cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Adam Mudinillah
Contact Email
adammudinillah@staialhikmahpariangan.ac.id
Phone
+6285379388533
Journal Mail Official
adammudinillah@staialhikmahpariangan.ac.id
Editorial Address
Jorong Kubang Kaciak Dusun Kubang Kaciak, Kelurahan Balai Tangah, Kecamatan Lintau Buo Utara, Kabupaten Tanah Datar, Provinsi Sumatera Barat, Kodepos 27293.
Location
Kab. tanah datar,
Sumatera barat
INDONESIA
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 5 Documents
 1 
Search results for , issue "Vol. 2 No. 5 (2025)" : 5 Documents clear
DESIGN AND TESTING OF A WIRELESS SENSOR NETWORK FOR REAL-TIME MONITORING OF SOIL NPK LEVELS IN SUGARCANE PLANTATIONS Wangmo, Tandin; Dorji, Jigme; Tenzin, Choden
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.v2i5.2949

Abstract

This study develops and tests a Wireless Sensor Network (WSN) system for real-time monitoring of soil nitrogen (N), phosphorus (P), and potassium (K) levels in sugarcane plantations. Traditional soil testing methods are time-consuming and costly, and they fail to provide continuous data on nutrient fluctuations, which limits effective decision-making in fertilization management. The study aims to evaluate the reliability and applicability of the WSN system in both agricultural field operations and as an educational tool for technology-enhanced learning. The research followed a design-and-testing methodology, developing sensor nodes with NPK soil sensors, microcontrollers, and wireless communication modules integrated into a centralized monitoring platform. Field testing took place in a sugarcane plantation, with sensor data continuously transmitted to a cloud-based dashboard for analysis. Results show that the WSN system accurately monitored spatial and temporal variations in soil NPK levels, providing stable data transmission with measurement accuracy comparable to laboratory soil analysis. Real-time visualization of nutrient status facilitated quicker interpretation and more responsive fertilization strategies. The study concludes that WSN-based soil monitoring is a practical, scalable solution for improving nutrient management in sugarcane plantations and offers potential as an educational tool to integrate digital sensing technologies into agricultural and vocational education.
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.  
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.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 2 No. 6 (2025) Vol. 2 No. 5 (2025) Vol. 2 No. 4 (2025) Vol. 2 No. 3 (2025) Vol. 2 No. 2 (2025) Vol. 2 No. 1 (2025) Vol. 1 No. 4 (2024) Vol. 1 No. 3 (2024) Vol. 1 No. 2 (2024) Vol. 1 No. 1 (2024)