Abstract: Agriculture and modern farming is one of the fields where IoT and automation can have a great impact. Maintaining healthy plants and monitoring their environment in order to identify or detect diseases is essential in order to maintain a maximum crop yield. The implementation of current high rocketing technologies including artificial intelligence (AI), machine learning, and deep learning has proved to be extremely important in modern agriculture as a method of advanced image analysis domain. Artificial intelligence adds time efficiency and the possibility of identifying plant diseases, in addition to monitoring and controlling the environmental conditions in farms. Several studies showed that machine learning and deep learning technologies can detect plant diseases upon analyzing plant leaves with great accuracy and sensitivity. In this study, considering the worth of machine learning for disease detection, we present a convolutional neural network VGG-16 model to detect plant diseases, to allow farmers to make timely actions with respect to treatment without further delay. To carry this out, 19 different classes of plants diseases were chosen, where 15,915 plant leaf images (both diseased and healthy leaves) were acquired from the Plant Village dataset for training and testing. Based on the experimental results, the proposed model is able to achieve an accuracy of about 95.2% with the testing loss being only 0.4418. The proposed model provides a clear direction toward a deep learning-based plant disease detection to apply on a large scale in future.

Anwar Abdullah Alatawi, Shahd Maadi Alomani, Najd Ibrahim Alhawiti and Muhammad Ayaz, “Plant Disease Detection using AI based VGG-16 Model” International Journal of Advanced Computer Science and Applications(IJACSA), 13(4), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130484

@article{Alatawi2022,
title = {Plant Disease Detection using AI based VGG-16 Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130484},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130484},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {4},
author = {Anwar Abdullah Alatawi and Shahd Maadi Alomani and Najd Ibrahim Alhawiti and Muhammad Ayaz}
}