Investigating attention mechanisms for plant disease identification in challenging environments
Sangeeta Duhan,
Preeti Gulia,
Nasib Singh Gill,
Piyush Kumar Shukla,
Surbhi Bhatia Khan,
Ahlam Almusharraf,
Norah Alkhaldi
Affiliations
Sangeeta Duhan
Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, 124001, Haryana, India
Preeti Gulia
Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, 124001, Haryana, India
Nasib Singh Gill
Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, 124001, Haryana, India
Piyush Kumar Shukla
Computer Science & Engineering Department, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya (Technological University of Madhya Pradesh), Bhopal, Madhya Pradesh, India
Surbhi Bhatia Khan
Department of Data Science, School of Science, Engineering and Environment, University of Salford, UK; Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon; Corresponding author. Computer Science & Engineering Department, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya (Technological University of Madhya Pradesh), Bhopal, Madhya Pradesh, India.
Ahlam Almusharraf
Department of Business Administration, College of Business and Administration, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia; Corresponding author. Department of Business Administration, College of Business and Administration, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabi.
Norah Alkhaldi
Department of Computer Science, School of Computer Science and Information Technology, King Faisal University, Saudi Arabia
There is an increasing demand for efficient and precise plant disease detection methods that can quickly identify disease outbreaks. For this, researchers have developed various machine learning and image processing techniques. However, real-field images present challenges due to complex backgrounds, similarities between different disease symptoms, and the need to detect multiple diseases simultaneously. These obstacles hinder the development of a reliable classification model. The attention mechanisms emerge as a critical factor in enhancing the robustness of classification models by selectively focusing on relevant regions or features within infected regions in an image. This paper provides details about various types of attention mechanisms and explores the utilization of these techniques for the machine learning solutions created by researchers for image segmentation, feature extraction, object detection, and classification for efficient plant disease identification. Experiments are conducted on three models: MobileNetV2, EfficientNetV2, and ShuffleNetV2, to assess the effectiveness of attention modules. For this, Squeeze and Excitation layers, the Convolutional Block Attention Module, and transformer modules have been integrated into these models, and their performance has been evaluated using different metrics. The outcomes show that adding attention modules enhances the original models' functionality.
