ac

Implementation of YOLO in Cabbage Plant Disease Detection for Smart and Sustainable Agriculture

Authors

  • Muhammad Andryan Wahyu Saputra Universitas Jember, Indonesia
  • Damar Novtahaning Universitas Jember, Indonesia
  • Narandha Arya Ranggianto Jember University
  • Dwi Wijonarko Jember University

DOI:

10.47709/brilliance.v4i2.5054

Keywords:

cabbage plant disease, Classification, deep learning, Yolo segmentation

Dimension Badge Record



Abstract

Cabbage plants are a commodity needed by the community and an export commodity that must have good quality and be worth selling. There are approaches to create detection systems, namely rule-based and image-based. The use of images allows the system to be reorganized by training data, resulting in a flexible system. The image will be detected by the model and then predict the cabbage plant disease. The data used is image data, namely Alternaria Spots, Healthy, Black Root, and White Rust. Implementation This research tests the YOLO model in making a detection system with the highest precision-confidence result for all labels is 78,5%. While in confusion-matrix testing, the highest result is 0.67 in White Rust disease. This indicates that the YOLO model can identify diseases in cabbage plants based on data that has been trained with great results.

Google Scholar Cite Analysis
Abstract viewed = 113 times

References

Dhamayanti, R., Fatchiyatur Rohma, M., & Zahara, S. (2021). Penggunaan Deep Learning Dengan Metode Convolutional Neural Network Untuk Klasifikasi Kualitas Sayur Kol Berdasarkan Citra Fisik. SUBMIT: Jurnal Ilmiah Teknologi Informasi Dan Sains, 1 (1), 08–15.

Du, L., Ji, J., Pei, Z., Zheng, H., Fu, S., Kong, H., … & Chen, W. (2020). Improved detection method for traffic signs in real scenes applied in intelligent and connected vehicles. Iet Intelligent Transport Systems, 14(12), 1555-1564. https://doi.org/10.1049/iet-its.2019.0475

Katafuchi, R. (2021). Lea-net: layer-wise external attention network for efficient color anomaly detection.. https://doi.org/10.48550/arxiv.2109.05493

Li, D., Ahmed, F., Wu, N., & Sethi, A. (2022). Yolo-jd: a deep learning network for jute diseases and pests detection from images. Plants, 11(7), 937. https://doi.org/10.3390/plants11070937

Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J. (2018). Path aggregation network for instance segmentation.. https://doi.org/10.1109/cvpr.2018.00913

Liu, T., Pang, B., Ai, S., & Sun, X. (2020). Study on visual detection algorithm of sea surface targets based on improved yolov3. Sensors, 20(24), 7263. https://doi.org/10.3390/s20247263

Haryobismoko, K., Muflikhah, L., & Perdana, R. S. (2023). Identifikasi Penyakit Tanaman Cabai menggunakan Metode Learning Vector Quantization (LVQ). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 7(4), 1953–1960. Diambil dari https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/12629

Meena, B. (2023). Plant health prediction and monitoring based on convolution neural network in north-east india. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 12-19. https://doi.org/10.17762/ijritcc.v11i2s.6024

Roy, A. and Bhaduri, J. (2023). A computer vision enabled damage detection model with improved yolov5 based on transformer prediction head.. https://doi.org/10.48550/arxiv.2303.04275

Septian, M. R. D., Paliwang, A. A. A., Cahyanti, M., & Swedia, E. R. (2020). Penyakit Tanaman Apel Dari Citra Daun Dengan Convolutional Neural Network. Sebatik, 24(2), 207–212. https://doi.org/10.46984/sebatik.v24i2.1060

Septiana, Y., Barwardono, W., & Nurhalim, A. (2024). Pengembangan sistem pakar diagnosa penyakit kubis berbasis forward chaining. Jurnal Algoritma, 21(1), 87–97. https://doi.org/10.33364/algoritma/v.21-1.1438

Simanjuntak, A., & Syahputra, G. (2020). Diagnosis System Penyakit Clubroot pada Tanaman Kubis dengan Menggunakan Metode Certainty Factor. Jurnal SI (SISTEM INFORMASI, 1(1), 1–10. https://doi.org/https://doi.org/10.53513/jct.v4i7.2396

Wang, G., Ding, H., Yang, Z., Li, B., Wang, Y., & Bao, L. (2021). Trc?yolo: a real?time detection method for lightweight targets based on mobile devices. Iet Computer Vision, 16(2), 126-142. https://doi.org/10.1049/cvi2.12072

Xu, T., Ma, A., Lv, H., Dai, Y., Lin, S., & Tan, H. (2023). A lightweight network of near cotton?coloured impurity detection method in raw cotton based on weighted feature fusion. Iet Image Processing, 17(9), 2585-2595. https://doi.org/10.1049/ipr2.12788

Zaidi, S., Ansari, M., Aslam, A., Kanwal, N., Asghar, M., & Lee, B. (2022). A survey of modern deep learning based object detection models. Digital Signal Processing, 126, 103514. https://doi.org/10.1016/j.dsp.2022.103514

Zhang, J., Liu, J., Chen, Y., Feng, X., & Sun, Z. (2021). Knowledge mapping of machine learning approaches applied in agricultural management—a scientometric review with citespace. Sustainability, 13(14), 7662. https://doi.org/10.3390/su13147662

Downloads

ARTICLE Published HISTORY

Submitted Date: 2024-12-05
Accepted Date: 2024-12-06
Published Date: 2024-12-26

How to Cite

Saputra, M. A. W., Novtahaning, D., Narandha Arya Ranggianto, & Dwi Wijonarko. (2024). Implementation of YOLO in Cabbage Plant Disease Detection for Smart and Sustainable Agriculture. Brilliance: Research of Artificial Intelligence, 4(2), 798-804. https://doi.org/10.47709/brilliance.v4i2.5054