A Deep Learning Based Mobile Application for Wheat Disease Diagnosis

Sarmad Riaz; Raja Taimour; Mashab Ali Javed; Amaad Khalil; Yasir Saleem Afridi; Abid Iqbal

Authors

Sarmad Riaz Department of CS & IT, University of Engineering & Technology, Peshawar 25000, Pakistan
Raja Taimour Department of CS & IT, University of Engineering & Technology, Peshawar 25000, Pakistan
Mashab Ali Javed Department of Computer Systems Engineering, Sir Syed CASE Institute of Technology Islamabad Pakistan
Amaad Khalil Department of Computer Systems Engineering, University of Engineering & Technology, Peshawar 25000, Pakistan
Yasir Saleem Afridi Department of Computer Systems Engineering, University of Engineering & Technology, Peshawar 25000, Pakistan
Abid Iqbal Department of Electrical Engineering Jalozai Campus, University of Engineering & Technology, Peshawar 25000, Pakistan

Keywords:

Wheat diseases, Convolutional Neural Networks (CNNs), Transfer Learning, Tensor Flow.

Abstract

Wheat is one of the major staple crops in Pakistan, playing a crucial role in ensuring food security and contributing to the country's economy. The productivity and quality of wheat crops, however, are vulnerable to several illnesses. The ability to diagnose these diseases quickly and accurately is crucial for taking the appropriate preventative actions, limiting losses, and maintaining food security. In this research paper, we build and test a wheat disease detection system adapted to the conditions in Pakistan. The suggested method uses machine learning-based techniques along with image processing algorithms to automatically detect and categorize various wheat diseases based on their symptoms. High-resolution photos of healthy wheat plants and sick plants displaying different diseases were collected from different regions of Pakistan in order to construct an accurate and robust disease detection model. The dataset has been annotated by plant pathologists who provided true labels for use in evaluation and training. To achieve the best results in wheat disease diagnosis, many cutting-edge deep-learning architectures were investigated and optimized. These included Convolutional Neural Networks (CNNs) and Transfer Learning models. Multiple models’ effectiveness was evaluated using accuracy, precision, and recall, in a series of extensive trials.

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