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Techno Agriculturae Studium of Research
Published by Yayasan Adra Karima Hubbi
ISSN : 30479835     EISSN : 30482321     DOI : 10.70177/agriculturae
Core Subject : Agriculture,
Agriculture, Biological Sciences & Forestry
Techno Agriculturae Studium of Research is an international forum for the publication of peer-reviewed integrative review articles, special thematic issues, reflections or comments on previous research or new research directions, interviews, replications, and intervention articles - all pertaining to the Research in agriculture, includes a wide range of studies and analyzes related to production, resource management, agricultural technology, environmental sustainability, agricultural policy, and more. All publications provide breadth of coverage appropriate to a wide readership in agriculture research depth to inform specialists in that area. We feel that the rapidly growing Techno Agriculturae Studium of Research community is looking for a journal with this profile that we can achieve together. Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers.
Arjuna Subject : Ilmu Pertanian dan Biologi (Semua) - Ilmu Pertanian dan Biologi (Lain-Lain)
Articles 50 Documents
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ROBOTIC WEEDING: AN AUTONOMOUS MECHANICAL SOLUTION FOR REDUCING HERBICIDE DEPENDENCE IN ORGANIC VEGETABLE FARMING SYSTEMS Mansour, Nadine; Halil , Ziad; Chahine, Alaa
Techno Agriculturae Studium of Research Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i5.2950

Abstract

This study designs, implements, and evaluates an autonomous robotic weeding system as a mechanical solution to reduce herbicide dependence in organic vegetable farming. Organic farming faces persistent weed management challenges due to restrictions on synthetic herbicides and rising labor costs. Weeds compete with crops for essential resources, resulting in yield losses and reduced efficiency. While mechanical weeding has been a viable alternative, traditional methods often lack precision and can harm crops. Recent advances in robotics and automation offer a solution by enabling intelligent, autonomous weed control that aligns with sustainable agricultural practices. The research uses a design-and-experimental methodology, developing an autonomous robotic platform equipped with vision-based sensors, navigation algorithms, and mechanical weeding tools. The system was tested in organic vegetable plots, and field trials measured weed removal efficiency, crop safety, operational accuracy, and energy consumption, with comparisons to traditional manual weeding methods. The results showed that the robotic system effectively reduced weed density with high precision while minimizing crop disturbance. The system demonstrated consistent performance across test plots and significantly reduced reliance on manual labor and chemical inputs. Weed control efficiency was comparable to traditional methods, with improved consistency and reduced operational fatigue. The study concludes that robotic weeding is a viable, sustainable solution for weed management in organic vegetable farming and offers promising implications for smart agriculture and technology-integrated education.  
AN IOT-BASED WEARABLE SENSOR SYSTEM FOR MONITORING THE HEALTH, RUMINATION, AND ESTRUS CYCLE OF DAIRY COWS IN INDONESIA Al-Sayid, Nisreen; Ibrahim, Nour; Al-Attar, Hassan
Techno Agriculturae Studium of Research Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i6.2951

Abstract

The rapid development of Internet of Things (IoT) technology offers significant opportunities for improving livestock management, especially in dairy farming systems in developing countries like Indonesia. Traditional methods of monitoring dairy cow health, behavior, and estrus cycles rely on manual observation, which can be time-consuming, subjective, and inaccurate. These limitations lead to delayed disease detection, suboptimal reproductive performance, and reduced milk productivity. This study aims to design and evaluate an IoT-based wearable sensor system for continuous monitoring of dairy cow health, rumination patterns, and estrus cycles in Indonesian dairy farms. A research and development approach combined with field testing was employed. The system integrates wearable sensors attached to cows, collecting data on movement, body temperature, and rumination activity. Data is transmitted in real-time via IoT networks to a cloud platform for processing and visualization. System performance was assessed through accuracy testing, reliability analysis, and farmer feedback. The results show that the system effectively detects changes in rumination behavior, identifies early health issues, and predicts estrus cycles with high consistency compared to traditional methods. Farmers reported improved decision-making efficiency and reduced labor intensity. The IoT-based wearable sensor system demonstrates potential as an innovative solution for enhancing dairy cow health monitoring and reproductive management in Indonesia, supporting sustainable dairy farming practices.
POST-HARVEST TECHNOLOGY: THE USE OF CONTROLLED ATMOSPHERE STORAGE TO EXTEND THE SHELF LIFE AND MAINTAIN THE QUALITY OF MANGOSTEEN Astuti, Suhartin Dewi; Dara, Chenda
Techno Agriculturae Studium of Research Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i4.2958

Abstract

Mangosteen (Garcinia mangostana), known as the “queen of fruits,” is a highly perishable tropical fruit with a short shelf life. Post-harvest losses due to poor storage and handling are significant challenges, especially for export markets. Controlled atmosphere (CA) storage, which modifies oxygen, carbon dioxide, and humidity levels, has been identified as a promising technology for extending the shelf life of fruits while maintaining their quality. However, limited research has been conducted on the application of CA storage for mangosteen in tropical climates like Indonesia. This study aims to explore the use of controlled atmosphere storage to extend the shelf life and preserve the quality of mangosteen fruits. The research focuses on determining the optimal storage conditions for mangosteen using CA technology and assessing its impact on fruit quality parameters such as color, texture, firmness, and overall freshness. A laboratory-based experimental design was employed, where mangosteen fruits were stored under various controlled atmosphere conditions (oxygen, carbon dioxide, and temperature). The fruits were periodically evaluated for changes in quality parameters using standard techniques, including firmness testing and sensory evaluation. The results indicate that CA storage effectively extended the shelf life of mangosteen by up to 15 days compared to the conventional storage method. The fruits stored under optimal CA conditions showed minimal loss in firmness, color retention, and overall freshness. In conclusion, controlled atmosphere storage is a promising solution for extending the shelf life and maintaining the quality of mangosteen, making it a viable option for improving post-harvest management and enhancing marketability in export markets.  
A COMPARATIVE STUDY OF GPS-GUIDED TRACTOR AUTOSTEER VS. TRADITIONAL SEEDING TECHNOLOGIES ON MAIZE YIELD AND FUEL EFFICIENCY Keolavong, Manivone; Vong, Soneva; Phommavong, Soukchinda
Techno Agriculturae Studium of Research Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i5.2959

Abstract

This study compares the impact of GPS-guided tractor autosteer technology and traditional manual steering on maize yield and fuel efficiency. Precision agriculture technologies, such as GPS-guided autosteer, offer more accurate and efficient field operations, reducing overlaps and gaps in seeding, which are common in manual methods. However, there is limited empirical evidence on the agronomic and operational performance of these technologies in maize cultivation. The research was conducted on maize farms over one growing season, with two treatments: GPS-guided autosteer and traditional manual steering. Data on maize yield, fuel consumption, seeding accuracy, and operational time were collected and analyzed. The results showed that GPS-guided autosteer significantly improved seeding accuracy, reducing overlaps and leading to a 12% increase in maize yield compared to traditional methods. Additionally, fuel consumption was reduced by 18% due to more efficient coverage and reduced operational time. The autosteer system also demonstrated improved consistency in row spacing and plant population. This study concludes that GPS-guided autosteer technology offers both agronomic and economic advantages, increasing maize productivity, enhancing fuel efficiency, and promoting more sustainable, cost-effective farming practices.
DEVELOPMENT OF AN IOT-BASED AUTOMATED DRIP IRRIGATION AND FERTIGATION SYSTEM FOR CHILI FARMING IN ARID REGIONS OF EAST JAVA Fujita, Miku; Nakamura, Yui; Tanaka, Kaito
Techno Agriculturae Studium of Research Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i5.2960

Abstract

Chili farming in arid regions of East Java faces persistent challenges related to water scarcity, inefficient irrigation practices, and inconsistent nutrient managment, which negatively affect crop productivity and farmers’ livelihoods. Traditional irrigation methods often result in excessive water use and uneven fertilizer distribution, limiting plant growth and increasing production costs. Recent advances in Internet of Things (IoT) technology offer promising solutions for precision agriculture by enabling automated, data-driven irrigation and fertigation systems tailored to specific crop and environmental conditions. This study aims to develop and evaluate an IoT-based automated drip irrigation and fertigation system designed for chili farming in arid areas of East Java. The system is intended to optimize water and nutrient usage while improving crop growth and resource efficiency. The research adopts a research and development (R&D) approach combined with experimental field testing. The system integrates soil moisture sensors, temperature and humidity sensors, nutrient solution controllers, and an IoT microcontroller connected to a cloud-based monitoring platform. The system was tested in selected chili farms over one growing season, with performance evaluated based on water consumption, fertilizer efficiency, plant growth indicators, and yield outcomes. The results indicate that the IoT-based system reduced water usage by approximately 30% and fertilizer consumption by 25% compared to conventional irrigation methods. Chili plants managed under the automated system showed more uniform growth, improved plant health, and a yield increase of 20%. Farmers also reported improved ease of irrigation management and real-time monitoring capabilities. The study concludes that IoT-based automated drip irrigation and fertigation systems are effective in enhancing water efficiency, nutrient management, and chili crop productivity in arid regions. The system demonstrates strong potential for supporting sustainable agriculture and climate-resilient farming practices in East Java.
THE USE OF MULTISPECTRAL DRONE IMAGERY AND ARTIFICIAL INTELLIGENCE FOR THE EARLY DETECTION OF LEAF BLIGHT DISEASE IN INDONESIAN RICE PADDIES Wei, Sun; Jun, Wang; Yang, Liu
Techno Agriculturae Studium of Research Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i5.2961

Abstract

Leaf blight disease remains one of the major threats to rice production in Indonesia, causing significant yield losses and threatening national food security. Conventional detection methods rely heavily on manual field inspection, which is time-consuming, labor-intensive, and often ineffective for early-stage identification. Recent advances in multispectral drone imagery and artificial intelligence (AI) offer new opportunities for precision agriculture by enabling rapid, accurate, and large-scale crop health monitoring. However, the practical application of these technologies in Indonesian rice paddies is still limited and requires empirical validation. This study aims to examine the effectiveness of multispectral drone imagery integrated with AI-based classification models for the early detection of leaf blight disease in Indonesian rice fields. The research focuses on improving detection accuracy and supporting timely disease management decisions for farmers and agricultural stakeholders. The study employs an experimental research design using multispectral drone data collected from rice paddies in West Java during the growing season. Vegetation indices such as NDVI and GNDVI were extracted and analyzed using machine learning algorithms, including Random Forest and Convolutional Neural Networks (CNN). Ground truth data were obtained through field observations and laboratory confirmation to validate the model outputs. The results demonstrate that the AI-based model achieved high classification accuracy, exceeding 90% in detecting early-stage leaf blight symptoms. The integration of multispectral data significantly improved detection performance compared to visual RGB imagery alone. The study concludes that multispectral drone imagery combined with AI provides a reliable and efficient approach for early detection of leaf blight disease in rice paddies. This approach has strong potential to support precision agriculture, reduce crop losses, and enhance sustainable rice production in Indonesia.
AN AUTOMATED FEED MANAGEMENT SYSTEM FOR HIGH-DENSITY CATFISH AQUACULTURE USING ACOUSTIC SENSORS AND MACHINE LEARNING Kamal, Mai; Abdallah, Mona; Youssef, Tamer
Techno Agriculturae Studium of Research Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i6.2962

Abstract

The rapid expansion of high-density catfish aquaculture has increased the demand for efficient and precise feed management systems to optimize growth performance, reduce feed waste, and maintain water quality. Conventional feeding practices largely depend on fixed schedules and visual estimation, which often result in overfeeding or underfeeding, leading to increased production costs and environmental degradation. Recent advances in sensing technologies and artificial intelligence offer new opportunities to transform aquaculture management through data-driven and automated approaches. The purpose of this study is to develop and evaluate an automated feed management system for high-density catfish aquaculture by integrating acoustic sensors and machine learning algorithms. The system aims to accurately detect feeding activity and dynamically regulate feed delivery based on real-time fish behavior. This study employed a research and development design combined with experimental field testing. Acoustic sensors were deployed in catfish ponds to capture underwater sound patterns associated with feeding behavior. The collected acoustic data were processed using machine learning models to classify feeding intensity and determine optimal feeding duration. System performance was evaluated through accuracy testing, feed efficiency analysis, and comparative assessment against conventional feeding methods. The results show that the proposed system successfully identified feeding activity with high classification accuracy and significantly reduced feed waste compared to manual feeding practices. Feed conversion ratios improved, and water quality indicators remained more stable due to reduced excess feed accumulation. In conclusion, the automated feed management system demonstrates strong potential as an intelligent aquaculture solution for high-density catfish farming. By integrating acoustic sensing and machine learning, the system enhances feeding precision, supports sustainable aquaculture practices, and contributes to increased productivity and environmental efficiency.
THE APPLICATION OF DRONE TECHNOLOGY AND IMAGE ANALYSIS FOR MONITORING GRAZING PATTERNS AND RANGELAND CAPACITY IN CATTLE FARMING Figueroa, Ricardo; Chavarria, Carlos; Ramirez, Luis
Techno Agriculturae Studium of Research Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i6.2963

Abstract

The increasing demand for sustainable cattle farming and the pressure on rangeland resources have highlighted the need for efficient monitoring of grazing patterns and land carrying capacity. Traditional methods of monitoring rely on manual field surveys, which are labor-intensive and have limited coverage. Recent advancements in drone technology and image analysis present new opportunities for data-driven decision-making in livestock and rangeland management. This study explores the use of drone technology combined with image analysis techniques to monitor grazing patterns and assess rangeland capacity. A research and development design was employed, with drones capturing high-resolution aerial imagery of grazing areas at regular intervals. Image analysis techniques, including vegetation index extraction and spatial pattern analysis, were used to assess grazing intensity, vegetation cover, and biomass distribution. Data from the drone-based imagery were validated through ground observations and rangeland productivity records. The results show that drone-derived imagery accurately captured spatial variations in grazing behavior and vegetation condition, allowing for precise mapping of grazing zones and reliable estimates of rangeland carrying capacity. Compared to traditional methods, the drone-based approach was more efficient, offered greater spatial accuracy, and reduced the need for field surveys. In conclusion, integrating drone technology and image analysis offers a scalable solution for sustainable rangeland and livestock management.
CRISPR/CAS9-MEDIATED GENETIC ENGINEERING FOR DEVELOPING SALINITY-TOLERANT RICE VARIETIES FOR INDONESIAN COASTAL AGRICULTURE Scott, James; Williams, Sarah; Martin, David
Techno Agriculturae Studium of Research Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i6.2964

Abstract

Salinity intrusion in coastal agricultural areas has become a major constraint to rice production in Indonesia, driven by climate change, sea-level rise, and unsustainable land management practices. High soil salinity adversely affects rice growth, yield stability, and food security, particularly in coastal regions that depend heavily on rice cultivation. Conventional breeding approaches for developing salinity-tolerant rice varieties are often time-consuming and limited by genetic complexity. Advances in genome editing technologies, particularly CRISPR/Cas9, offer a precise and efficient alternative for accelerating crop improvement. The objective of this study is to develop salinity-tolerant rice varieties suitable for Indonesian coastal agriculture through CRISPR/Cas9-mediated genetic engineering targeting key genes associated with salt stress tolerance. This research employed an experimental laboratory-based design combined with controlled greenhouse evaluation. Specific salinity-responsive genes were identified and edited using the CRISPR/Cas9 system. Transgenic rice lines were generated and screened for successful gene edits using molecular analysis techniques. Edited lines were subsequently evaluated under saline and non-saline conditions to assess physiological responses, growth performance, and yield-related traits. The results demonstrate that CRISPR/Cas9-edited rice lines exhibited enhanced tolerance to saline stress, indicated by improved germination rates, higher chlorophyll content, better ion homeostasis, and increased biomass compared to non-edited controls. Several edited lines maintained stable growth and yield under moderate to high salinity levels, confirming the effectiveness of targeted gene modification. In conclusion, CRISPR/Cas9-mediated genetic engineering shows strong potential for developing salinity-tolerant rice varieties tailored to Indonesian coastal environments. This approach provides a rapid and precise strategy to enhance rice resilience, support sustainable coastal agriculture, and strengthen national food security under changing climatic conditions.
THE USE OF RECOMBINANT DNA TECHNOLOGY TO ENHANCE BETA-CAROTENE CONTENT IN CASSAVA (GOLDEN CASSAVA) Abakar, Sonia; Ali, Fatima; Saleh, Mahamat
Techno Agriculturae Studium of Research Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i6.2965

Abstract

Cassava (Manihot esculenta) is a staple crop widely grown in tropical regions, providing a major source of carbohydrates. However, its nutritional content is limited, particularly in essential micronutrients such as provitamin A. Beta-carotene, a precursor of vitamin A, plays a critical role in human health, particularly in preventing vitamin A deficiency, which is prevalent in many developing countries. Enhancing beta-carotene content in cassava could significantly improve its nutritional value and address public health concerns related to micronutrient malnutrition. The objective of this study is to use recombinant DNA technology to genetically engineer cassava varieties with enhanced beta-carotene content, creating what is commonly referred to as “Golden Cassava.”This research employed genetic transformation techniques, specifically Agrobacterium-mediated transformation, to introduce genes responsible for the biosynthesis of beta-carotene into cassava. Candidate genes, including those from the daffodil and maize, were selected to enhance the carotenoid biosynthesis pathway. Transgenic cassava plants were developed, and molecular analysis, including PCR and Southern blotting, was used to confirm the presence and integration of the introduced genes. Beta-carotene content in the transgenic plants was measured using high-performance liquid chromatography (HPLC). The results showed that the genetically modified cassava plants exhibited a significant increase in beta-carotene content compared to the wild-type varieties. The transgenic lines demonstrated enhanced nutritional quality without affecting other agronomic traits. In conclusion, recombinant DNA technology has proven to be an effective tool for biofortifying cassava with beta-carotene. This approach offers a promising strategy for addressing vitamin A deficiency and improving the nutritional value of cassava in regions where it is a major food source.

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