ANALISIS PENGARUH RUANG WARNA RGB, LAB, DAN HSV TERHADAP KINERJA RESNET50 UNTUK KLASIFIKASI PENYAKIT UMBI KENTANG

Ray Restu Seftiyan; Martanto .; Yudhistira Arie Wijaya; Tati Suprapti

Authors

Ray Restu Seftiyan STMIK IKMI Cirebon, Indonesia
Martanto . STMIK IKMI Cirebon, Indonesia
Yudhistira Arie Wijaya STMIK IKMI Cirebon, Indonesia
Tati Suprapti STMIK IKMI Cirebon, Indonesia

Keywords:

ResNet50, ruang warna, RGB, Lab, HSV, penyakit umbi kentang, deep learning

Abstract

Penelitian ini bertujuan untuk menganalisis pengaruh ruang warna RGB, Lab, dan HSV terhadap kinerja model ResNet50 dalam mengklasifikasikan penyakit umbi kentang menggunakan dataset Kaggle Potato Diseases. Variasi ruang warna berpotensi memengaruhi kemampuan model dalam menangkap informasi visual yang relevan, terutama pada kasus citra tanaman yang dipengaruhi perbedaan cahaya, tekstur, dan intensitas warna. Metode yang digunakan meliputi pra-pemrosesan citra, konversi ruang warna, data augmentation, serta penerapan transfer learning pada arsitektur ResNet50 yang dimodifikasi pada lapisan akhirnya. Evaluasi kinerja dilakukan menggunakan metrik akurasi, precision, recall, f1-score, dan confusion matrix. Hasil penelitian menunjukkan bahwa ruang warna RGB memberikan performa terbaik dibandingkan Lab dan HSV, dengan akurasi validasi tertinggi serta distribusi kesalahan yang lebih rendah antar kelas penyakit. Representasi warna Lab menunjukkan performa sedang, sedangkan HSV menghasilkan akurasi paling rendah. Temuan ini menunjukkan bahwa pemilihan ruang warna berpengaruh signifikan terhadap robustness dan akurasi model CNN dalam deteksi penyakit tanaman. Penelitian ini dapat menjadi dasar untuk pengembangan sistem deteksi penyakit berbasis citra yang lebih optimal pada skala pertanian cerdas (smart farming).

References

A. Upadhyay Et Al., “Deep Learning And Computer Vision In Plant Disease Detection: A Comprehensive Review Of Techniques, Models, And Trends In Precision Agriculture,” Artif. Intell. Rev., Vol. 58, No. 3, P. 92, 2025, Doi: 10.1007/S10462-024-11100-X.

I. Ahmed And P. K. Yadav, “A Systematic Analysis Of Machine Learning And Deep Learning Based Approaches For Identifying And Diagnosing Plant Diseases,” Sustainable Operations And Computers, Vol. 4, Pp. 96–104, 2023, Doi: 10.1016/J.Susoc.2023.03.001.

F. O. Isinkaye, M. O. Olusanya, And P. K. Singh, “Deep Learning And Content-Based Filtering Techniques For Improving Plant Disease Identification And Treatment Recommendations: A Comprehensive Review,” Heliyon, Vol. 10, No. 9, P. E29583, 2024, Doi: 10.1016/J.Heliyon.2024.E29583.

A. M. Roy And J. Bhaduri, “A Deep Learning Enabled Multi-Class Plant Disease Detection Model Based On Computer Vision,” Ai, Vol. 2, No. 3, Pp. 413–428, 2021, Doi: 10.3390/Ai2030026.

M. S. Krishna, P. Machado, R. I. Otuka, S. W. Yahaya, F. Neves Dos Santos, And I. K. Ihianle, “Plant Leaf Disease Detection Using Deep Learning: A Multi-Dataset Approach,” J (Basel)., Vol. 8, No. 1, P. 4, 2025, Doi: 10.3390/J8010004.

I. Hernández, “In-Field Disease Symptom Detection And Localisation Using Convolutional Neural Networks Under Natural Lighting Conditions,” Comput. Electron. Agric., Vol. 211, P. 107317, 2024, Doi: 10.1016/J.Compag.2024.107317.

S. M. Javidan, “Feature Engineering To Identify Plant Diseases Using Image Quality-Aware Preprocessing And Classification Frameworks,” Comput. Electron. Agric., Vol. 220, P. 106988, 2024, Doi: 10.1016/J.Compag.2024.106988.

A. Y. Ashurov, “Enhancing Plant Disease Detection Through Deep Learning With Illumination-Aware Data Augmentation And Normalization,” Front. Plant Sci., Vol. 16, P. 1505857, 2025, Doi: 10.3389/Fpls.2024.1505857.

M. Shafay Et Al., “Recent Advances In Plant Disease Detection: Challenges And Opportunities,” Plant Methods 2025 21:1, Vol. 21, No. 1, Pp. 140-, Oct. 2025, Doi: 10.1186/S13007-025-01450-0.

P. Sridhar, “Enhancing Image-Based Classification For Crop Disease Detection Under Heterogeneous Field Conditions,” Sci. Rep., Vol. 15, P. 23568, 2025, Doi: 10.1038/S41598-025-23568-W.

N. Ahmad, S. Singh, M. F. Alajmi, A. Hussain, And K. Raza, “Cropgcnn: Color Space-Based Crop Disease Classification Using Group Convolutional Neural Network,” Peerj Comput. Sci., Vol. 10, P. E2136, 2024, Doi: 10.7717/Peerj-Cs.2136/Supp-1.

Z. Xian, R. Huang, And D. Towey, “Convolutional Neural Network Image Classification Based On Different Color Spaces,” Tsinghua Sci. Technol., Vol. 30, No. 1, Pp. 402–417, 2025, Doi: 10.26599/Tst.2024.9010001.

I. Jiménez-López, “Color Space Comparison Of Isolated Cervix Cells For Morphology Classification,” Ai, Vol. 6, No. 10, P. 261, 2025, Doi: 10.3390/Ai6100261.

H. Gupta, A. J. Lilienthal, H. Andreasson, And P. Kurtser, “Ndt-6d For Color Registration In Agri-Robotic Applications,” J. Field Robot., Vol. 40, No. 6, Pp. 1603–1619, 2023, Doi: 10.1002/Rob.22194.

S. Ghanem And J. H. Holliman, “Impact Of Color Space And Color Resolution On Vehicle Recognition Models,” J. Imaging, Vol. 10, No. 7, P. 155, 2024, Doi: 10.3390/Jimaging10070155.

J. Lu, L. Tan, And H. Jiang, “Review On Convolutional Neural Network (Cnn) Applied To Plant Leaf Disease Classification,” Agriculture, Vol. 11, No. 8, P. 707, 2021, Doi: 10.3390/Agriculture11080707.

D. Mamadou, K. Ayikpa, A. Ballo, And B. Kouassi, “Analysis Of The Impact Of Color Spaces On Skin Cancer Diagnosis Using Deep Learning Techniques,” Revue D’intelligence Artificielle, Vol. 37, No. 6, Pp. 1377–1385, 2023, Doi: 10.18280/Ria.370601.

Q. Tian Et Al., “Enhancing Practicality Of Deep Learning For Crop Disease Identification Under Field Conditions: Insights From Model Evaluation And Crop-Specific Approaches,” Pest Manag. Sci., Vol. 80, No. 11, Pp. 5864–5875, 2024, Doi: 10.1002/Ps.8317.