ARCHIVES

1. Improved yolo v3 convolutional neural network. Frontiers in plant science, 11:898, 2020.
2. Achyut Morbekar, Ashi Parihar, and Rashmi Jadhav. Crop disease detection using yolo. In 2020 International Conference for Emerging
Technology (INCET), pages 1–5, 2020.
3. G Nihar, V Raghavendra, V Suresh, and M Sandhya. Rice crop disease detection using yolo algorithm. In National Conference On Advances
in Electronics Signal Processing and Communications (AESPC-2020), volume 6, 2020.
4. Ma Kristin Agbulos, Yovito Sarmiento, and Jocelyn Villaverde. Identification of leaf blast and brown spot diseases on rice leaf with yolo
algorithm. In 2021 IEEE 7th International Conference on Control Science and Systems Engineering (ICCSSE), pages 307–312. IEEE, 2021.
5. Martina Lippi, Niccolò Bonucci, Renzo Fabrizio Carpio, Mario Contarini, Stefano Speranza, and Andrea Gasparri. A yolo -based pest
detection system for precision agriculture. In 2021 29th Mediterranean Conference on Control and Automation (MED), pages 342–347,
2021.
6. Monalika Padma Reddy and A Deeksha. Mulberry leaf disease detection using yolo. International Journal of Advance Research, Id eas and
Innovations inTechnology, 7:1816–1821, 2021.
7. Midhun P Mathew and Therese Yamuna Mahesh. Determining the region of apple leaf affected by disease using yolo v3. In 2021 International
Conference onCommunication, Control and Information Sciences (ICCISc), volume 1, pages 1–4, 2021.
8. Shani Verma, Shrivishal Tripathi, Anurag Singh, Muneendra Ojha, and Ravi R Saxena. Insect detection and identification using yolo
algorithms on soybean crop. In TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON), pages 272–277, 2021.
9. Nidhi Kundu, Geeta Rani, and Vijaypal Singh Dhaka. Seeds classification and quality testing using deep learning and yolo v5. In Proceedings
of the International Conference on Data Science, Machine Learning and Artificial Intelligence, pages 153–160, 2021. M.A.R Alif Et Al.:
Yolov1 To Yolov10: A Comprehensive Review Of Yolo Variants And Their Application In The Agricultural Domain - JUNE 17, 2024.
10. Midhun P Mathew and Therese Yamuna Mahesh. Leaf-based disease detection in bell pepper plant using yolo v5. Signal, Image and Video
Processing, pages 1–7, 2022.
11. Md Janibul Alam Soeb, Md Fahad Jubayer, Tahmina Akanjee Tarin, Muhammad Rashed Al Mamun, Fahim Mahafuz Ruhad, Aney Parven,
Nabisab Mujawar Mubarak, Soni Lanka Karri, and Islam Md Meftaul. Tea leaf disease detection and identification based on y olov7 (yolo-
t). Scientific reports, 13(1):6078, 2023.
12. Zhenyang Xue, Renjie Xu, Di Bai, and Haifeng Lin. Yolo-tea: A tea disease detection model improved by yolov5. Forests, 14(2):415, 2023.
13. H.Yang ,S.sheng ,F.Jiang “YOLO-SDW: A Method for detecting infection in corn leaves ,”Energy Reports,vol 12,pp.6102-6111,2024,
https://www.sciencedirect.com/science/article/pii/S2352484724007923.
14. Meng Y, Zhan J, Li K, Yan F, Zhang L. A rapid and precise algorithm for maize leaf disease detection based on YOLO MSM. Sci Rep. 2025
Feb 19;15(1):6016. doi: 10.1038/s41598-025-88399-1. PMID: 39971956; PMCID: PMC11839928.
15. Hao, S., Gao, E., Ji, Z., & Ganchev, I. (2025). BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on
YOLOv11n. Applied Sciences, 15(15), 8231. https://doi.org/10.3390/app15158231.
16. Al Husaini, M., Rachmat Raharja , A. ., Cahaya Putra , V. H. ., & Lukmana, H. H. . (2025). Enhanced Plant Disease Detection Using
Computer Vision YOLOv11: Pre-Trained Neural Network Model Application . Journal of Computer Networks, Architecture and High
Performance Computing, 7(1),82–95. https://doi.org/10.47709/cnahpc.v7i1.5146