Automated Cotton Leaf Identification Using Feature Selection Techniques

Authors

  • 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

Keywords:

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

Abstract

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%.

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References

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Published

30.08.2023

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 https://ijisae.org/index.php/IJISAE/article/view/3506

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Research Article

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