Automated Cotton Leaf Identification Using Feature Selection Techniques


  • M. Rudra Kumar Professor, Department of Computer Science and Engineering, GPCET
  • Anitha P Assistant Professor, Department of ISE, Ramaiah Institute of Technology, Bengaluru
  • Ashwitha A Assistant Professor (Senior Scale), Information Technology department, Manipal Academy of Higher Education (MAHE), Bangalore
  • P. Venkateswarlu Reddy Assistant Professor, Dept. of CSE, School of Computing, Mohan Babu University, (Erstwhile Sree Vidyanikethan Engineering College (Autonomous), Tirupati, Andhra Pradesh, India
  • H. Manoj T. Gadiyar Associate Professor, Department of Computer Science and Engineering, Sri Dharmasthala Manjunatheshwara Institute of Technology, Ujire, Karnataka, India


Cotton Disease, Improved Grey Wolf Optimization, Agriculture, Indian Economy, Farmers, Bacterial blight


Agriculture is the backbone of any prosperous nation. Pest infestations and bacterial or viral illnesses cause significant economic losses in the cotton farming commercial, costing Indian farmers an average of 10-20% of their annual income. Cash crops include cotton and other valuable agricultural products. Cotton is highly susceptible to the vast majority of crop-damaging diseases. Several diseases affect crop production by attacking the leaves. The early diagnosis of diseases helps prevent additional damage to crops. Many diseases can afflict cotton, including leaf spot, nutrient insufficiency, powdery mildew, leaf curl, and many others. Correctly diagnosing a condition is crucial for taking appropriate action. Accurately diagnosing plant diseases requires. The suggested model based on the Biattention process makes accurate diagnosis of cotton leaf diseases possible. Also, useless features lower categorization precision. These issues are tackled by the IGWO (Improved Grey Wolf Optimization) method. We photographed cotton leaves in the field for our analysis. There are 2385 images in the dataset, including both leaves. The dataset was expanded with the use of data increase techniques. A meta-learning strategy has been devised and applied to deliver high precision and generalization. The projected model has a higher accuracy on the Cotton Dataset, at 97.45%.


Download data is not yet available.


Tripathy, S., 2021, November. Detection of cotton leaf disease using image processing techniques. In Journal of Physics: Conference Series (Vol. 2062, No. 1, p. 012009). IOP Publishing.

Kumar, S., Jain, A., Shukla, A.P., Singh, S., Raja, R., Rani, S., Harshitha, G., AlZain, M.A. and Masud, M., 2021. A comparative analysis of machine learning algorithms for detecting organic and nonorganic cotton diseases. Mathematical Problems in Engineering, 2021, pp.1-18.

Ramana, Kadiyala, Rajanikanth Aluvala, Madapuri Rudra Kumar, G. Nagaraja, Akula Vijaya Krishna, and Pidugu Nagendra. "Leaf Disease Classification in Smart Agriculture using Deep Neural Network Architecture and IoT." Journal of Circuits, Systems and Computers (2022).

Appalanaidu, M.V. and Kumaravelan, G., 2021. Plant leaf disease detection and classification using machine learning approaches: a review. Innovations in Computer Science and Engineering: Proceedings of 8th ICICSE, pp.515-525.

S. Prasath Alais Surendhar, Govindaraj Ramkumar, Ram Prasad, Piyush Kumar Pareek, R. Subbiah, Abdullah A. Alarfaj, Abdurahman Hajinur Hirad, S. S. Priya, Raja Raju, "Prediction of Escherichia coli Bacterial and Coliforms on Plants through Artificial Neural Network", Advances in Materials Science and Engineering, vol. 2022, Article ID 9793790, 13 pages, 2022.

Patil, B.V. and Patil, P.S., 2021. Computational method for Cotton Plant disease detection of crop management using deep learning and internet of things platforms. In Evolutionary Computing and Mobile Sustainable Networks: Proceedings of ICECMSN 2020 (pp. 875-885). Springer Singapore.

Mani, R. G., Parthasarathy, R., Sivaraman, E., Honnavalli, P. (2022). A Survey on Digital Image Forensics: Metadata and Image Forgeries. Workshop on Applied Computing 2022, Chennai, CEUR Workshop Proceedings, pp. 22-55,

Vasavi, P., Punitha, A. and Rao, T.V.N., 2022. Crop leaf disease detection and classification using machine learning and deep learning algorithms by visual symptoms: A review. International Journal of Electrical and Computer Engineering, 12(2), p.2079.

Zekiwos, M. and Bruck, A., 2021. Deep learning-based image processing for cotton leaf disease and pest diagnosis. Journal of Electrical and Computer Engineering, 2021, pp.1-10.

Maheswari, V. U., Aluvalu, R., & Mudrakola, S. (2022, March). An Integrated Number Plate Recognition System through images using Threshold-based methods and KNN. In 2022 International Conference on Decision Aid Sciences and Applications (DASA) (pp. 493-497). IEEE.

Karthika, J., Santhose, M. and Sharan, T., 2021, May. Disease detection in cotton leaf spot using image processing. In Journal of Physics: Conference Series (Vol. 1916, No. 1, p. 012224). IOP Publishing.

Kumar, S., Ratan, R. and Desai, J.V., 2022. A study of iOS machine learning and artificial intelligence frameworks and libraries for cotton plant disease detection. In Machine Learning, Advances in Computing, Renewable Energy and Communication: Proceedings of MARC 2020 (pp. 259-270). Springer Singapore.

Caldeira, R.F., Santiago, W.E. and Teruel, B., 2021. Identification of cotton leaf lesions using deep learning techniques. Sensors, 21(9), p.3169.

Moyazzoma, R., Hossain, M.A.A., Anuz, M.H. and Sattar, A., 2021, January. Transfer learning approach for plant leaf disease detection using CNN with pre-trained feature extraction method Mobilnetv2. In 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) (pp. 526-529). IEEE.

Saberi Anari, M., 2022. A hybrid model for leaf diseases classification based on the modified deep transfer learning and ensemble approach for agricultural aiot-based monitoring. Computational Intelligence and Neuroscience, 2022.

Rai, C.K. and Pahuja, R., 2023. Classification of Diseased Cotton Leaves and Plants Using Improved Deep Convolutional Neural Network. Multimedia Tools and Applications, pp.1-19.

Naeem, A.B., Senapati, B., Chauhan, A.S., Kumar, S., Gavilan, J.C.O. and Abdel-Rehim, W.M., 2023. Deep Learning Models for Cotton Leaf Disease Detection with VGG-16. International Journal of Intelligent Systems and Applications in Engineering, 11(2), pp.550-556.

Zhou, X., Yang, M., Chen, X., Ma, L., Yin, C., Qin, S., Wang, L., Lv, X. and Zhang, Z., 2023. Estimation of Cotton Nitrogen Content Based on Multi-Angle Hyperspectral Data and Machine Learning Models. Remote Sensing, 15(4), p.955.

Huang C, Zhang Z, Zhang X, Jiang L, Hua X, Ye J, Yang W, Song P, Zhu L. A Novel Intelligent System for Dynamic Observation of Cotton Verticillium Wilt. Plant Phenomics. 2023 Jan 10;5:0013.

Amin, J., Anjum, M.A., Sharif, M., Kadry, S. and Kim, J., 2022. Explainable Neural Network for Classification of Cotton Leaf Diseases. Agriculture, 12(12), p.2029.

Zhang, Y., Ma, B., Hu, Y., Li, C. and Li, Y., 2022. Accurate cotton diseases and pests detection in complex background based on an improved YOLOX model. Computers and Electronics in Agriculture, 203, p.107484.

Memon, M.S., Kumar, P. and Iqbal, R., 2022. Meta Deep Learn Leaf Disease Identification Model for Cotton Crop. Computers, 11(7), p.102.

V. Kishen Ajay Kumar, M. Rudra Kumar, N. Shribala, Ninni Singh, Vinit Kumar Gunjan, Kazy Noor-e-alam Siddiquee, Muhammad Arif, "Dynamic Wavelength Scheduling by Multiobjectives in OBS Networks", Journal of Mathematics, vol. 2022, Article ID 3806018, 10 pages, 2022.

Rudra Kumar, M., Gunjan, V.K. (2022). Peer Level Credit Rating: An Extended Plugin for Credit Scoring Framework. In: Kumar, A., Mozar, S. (eds) ICCCE 2021. Lecture Notes in Electrical Engineering, vol 828. Springer, Singapore.

Mirjalili, S., Mirjalili, S.M. and Lewis, A., 2014. Grey wolf optimizer. Advances in engineering software, 69, pp.46-61.

Dwaram, Jayanarayana Reddy, and Rudra Kumar Madapuri. "Crop yield forecasting by long short‐term memory network with Adam optimizer and Huber loss function in Andhra Pradesh, India." Concurrency and Computation: Practice and Experience 34.27 (2022): e7310

Ramana, Kadiyala, et al. "Leaf disease classification in smart agriculture using deep neural network architecture and IoT." Journal of Circuits, Systems and Computers 31.15 (2022): 2240004.

Niu Z, Yu Z, Tang W, Wu Q, Reformat M. Wind power forecasting using attention-based gated recurrent unit network. Energy 2020;196:117081.

Rudra Kumar, M., Gunjan, V.K. (2022). Machine Learning Based Solutions for Human Resource Systems Management. In: Kumar, A., Mozar, S. (eds) ICCCE 2021. Lecture Notes in Electrical Engineering, vol 828. Springer, Singapore.




How to Cite

Kumar, M. R. ., P, A. ., A, A. ., Reddy, P. V. ., & Gadiyar, H. M. T. . (2023). Automated Cotton Leaf Identification Using Feature Selection Techniques. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 410–415. Retrieved from



Research Article

Most read articles by the same author(s)