Purdue University Graduate School
Aanis_Ahmad_PhD_Thesis_FINAL.pdf (13.89 MB)


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posted on 2023-12-14, 16:51 authored by Aanis AhmadAanis Ahmad

Efficient management of plant diseases and their spread within fields requires a system capable of early and accurate disease identification and its severity estimation. Many plant diseases have distinct visual symptoms, which can be used to correctly identify, classify, and manage them. Recent technological advancements have led to increased adoption of deep neural networks (DNN) for developing deep learning (DL)-based computer vision systems. An accurate disease identification and severity estimation system using a DL-based computer vision framework is critical for efficiently managing corn diseases under field conditions and further restricting the spread of disease. Image processing and machine learning methods for disease identification and classification have been employed in the last two decades using high-cost sensors that need frequent calibration. Researchers have used low-cost red, green, and blue (RGB) sensors to mostly identify single diseases affecting crops, whereas, in real-world applications, a single leaf can be affected by multiple diseases. This research identifies gaps in knowledge of DL applications to field crops by reviewing 70 research articles published between 1983 and 2022. It creates a much-needed disease database for corn grown under field conditions by adding custom-acquired image data to other publicly available image repositories. The image data was used to train and evaluate the performance of commonly used DL-based image classification models for differentiating single diseases on individual corn leaves under field conditions. However, many disease lesions of different shapes and sizes can simultaneously develop on infected leaves. The performance of DL-based image classification and object detection models was evaluated to accurately identify multiple simultaneous diseases with varying symptoms. Disease identification under field conditions is necessary to implement an effective disease management system. However, recent work has demonstrated poor generalization accuracies of DL models trained on lab-acquired imagery for identifying diseases in the field. Therefore, after achieving promising results for disease identification, DL generalization performance was assessed and improved using different dataset combinations with varying backgrounds. A novel neural network architecture using a hierarchical structure was also proposed, which resulted in improved generalization performance. Additionally, disease severity must be estimated to implement an effective management response. DL models were evaluated to estimate the severity of multiple corn diseases under field conditions using aerial and ground-based platforms to identify specific lesions from above and below the canopy. A progressive web application was designed to empower end users with disease recognition capabilities. Overall, this research reports findings of the performance of deep learning image processing, object detection, and segmentation models for identifying single/multiple diseases on field corn and the development of tools that can potentially be a component of production-ready disease diagnosis systems for implementing effective management practices.


Wabash Heartland Innovation Network (WHIN) grant number 18024589

Higher Education Challenge Grant from the United States Department of Agriculture (USDA) – National Institute of Food and Agriculture (NIFA) grant number 17000716

Department of Agricultural and Biological Engineering at Purdue University


Degree Type

  • Doctor of Philosophy


  • Electrical and Computer Engineering

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Dharmendra Saraswat

Advisor/Supervisor/Committee co-chair

Ali El Gamal

Additional Committee Member 2

Gurmukh S. Johal

Additional Committee Member 3

James V. Krogmeier