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All Journal JOIV : International Journal on Informatics Visualization Jurnal SOLMA E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Community Development Journal: Jurnal Pengabdian Masyarakat JUKI : Jurnal Komputer dan Informatika Journal of Information Technology and Its Utilization Jurnal Studi Inovasi DIPANEGARA KOMPUTER SISTEM INFORMASI (DIPAKOMSI)
Muh. Syahlan Natsir
Universitas Dipa Makassar
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Other

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Comparison Architecture of Convolutional Neural Network for Fertility Level of Paddy Soil Detection Natsir, Muh. Syahlan; Suyuti, Ansar; Nurtanio, Ingrid; Palantei, Elyas
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3342

Abstract

This study proposes to detect the fertility of paddy soil based on texture, the power of Hydrogen (pH), and the amount of production. Fertile paddy soil provides essential nutrients and supports optimal plant growth. Therefore, monitoring and analyzing soil fertility is crucial in agricultural land management, which significantly increases rice yields. Paddy soil is categorized into three parts: very fertile soil, fertile soil, and reasonably fertile soil. This research proposes a new approach to detecting soil fertility levels based on factors that influence soil fertility using the Convolutional Neural Network (CNN) algorithm. There are 558 paddy soil datasets of 178 very fertile datasets, 135 fertile datasets, and 245 quite fertile datasets. In this research, we conducted trials using the CNN, Resnet, Enet, and VGG19 models. According to the test results, the CNN model using the Adam optimizer and a learning rate of 0.001 achieves the highest training accuracy of 0.9687 and validation accuracy of 0.8333. This suggests that this model can accurately identify the fertility of paddy soil, making it easier to calculate the fertility of paddy soil through its use. Future research can expand this study by integrating additional soil parameters, such as nitrogen, phosphorus, potassium levels, and organic matter content, to improve classification accuracy further. Additionally, employing multimodal data sources, such as remote sensing and hyperspectral imaging, could enhance the model's robustness in various environmental conditions. Further optimization of deep learning architectures and Artificial Intelligence (AI) techniques can also provide better interpretability and usability for agricultural stakeholders.
Co-Authors Abdul Kadir Jailani ABDUL RAUF Akbar Bahtiar Alam, Samsu Andi Irmayana Andi Zulkarnain, Andi Angdy Erna Ansar Suyuti Anugra, Anugra Asrul Syam Asrul Syam Gading, Gading Hasriani Hasriani Herlinda Herlinda Husain Husain Husain T Ida Ayu Putu Sri Widnyani Ingrid Nurtanio Iqbal Gunawan Kunda, Asri M. Redinal Muktar Marsa Marsa Muhammad Rifqi Faidhil Syam Muhardi Muhardi Muhardi Muhardi _ Nirwana Nirwana Nurlindasari Tamsir Nurlindasari Tamsir Palantei, Elyas Putri Nurainun Yusuf R Thabrani Rauf, Abdul Riki Taruk Bua Risnayanti Andi Djamro Samsu Alam Suci Rahma Dani Rachman Suryani Suryani Suryani Suryani Syam, Asrul Syiar Al Tarif Thabrani R Thabrani Thabrani Wahyudi Rusdi Yaury Riski Wicaksono