Deep Learning Based-Cotton Disease Recognition System

Muhammad Bilal; Yash kumar; Akhtar Hussain Jalbani; Subhan khan Solangi

Authors

Muhammad Bilal Department of Computer Science Quaid-e-Awam University of Engineering Science and Technology, Nawabshah
Yash kumar Department of Computer Science Quaid-e-Awam University of Engineering Science and Technology, Nawabshah
Akhtar Hussain Jalbani Department of Computer Science Quaid-e-Awam University of Engineering Science and Technology, Nawabshah
Subhan khan Solangi Department of Computer Science Quaid-e-Awam University of Engineering Science and Technology, Nawabshah

Keywords:

Deep Learning, CNN, cotton disease detection, Image Recognition, Agricultural Technology, Pattern Recognition

Abstract

Cotton is a vital cash crop in Sindh, Pakistan, playing a crucial role in the agricultural economy. However, diseases such as Cotton Leaf Curl Virus (CLCV), bacterial blight, and Fusariumwilt significantly reduce cotton yield, affecting farmers' livelihoods. Traditional disease identification methods are labor-intensive, error-prone, and inefficient, necessitating automated approaches for early and accurate detection. This research introduces a deep learning-based cotton disease recognition system, leveraging Convolutional Neural Networks (CNNs) with transfer learning to classify diseases. Experimental results demonstrate that our approach achieves high accuracy, offering an efficient, user-friendly, and scalable solution to promote sustainable agricultural practices in Pakistan.

References

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