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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika JOURNAL OF SCIENCE AND SOCIAL RESEARCH INFOKUM Jurnal Abdidas Jurnal Teknik Informatika (JUTIF) Mejuajua Jurnal Minfo Polgan (JMP) Journal of Science Technology (JoSTec) Jurnal Pengabdian Masyarakat Nusantara Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Jurnal Pengabdian kepada Masyarakat Journal of Technology and Computer (JOTECHCOM) JIPITI: Jurnal Pengabdian kepada Masyarakat International Journal of Educational Insights and Innovations (IJEDINS) Outline Journal of Community Development International Journal for Advanced Research
ruziq, fahmi
Battuta University
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Artificial Intelligence-Based Hydroponic Plant Disease Detection System (Lactuca sativa) Wayahdi, M. Rhifky; Ruziq, Fahmi; Nurhajijah, Nurhajijah
Journal of Technology and Computer Vol. 2 No. 4 (2025): November 2025 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

Hydroponic cultivation of lettuce (Lactuca sativa) offers high water efficiency, yet productivity is frequently compromised by rapid disease spread and nutrient imbalances. Traditional manual monitoring is labor-intensive, time-consuming, and prone to subjective diagnostic errors, often leading to delayed interventions. This study aims to develop an automated, real-time disease detection system by integrating Deep Learning algorithms with an Internet of Things (IoT) architecture. The proposed method utilizes an optimized One-Stage Object Detector based on the YOLO framework, specifically designed for efficient deployment on edge computing devices. The model was trained and validated on a diverse dataset encompassing healthy plants, tip-burn, leaf spot, and nutrient deficiencies, employing rigorous data augmentation to ensure robustness against indoor lighting variability. Experimental results demonstrate that the system achieves a Mean Average Precision (mAP@0.5) of 94.8%, significantly outperforming conventional Support Vector Machine (SVM) approaches and standard detectors. The model maintains high detection accuracy even under complex background conditions. In conclusion, this research provides a viable, low-latency solution for precision agriculture, enabling growers to automate plant health monitoring and effectively minimize crop losses.
Real-Time Classification of Hydroponic Vegetable Types on Mobile Devices Using Lightweight Deep Learning Models Wayahdi, M. Rhifky; Ruziq, Fahmi; Nurhajijah, Nurhajijah
Journal of Technology and Computer Vol. 1 No. 4 (2024): November 2024 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Hydroponic cultivation requires precise monitoring to ensure crop quality and productivity, yet manual identification of vegetable varieties and their growth status remains labor-intensive and prone to error. This study aims to develop a real-time, mobile-based classification system for hydroponic vegetables using lightweight Deep Learning models optimized for edge computing. The proposed method evaluates two distinct architectures, MobileNetV3 and YOLO-Nano, trained via transfer learning on a dataset comprising major hydroponic crops such as Lettuce, Pak Choy, Mustard Greens, and Cherry Tomatoes. Experimental results demonstrate that while YOLO-Nano offers superior inference speed (~55 FPS), MobileNetV3 achieves a significantly higher classification accuracy of 96.4% while maintaining a real-time performance of ~35 FPS on standard mobile hardware. The study concludes that MobileNetV3 provides the optimal balance between accuracy and computational efficiency for handheld agricultural applications. This research contributes a scalable, low-cost solution for smart farming, enabling producers to perform rapid, on-site digital inventory and quality assessment without reliance on internet connectivity.
Co-Authors Anggraini, Mia Aripin Rambe Baginda Harahap Chairul Imam Dewi Wahyuni Dewi Wahyuni Dinur Syahputra Ellanda Purwawijaya Fachry Abda El Rahman Fadli, Rahmad Fithrie Soufitri Ginting, Subhan Hafiz Nanda Hutasoit, Rutmian Mariama Ilham Syahputra Intan Pratiwi, Dea Balqis Irna Herliza Rizki Junaidi Lubis M. Rhifky Wahyadi M. Rhifky Wayahdi Mayang Mughnyanti Melky Suhery Simamora Muhammad Furqon Siregar Nanda Ginting, Subhan Hafiz Nuary Singarimbun, Roy Nurhajijah, Nurhajijah Rahmad Syuhada Rambe, Aripin Sattar Rasul Sianipar, Erika T. Siti Farida Sridewi, Nurmala Subhan Hafiz Nanda Ginting Subhan Hafiz Nanda Ginting Surya Darma Swanda, Ari Syahputra, Dinur Weima, Zhang