Comparative Analysis of Computer Vision Models for Detecting Nilam Plant Diseases: A Case Study of MobileNet vs. YOLO

Jimmy Robot; Nancy Tuturoong; Ade Yusupa

doi:10.52958/iftk.v22i1.12800

Authors

Jimmy Robot Universitas Sam Ratulangi
Nancy Tuturoong Universitas Sam Ratulangi
Ade Yusupa Universitas Sam Ratulangi

DOI:

https://doi.org/10.52958/iftk.v22i1.12800

Keywords:

MobileNet, Nilam, Plant Disease, Real-time detection, YOLO

Abstract

Nilam Plant (Patchouli plant) located in Minahasa Regency can be affected by several diseases that can significantly reduce their essential oil capacity. The common practice of monitoring crops for diagnosis relies on labor-intensive methods that can be variable in accuracy, depending on previous experience of the tended. The main goal of the study is to develop and to compare model performance using Deep Learning computer vision in monitoring conditions of patchouli plants with respect to different conditions (healthy, bacterial wilt, viral, and budok). This study assess and compare MobileNet (a lightweight classifier) against Deep Learning based object detectors (YOLOv5, v8, v11) using an underlying dataset that was created unimpeachably in a natural patchouli field setting, consisting of 3,000 images which contain 3,820 annotated bounding boxes, across 4 classes (Healthy, Bacterial Wilt, Viral, Budok). Evaluation reveals a clear trade-off between the two models. MobileNet finds (nearly perfect) classification accuracy of 94.7% (F1-score>0.90 for all classes), while the faster YOLOv8l yields an 88.2% mAP50. Both models had the hardest time dealing with the "Viral" class due to visual similarities it shared with the healthy class (F1: 0.90, mAP: 0.81). MobileNet produced better accuracy (94.7%) but had slower inference time (3.0s). YOLOv8l provided real-time detection (1.4s) but lower mAP (88.2%). Our recommendation is a hybrid 2-stage system (YOLO-drone scan; MobileNet-farmer confirmation) as an operational approach for Precision Agriculture in Patchouli farming. Overall, the main takeaway is that: MobileNet is intended for diagnostic application and YOLOv8 superior for real-time video-based field monitoring.

References

E. M. A. T. T. J. Manangkot, S. Tumbelaka, and J. Pelealu, “Identifikasi Penyakit Tanaman Nilam (Pogostemon cablin Benth.) di Desa Kembes Kecamatan Tombulu Kabupaten Minahasa,” J. Bios Logos, vol. 9, no. 2, pp. 87–94, 2019, doi: 10.35799/jbl.9.2.2019.23478.

J. G. A. Barbedo, “Factors influencing the use of deep learning for plant disease recognition,” Biosyst. Eng., vol. 172, pp. 84–91, 2018, doi: 10.1016/j.biosystemseng.2018.05.013.

S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using Deep Learning for Image-Based Plant Disease Detection,” Front. Plant Sci., vol. 7, p. 1419, 2016, doi: 10.3389/fpls.2016.01419.

A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Comput. Electron. Agric., vol. 147, pp. 70–90, 2018, doi: 10.1016/j.compag.2018.02.016.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779–788. doi: 10.1109/CVPR.2016.91.

C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J. Big Data, vol. 6, no. 1, p. 60, 2019, doi: 10.1186/s40537-019-0197-0.

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 4510–4520. doi: 10.1109/CVPR.2018.00474.

G. Jocher, “ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation,” 2022, Zenodo. doi: 10.5281/zenodo.7347926.

D. Reis, J. Kupec, J. Hong, and A. Daoudi, “Real-Time Flying Object Detection with YOLOv8,” arXiv preprint arXiv:2305.09972, 2023.

T.-Y. Lin et al., “Microsoft COCO: Common Objects in Context,” in European Conference on Computer Vision (ECCV), 2014, pp. 740–755.

F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1251–1258. doi: 10.1109/CVPR.2017.195.

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137–1149, 2017, doi: 10.1109/TPAMI.2016.2577031.

K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv preprint arXiv:1409.1556, 2014.

A. Rahman, “Dataset for Plant Disease Classification and Detection Using Deep Learning Techniques,” Journal of Agricultural Informatics, vol. 11, no. 1, p. e5, 2020.

D. S. Wita, “Deteksi Penyakit pada Tanaman Eucalyptus Pellita Menggunakan MobileNet,” International Journal of Plant Health, vol. 10, no. 1, pp. 23–30, 2022.

Mashuri and Enri, “Penerapan Inception V3 untuk Deteksi Penyakit pada Tanaman Gandum,” Journal of Agricultural Technology, vol. 15, no. 4, pp. 145–156, 2022.

E. Anggiratih, M. Z. Mustaqim, and D. Muriatmoko, “Transfer Learning untuk Klasifikasi Penyakit dan Hama Padi Menggunakan MobileNetV2,” Techno.Com, vol. 22, no. 3, pp. 562–575, 2022, doi: 10.33633/tc.v22i3.8516.

M. G. Pradana, H. B. Seta, N. Irzavika, P. H. Saputro, and R. Rusiyono, “Levenshtein Distance Algorithm in Javanese Character Translation Machine Based on Optical Character Recognition,” JOIV : International Journal on Informatics Visualization, vol. 9, no. 4, p. 1411, Jul. 2025, doi: 10.62527/joiv.9.4.3151.

D. Arisandi, A. Khoerunnisa, R. Subekti, A. Eko Setiawan, and C. Ramdani, “Performance Evaluation of CLAHE-Enhanced Edge Detection on Low-Light Faces,” Journal of Computing Innovations and Emerging Technologies, vol. 1, no. 1, pp. 7–10, Jul. 2025, doi: 10.64472/jciet.v1i1.2.