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All Journal Jurnal Teknologi Journal of Applied Data Sciences Indo-MathEdu Intellectuals Journal CSRID
Mardison Mardison
Universitas Putra Indonesia YPTK Padang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Mathematics

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Journal : Journal of Applied Data Sciences

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Development of New Identification Formula to Extract Organic Fertilizer Content Based on Organic Fertilizer Image Ramadhanu, Agung; Mardison, Mardison; Hendri, Halifia; Hadi, Febri; Rani, Larissa Navia; Yuhandri, Yuhandri
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1300

Abstract

Traditional laboratory techniques for examining the nutrient content of organic fertilizers, specifically nitrogen (N), phosphorus (P), and potassium (K), are expensive, time-intensive, and pose environmental hazards. To address these issues, this paper presents a novel, non-destructive, image-based classification algorithm to identify fertilizer nutrient content. The proposed technique integrates color space conversion, unsupervised clustering, texture extraction, and an adapted New Identification Weighting (NIW) method. The NIW is derived from prior probability-based distance measurements and optimized with a balancing weighting factor to improve analytical stability across heterogeneous agricultural images. First, RGB images of fertilizers are converted into the perceptually uniform CIE L*a*b color space, which enhances color distinction under varying lighting conditions. Next, the images are segmented using K-Means clustering, followed by Gray-Level Co-occurrence Matrix (GLCM) extraction to capture textural and structural features. A key innovation of this research is the NIW method, functioning as an adaptive feature prioritization tool that assesses each features contribution to nutrient classification, effectively overcoming the limitations of previous a priori approaches. The system was tested on a dataset of 500 organic fertilizer images, achieving an overall classification accuracy of 97%, demonstrating its effectiveness and robustness. This approach offers a highly accurate and interpretable alternative to conventional chemical testing, making it a feasible, scalable, and affordable field tool for smart farming. By enabling on-site nutrient analysis, it strongly supports sustainable agricultural practices. Future work will focus on enhancing the systems flexibility to varying environmental conditions and integrating this approach into mobile-based diagnostic devices to facilitate real-time decision-making in agriculture.
Co-Authors Awal, Hasri Chan, Fajri Rinaldi Febri Hadi Gaby Arnez Halifia Hendri Mahdi, Arisul Negoro, Wahyu Saptha Putra, Yoga Ananda Ramadhanu, Agung Ramadhanu, Agung Ramadhanu, Agung Ramadhanu, Agung Rani, Larissa Navia Rani, Larissa Navia Setia Budi Sumijan Sumijan Syafrika Deni Rizki, Syafrika Deni Witri, Repelita Yasmin, Nabilla Yuhandri Yuhandri, Yuhandri