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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 5 Documents
 1 
Search results for , issue "Vol 1 No 2 (2024): May, 2024" : 5 Documents clear
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.
Decentralized Blockchain-Based Traceability Systems in Agricultural Supply Chains: A Global Perspective on Data Security, Transparency, and Equity Aristarkus
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.v1i2.88

Abstract

Decentralized blockchain-based traceability systems offer a solution to challenges in global agricultural supply chains, including lack of transparency, data security, and equity. This study investigates how blockchain, integrated with IoT, enhances data integrity, automates validation, and ensures fair benefit distribution from farm to consumer. Using a mixed-methods approach, including prototype development on Ethereum and Hyperledger, the research evaluates system efficiency, security, and socio-economic impacts. Results show significant improvements in data transparency, trust, and reduced tampering risks, empowering smallholder farmers. While private blockchains excel in efficiency, balancing transparency with data privacy and addressing infrastructure remain challenges. The study concludes that these systems hold substantial potential for creating more secure, transparent, and just agricultural supply chains globally.
Satellite-Guided Decision Support Systems for Sustainable Land Management: A Cross-Regional Approach to Crop Monitoring and Resource Optimization Sunarko
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.v1i2.89

Abstract

Satellite-guided decision support systems (DSS) have emerged as critical tools for sustainable land management amid global challenges such as land degradation, climate change, and the increasing demand for agricultural productivity. This study presents a cross-regional approach that integrates satellite remote sensing data, agricultural growth models, and multi-criteria analysis to optimize crop monitoring and resource use across diverse agro-ecological zones. Utilizing advanced geospatial technologies and real-time climate datasets, the developed DSS provides precise, adaptive recommendations to farmers and policymakers, enhancing decision-making processes for sustainable agriculture. Field validations across multiple regions demonstrated the system’s capability to accurately monitor crop conditions and optimize resource allocation, resulting in improved productivity while maintaining environmental sustainability. The findings highlight the importance of incorporating dynamic satellite data and region-specific models to address variability in land use and socio-economic contexts. Despite challenges related to data uncertainty and user engagement, this research advances the integration of satellite technologies in land management frameworks and underscores the potential of cross-regional DSS in supporting adaptive, efficient, and sustainable agricultural practices.
A Global Framework for Carbon-Smart Agricultural Systems: Evaluating the Role of Regenerative Practices in Carbon Sequestration and Emissions Mitigation Dian Diani T
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.v1i2.90

Abstract

Global agriculture must meet growing food demands while reducing greenhouse gas (GHG) emissions. This study proposes a global framework to assess regenerative agricultural practices within climate-smart agriculture (CSA) systems, focusing on their potential for soil carbon sequestration and GHG mitigation. Secondary data were collected from scientific literature and public databases on key regenerative practices, including agroforestry, cover crops, legume rotations, livestock integration, non-chemical inputs, and no-till farming. A meta-analysis was conducted to estimate average impacts on carbon sequestration and emissions reduction, leading to the development of a flexible, globally applicable evaluation framework. Findings show that all regenerative practices enhance soil carbon levels compared to conventional methods, with agroforestry and combined legume/non-legume cover crops showing the highest potential, particularly in perennial systems. Integrated approaches yielded stronger results than individual practices due to synergistic effects. However, outcomes varied significantly depending on local soil and climate conditions. The study reinforces the role of regenerative agriculture in addressing climate change and ensuring food security while providing a practical tool for policymakers and practitioners. It recommends further long-term research and the use of digital technologies to refine and adapt the framework across diverse agricultural contexts.

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