Article Per Year (5 Year)
p-Index From 2021 - 2026
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All Journal Smart Techno (Smart Technology, Informatic and Technopreneurship) International Journal of Technology and Modeling
Fatimah, Aisya
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Predicting Crop Water Requirements Using IoT Sensor Data for Deep Learning Saluky, Saluky; Fatimah, Aisya
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 7 No. 2 (2025)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v7i02.151

Abstract

The optimization of irrigation is a crucial factor in enhancing agricultural productivity and resource efficiency. This study proposes a deep learning-based approach to predict plant water requirements using data from IoT sensors. The system collects real-time environmental parameters such as soil moisture, temperature, humidity, and solar radiation, which are then processed using a deep learning model to generate accurate irrigation recommendations. The model is trained and evaluated on historical sensor data to ensure robustness and reliability in varying climatic conditions. The proposed method aims to minimize water wastage while maintaining optimal soil moisture levels, thereby improving crop health and yield. Experimental results demonstrate that the deep learning model outperforms conventional threshold-based irrigation systems in terms of prediction accuracy and water conservation. This research contributes to the advancement of smart farming by integrating IoT and artificial intelligence for precision agriculture.
Unmanned Aerial Vehicles (UAVs) for Pest and Disease Detection in Rice Cultivation: A Systematic Review Saluky, Saluky; Marine, Yoni; Fatimah, Aisya
International Journal of Technology and Modeling Vol. 4 No. 3 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i3.158

Abstract

This paper presents a systematic review of the use of Unmanned Aerial Vehicles (UAVs) for pest and disease detection in rice cultivation, a critical challenge in maintaining yield stability and reducing chemical overuse in global food systems. The study aims to synthesize current approaches, technologies, and algorithms employed in UAV-based monitoring of rice pests and diseases, while identifying research gaps and future directions for precision rice farming. Following PRISMA-inspired guidelines, a Systematic Literature Review (SLR) was conducted across major scientific databases (Scopus, Web of Science, IEEE Xplore, and ScienceDirect) using predefined keyword combinations related to UAVs, rice, pest/disease detection, and remote sensing. Inclusion criteria focused on peer-reviewed studies that explicitly employed aerial platforms for detecting biotic stress in rice, while review papers, non-rice crops, and purely simulation-based works were excluded. The findings highlight three dominant technology dimensions: sensing modalities, with RGB and multispectral imagery being most prevalent, followed by hyperspectral and thermal sensors; analytical methods, ranging from traditional vegetation indices and thresholding to advanced machine learning and deep learning models; and operational considerations, including flight altitude, spatial resolution, and temporal frequency of data acquisition. The review contributes by proposing a conceptual framework linking sensor choice, image processing pipelines, and pest/disease symptom characteristics in rice, and by outlining open challenges regarding data standardization, smallholder adoption, and model transferability across regions.
Co-Authors Marine, Yoni Saluky