<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">IJOSI</journal-id><journal-title-group><journal-title>International Journal of Systematic Innovation</journal-title></journal-title-group><issn>2077-7973</issn><eissn>2077-8767</eissn><publisher><publisher-name>AccScience Publishing</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.6977/IJoSI.202309_7(7).0005</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Cotton leaf disease classification using YOLO deep learning frame-work and indigenous dataset</title><url>https://artdesignp.com/journal/IJOSI/7/7/10.6977/IJoSI.202309_7(7).0005</url><author>KolachiAbdul Rahim,SoomroShoaib R.,BalochShadi Khan,PatoliAamir Ali,AnwarSohail</author><pub-date pub-type="publication-year"><year>2023</year></pub-date><volume>7</volume><issue>7</issue><history><date date-type="pub"><published-time>2023-09-25</published-time></date></history><abstract/><keywords>Cotton disease classification, deep learning, real-time detection, YOLO</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>
Ahmed, M. R. (2021). Leveraging convolutional neural net-work and transfer learning for cotton plant and leaf disease recognition. Int. J. Image Graph. Signal Process, 13, 47&amp;ndash;62.
Applied Earth Observations and Remote Sensing, 9(9), 4344&amp;ndash;4351. https://doi.org/10.1109/JSTARS.2016.2575360
Arsenovic, M., Karanovic, M., Sladojevic, S., Anderla, A., &amp;amp; Stefanovic, D. (2019). Solving current limitations of deep learning-based approaches for plant disease detection. Symmetry, 11(7). https://doi.org/10.3390/sym11070939
Bodhe, K. D., Taiwade, H. V., Yadav, V. P., &amp;amp; Aote, N. V. (2018). Implementation of Prototype for Detection &amp;amp; Di-agnosis of Cotton Leaf Diseases using a Rule-Based Sys-tem for Farmers. Proceedings of the International Confer-ence on Communication and Electronics Systems (ICCES 2018), 165&amp;ndash;169.
Cotton Disease Dataset | Kaggle. (2022). https://www.kaggle.com/datasets/janmejaybhoi/cotton-disease-dataset
Haque, M. E., Hoque, S., Paul, M., Haque, E., Rahman, A., Ju-naeid, I., &amp;amp; Hoque, S. U. (2022). Rice Leaf Disease Clas-sification and Detection Using YOLOv5 arXiv preprint arXiv:2209.01579
Jiang, P., Ergu, D., Liu, F., Cai, Y., &amp;amp; Ma, B. (2021). A Review of Yolo Algorithm Developments. Procedia Computer Science, 199, 1066&amp;ndash;1073. https://doi.org/10.1016/j.procs.2022.01.135
Jubayer, F., Soeb, J. A., Mojumder, A. N., Paul, M. K., Barua, P., Kayshar, S., Akter, S. S., Rahman, M., &amp;amp; Islam, A. (2021). Detection of mold on the food surface using YOLOv5. Current Research in Food Science, 4, 724&amp;ndash;728. https://doi.org/10.1016/j.crfs.2021.10.003
Juman Jhatial, M., Ahmed Shaikh, R., Ahmed Shaikh, N., Rajper, S., Hussain Arain, R., Hussain Chandio, G., Qa-dir Bhangwar, A., Shaikh, H., &amp;amp; Hussain Shaikh, K. (2022). Deep Learning-Based Rice Leaf Diseases Detection Using Yolov5. Sukkur IBA Journal of Com-puting and Mathematical Sciences 6(1).
Kumar, S., Jain, A., Shukla, A. P., Singh, S., Raja, R., Rani, S., Harshitha, G., AlZain, M. A., &amp;amp; Masud, M. (2021). A comparative analysis of machine learning algo-rithms for detection of organic and nonorganic cotton diseases. Mathematical Problems in Engineering, 2021, 1&amp;ndash;18.
Li, J., Zhu, X., Jia, R., Liu, B., &amp;amp; Yu, C. (2022). Apple-YOLO: A Novel Mobile Terminal Detector Based on YOLOv5 for Early Apple Leaf Diseases. Proceedings -2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022, 352&amp;ndash;361. https://doi.org/10.1109/COMPSAC54236.2022.00056
Mathew, M. P., &amp;amp; Mahesh, T. Y. (2022). Leaf-based disease detection in bell pepper plant using YOLO v5. Signal, Image and Video Processing, 16(3), 841&amp;ndash;847. https://doi.org/10.1007/s11760-021-02024-y
Olorunshola, O. E., Irhebhude, M. E., &amp;amp; Evwiekpaefe, A. E. (2023). A Comparative Study of YOLOv5 and YOLOv7 Object Detection Algorithms 1*. In Journal of Compu-ting and Social Informatics (Vol. 2, Issue 1).
Prashar, K., Talwar, R., &amp;amp; Kant, C. (2017). Robust automatic cotton crop disease recognition (ACDR) method using the hybrid feature descriptor with SVM. 4th 2016 Inter-national Conference on Computing on Sustainable Global Development, 1&amp;ndash;3.
Qian, Q., Yu, K., Yadav, P. K., Dhal, S., Kalafatis, S., Thomasson, J. A., &amp;amp; Hardin, R. G. (2022). Cotton crop disease detection on remotely collected aerial images with deep learning. 5. https://doi.org/10.1117/12.2623039
Rothe, P. R., &amp;amp; Kshirsagar, R. V. (2012). A Study on the Method of Image Preprocessing for Recognition of Crop Diseases. In International Journal of Computer Applica-tions. In IJCA Proceedings on International Conference on Benchmarks in Engineering Scienceand Technology 2012 ICBEST, no. 3, pp. 8-10.
Wang, H., Shang, S., Wang, D., He, X., Feng, K., &amp;amp; Zhu, H. (2022). Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model. Agriculture (Switzerland), 12(7). https://doi.org/10.3390/agriculture12070931
Welcome To Colaboratory -Colaboratory. (2022). Retrieved June 27, 2022, from https://colab.re-search.google.com/?utm_source=scs-index
Xu, R., Li, C., Paterson, A. H., Jiang, Y., Sun, S., &amp;amp; Robertson, J. S. (2018). Aerial Images and Convolutional Neural Net-work for Cotton Bloom Detection. Frontiers in Plant Sci-ence, 8. https://doi.org/10.3389/fpls.2017.02235
Xue, Z., Xu, R., Bai, D., &amp;amp; Lin, H. (2023). YOLO-Tea: A Tea Disease Detection Model Improved by YOLOv5. For-ests, 14(2). https://doi.org/10.3390/f14020415
Zekiwos, M., &amp;amp; Bruck, A. (2021). Deep learning-based image processing for cotton leaf disease and pest diagnosis. Journal of Electrical and Computer Engineering, 2021, 1&amp;ndash;10.
Zhu, R., Zou, H., Li, Z., &amp;amp; Ni, R. (2023). Apple-Net: A Model Based on Improved YOLOv5 to Detect the Apple Leaf Diseases. Plants, 12(1). https://doi.org/10.3390/plants12010169
</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
