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All Journal Jurnal Agrotek UMMat SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Jurnal Ilmiah Hijau Cendekia Agroteknika Journal La Lifesci Technologica Jurnal Pengabdian Nasional (JPN) Indonesia
Hasbi Mubarak Suud
Agricultural Science Department, Faculty Of Agriculture, University Of Jember, Indonesia
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Kinerja Convolutional Neural Network untuk Klasifikasi Level Kadar Air Tanah Berdasarkan Gambar Permukaan Tanah Hasbi Mubarak Suud; Subhan Arif Budiman; Ebban Bagus Kuntadi; Dwi Erwin Kusbianto; Ika Purnamasari
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 15 No. 2 (2026): April 2026
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v15i2.571-579

Abstract

Computer vision offers a promising method for soil moisture assessment especially for real-time field monitoring where sensor-based measurements are limited. This study evaluates the performance of a traditional Convolutional Neural Network (CNN) and ResNet-50 architecture for classifying soil moisture levels directly from in-situ surface images. The research involved 200 field-captured images and corresponding moisture data from a rainfed agricultural area. The models were trained with datasets grouped into two, three, and four moisture categories to test performance under varying complexity. The results showed poor model performance, characterized by high instability and severe overfitting across all experiments. Model accuracy for the traditional CNN significantly decreased from 0.513 to 0.256 as the number of classification categories increased and from 0.487 to 0.205 for ResNet-50. High RMSE values from 0.433 to 0.507 further confirmed substantial prediction errors. This finding highlights the limitation of RGB-based in-situ imagery for soil moisture classification, where environmental variability dominates the visual signal. It also suggests that soil moisture-related features are not sufficiently distinguishable under uncontrolled field conditions. The study concludes that the high variability of direct field images due to factors like inconsistent lighting, illumination, and the presence of non-soil objects is a primary obstacle to accurate classification. Future studies should implement advanced pre-processing techniques such as segmentation to reduce illumination noise.
Co-Authors Adhitya Wardhono Agung Nugroho Puspito Aulia Rafidah Pananto Bambang Kusmanadhi Ciplis Gema Qori'ah Clara Sintha Putri Pratiwi Dodik Priyanto Dwi Erwin Kusbianto Dyah Ayu Savitri Ebban Bagus Kuntadi Gatot Subroto Hasna Afaf Faizah Ika Purnamasari Insan Sabri Helwandi Keysa Churiyyatul Haris Laras Sekar Arum Linda Ayu Islamia Mellfani Rhamadinda Nendra Tigara Mohammad Ubaidillah Nabila Safira Putri Madani Naiya Nalya Cahyadi Noer Novijanto Nurhayadatul Haliza Oria Alit Farisi Risa Umami Rosyady, Muhammad Gufron Rufiani Nadzirah S, Setiyono Safa Rajni Ismaya Setiyono Subhan Arif Budiman Telpivani Solehatul Putri Untari