Received 9 February 2023, accepted 28 February 2023, date of publication 8 March 2023, date of current version 14 March 2023. Digital Object Identifier 10.1109/ACCESS.2023.3254430 Embedded AI for Wheat Yellow Rust Infection Type Classification UFERAH SHAFI 1 , RAFIA MUMTAZ 1 , (Senior Member, IEEE), MUHAMMAD DEEDAHWAR MAZHAR QURESHI 1,3 , ZAHID MAHMOOD 2 , SIKANDER KHAN TANVEER 2 , IHSAN UL HAQ 1 , AND SYED MOHAMMAD HASSAN ZAIDI 1 1 School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan 2 Crop Sciences Institute, National Agricultural Research Centre, Islamabad 44000, Pakistan 3 Graduate Business School, Technological University Dublin, Dublin, D02 HW71 Ireland Corresponding author: Rafia Mumtaz (rafia.mumtaz@seecs.edu.pk) This research is funded by National Center for Artificial Intelligence (NCAI), National University of Sciences and Technology (NUST), Islamabad, Pakistan. ABSTRACT Wheat is the most important and dominating crop in Pakistan in terms of production and acreage, which is grown on 37% of the cultivated area, accounting for 70% of the total production. However, wheat yield is highly affected by stripe rust, which is considered the most devastating fungal disease, causing 5.5 million tonnes of loss per year globally. In order to minimize this loss, the accurate and timely detection of rust disease is crucial instead of manual inspection. Towards this end, we propose a system to detect wheat rust disease and classify its infection types into four classes, including healthy, resistant, moderate (moderately resistant to moderately susceptible), and susceptible. The wheat rust dataset is collected indigenously from the National Agricultural Research Centre, Islamabad. A pre-trained U 2 Net model is used to remove the background and extract the leaf containing the rust disease. Subsequently, two deep learning classifiers, including the Xception model and ResNet-50 are applied to classify the stripe rust severity levels, where the ResNet-50 model outperformed with the highest accuracy of 96%. This research presents a comparison between two state-of-the-art deep learning classifiers in terms of accuracy, memory utilization, and prediction time, which will assist the research community in selecting the most appropriate model for plant disease detection. Moreover, to assess the external validity, the performance of these classifiers is compared with the existing technique using a publicly available dataset, which confirms the validity of the results. Additionally, an intelligent edge computing rust detection device has been developed, where the trained ResNet-50 model is deployed, which facilitates the farmers to monitor the rust attack. The proposed research is aimed to assist the agricultural community to employ preventive measures in a site-specific manner based on the accurate diagnosis of rust disease & its severity, which is intended to improve the quality of the wheat as well as production. INDEX TERMS Deep learning, classification, wheat stripe rust disease, segmentation, rust infection types, edge device. I. INTRODUCTION Wheat is the prime cereal crop that holds paramount impor- tance worldwide due to its immense contribution to the human diet. It is the third most harvested crop in the world and contains carbohydrates, dietary fiber, protein, and vitamins. The associate editor coordinating the review of this manuscript and approving it for publication was Wai-Keung Fung . Its average annual production is around 680 million tonnes per year globally [1]. In Pakistan, wheat is considered the most significant staple crop, accounting for 37.1% of the cultivated area and 65% of the food grain acreage. It is culti- vated by 80% farmers and covers almost 9 million hectares of agricultural land. Its favorable temperature is 21 ◦ C to 24 ◦ C, where it requires a moist and cool temperature in the early stages and a warm temperature in the mature stages with substantial sunshine. 23726 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ VOLUME 11, 2023