Academy & Industry Research Collaboration Center (AIRCC)

Volume 11, Number 24, December 2021

MajraDoc an Image based Disease Detection App for Agricultural Plants using Deep Learning Techniques


Sara Saleh Alfozan and Mohamad Mahdi Hassan, Qassim University, Saudi Arabia


Infection of agricultural plants is a serious threat to food safety. It can severely damage plants' yielding capacity. Farmers are the primary victims of this threat. Due to the advancement of AI, image-based intelligent apps can play a vital role in mitigating this threat by quick and early detection of plants infections. In this paper, we present a mobile app in this regard. We have developed MajraDoc to detect some common diseases in local agricultural plants. We have created a dataset of 10886 images for ten classes of plants diseases to train the deep neural network. The VGG-19 network model was modified and trained using transfer learning techniques. The model achieved high accuracy, and the application performed well in predicting all ten classes of infections.


Plant diseases, plant diseases diagnosis, deep learning, VGG19 CNN, mobile application.