Abstract: Agriculture on earth is the biggest need for human sustenance. Over years, many farming methods and components have become computerized to guarantee quicker production with higher quality. Because of the enlarged demand in the farming industry, agricultural produce must be cultivated using an efficient process. Onion (Allium cepa L.) is an economically valuable crop and is the second-largest vegetable crop in the world. The spread of various diseases highly affected the production of the onion crop. One of the serious and most common diseases of onion worldwide is purple blotch. To compensate for a limited amount of training dataset of healthy and infected onion crops, the proposed method employs a pre-trained enhanced InceptionV3 model. The proposed model detects onion disease (purple blotch) from images by recognizing the abnormalities caused by the disease. The suggested approach achieves a classification accuracy of 85.47% in recognizing the disease. This research investigates a novel approach for the rapid and accurate diagnosis of plant/crop diseases, laying the theoretical foundation for the use of deep learning in agricultural information.

Muhammad Ahmed Zaki, Sanam Narejo, Muhammad Ahsan, Sammer Zai, Muhammad Rizwan Anjum and Naseer u Din, “Image-based Onion Disease (Purple Blotch) Detection using Deep Convolutional Neural Network” International Journal of Advanced Computer Science and Applications(IJACSA), 12(5), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120556

@article{Zaki2021,
title = {Image-based Onion Disease (Purple Blotch) Detection using Deep Convolutional Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120556},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120556},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {5},
author = {Muhammad Ahmed Zaki and Sanam Narejo and Muhammad Ahsan and Sammer Zai and Muhammad Rizwan Anjum and Naseer u Din}
}