systems in tropical agriculture.
Revitalization of Pesticide-treated Rice Fields through Local Bacterial Biofilms: An Environmental and Sustainable Agricultural Solution Rima Permata
Agriculture Journal Vol 1 No 3 (2024): August, 2024
Publisher : CV. HEI PUBLISHING INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70076/apj.v1i3.84

Abstract

This study explores the development of Smart Urban Farming 5.0 as an innovative response to energy efficiency and food security issues in Indonesia’s rapidly urbanizing cities. With urban expansion reducing agricultural land, there is a growing need for farming systems that are both space- and energy-efficient. The research focuses on designing and evaluating a vertical farming system that integrates solar panels with automation technologies to boost productivity and reduce dependence on conventional electricity. A prototype system was built using IoT and AI, powered entirely by solar energy, and tested over a three-month period. Key performance indicators included energy consumption, plant growth, and harvest quality. The results revealed a 62% reduction in traditional electricity use and a 28% increase in crop yield, along with lower operational costs compared to conventional farming methods. Although the system requires technical training for operation, user feedback was generally positive. The findings demonstrate that Smart Urban Farming 5.0 can significantly improve the sustainability and efficiency of urban agriculture. This research supports progress toward sustainable development goals, particularly in clean energy and food security, while offering potential for future advancements in intelligent and environmentally friendly urban farming solutions tailored to dense metropolitan areas.
Response of Sweet Corn (Zea mays saccharata Sturt) to Different Decomposers and Rice Straw Compost Dosages Jamilah jamilah
Agriculture Journal Vol 2 No 2 (2025): May, 2025
Publisher : CV. HEI PUBLISHING INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70076/apj.v2i2.87

Abstract

This study was conducted in Kalumbuk Subdistrict, Kuranji District, Padang City, to evaluate the effects of decomposer type and rice straw compost dosage on the growth and yield of sweet corn. The objective of the study was to determine the effect of decomposer origin and rice straw compost dosage on the growth and yield of sweet corn. The experiment used a factorial randomized complete block design (RCBD) with two factors. The first factor was the type of decomposer consisting of three sources: natural (no added decomposer), EM4, and Crocober Plus LOF (CP LOF). The second factor was the rice straw compost dosage consisting of three levels: 10, 15, and 20 tons per hectare. The results showed no significant interaction between decomposer type and compost dosage on plant height and Leaf Area Index (LAI). However, both decomposer type and compost dosage had significant main effects. Crocober Plus (CP LOF) and EM4 liquid significantly improved plant growth and yield compared to natural decomposer, with CP LOF producing the best results. The optimal compost dosage was 15 t ha⁻¹, which resulted in a maximum yield of 27.46 t ha⁻¹ of fresh ear weight.

Page 2 of 4 | Total Record : 40

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