Plant disease detection in precision agriculture must operate under significant computational constraints, particularly in low-resource (LR) environments such as unmanned aerial vehicles (UAVs) and edge devices. While LR models enable efficient real-time inference, they often lack the representational capacity required to accurately detect complex disease patterns. In contrast, high-resource (HR) models achieve superior detection performance due to their deep architectures but are computationally expensive and unsuitable for direct deployment in resource-constrained settings. This fundamental trade-off between efficiency and accuracy presents a major challenge for practical agricultural monitoring systems.
To address the distributed nature of agricultural data, federated learning (FL) is adopted to enable collaborative model training across multiple data sources without requiring centralized data aggregation. FL improves generalization and preserves data privacy; however, it does not resolve the inherent computational disparity between LR and HR models. Consequently, models trained under FL still face limitations when deployed in real-time LR environments.
To bridge this gap, this work proposes a hybrid adaptive inference framework that integrates LR and HR models within a unified system. The framework dynamically selects between efficient LR inference and more accurate HR processing based on prediction confidence, enabling effective utilization of computational resources while maintaining high detection performance. Furthermore, a Bayesian optimization strategy is employed to determine optimal decision thresholds, ensuring a balanced trade-off between accuracy, processing time, and energy consumption.
Experimental results demonstrate that the proposed framework significantly reduces computational cost while preserving high detection accuracy, enabling scalable and efficient plant disease detection in real-world UAV-based agricultural systems.
","abstract_html":"<p>Plant disease detection in precision agriculture must operate under significant computational constraints, particularly in low-resource (LR) environments such as unmanned aerial vehicles (UAVs) and edge devices. While LR models enable efficient real-time inference, they often lack the representational capacity required to accurately detect complex disease patterns. In contrast, high-resource (HR) models achieve superior detection performance due to their deep architectures but are computationally expensive and unsuitable for direct deployment in resource-constrained settings. This fundamental trade-off between efficiency and accuracy presents a major challenge for practical agricultural monitoring systems.</p> <p>To address the distributed nature of agricultural data, federated learning (FL) is adopted to enable collaborative model training across multiple data sources without requiring centralized data aggregation. FL improves generalization and preserves data privacy; however, it does not resolve the inherent computational disparity between LR and HR models. Consequently, models trained under FL still face limitations when deployed in real-time LR environments.</p> <p>To bridge this gap, this work proposes a hybrid adaptive inference framework that integrates LR and HR models within a unified system. The framework dynamically selects between efficient LR inference and more accurate HR processing based on prediction confidence, enabling effective utilization of computational resources while maintaining high detection performance. Furthermore, a Bayesian optimization strategy is employed to determine optimal decision thresholds, ensuring a balanced trade-off between accuracy, processing time, and energy consumption.</p> <p>Experimental results demonstrate that the proposed framework significantly reduces computational cost while preserving high detection accuracy, enabling scalable and efficient plant disease detection in real-world UAV-based agricultural systems.</p>","abstract_has_math":false,"creators":["Stokes, Ticauris S"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Masters Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Dr. Rabab Abdelfattah","Dr. Sarah Lee","Dr. Ahmed Sherif"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-05-01T07:00:00Z","date_published":"2026-05-01T07:00:00Z","updated_at":"2026-07-24T05:45:55Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://aquila.usm.edu/masters_theses/1185","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Rabab Abdelfattah","Dr. Sarah Lee","Dr. Ahmed Sherif"]},{"key":"dc:creator","label":"Author","values":["Stokes, Ticauris S"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2026-05-20T07:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://aquila.usm.edu/masters_theses/1185"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Plant disease detection in precision agriculture must operate under significant computational constraints, particularly in low-resource (LR) environments such as unmanned aerial vehicles (UAVs) and edge devices. While LR models enable efficient real-time inference, they often lack the representational capacity required to accurately detect complex disease patterns. In contrast, high-resource (HR) models achieve superior detection performance due to their deep architectures but are computationally expensive and unsuitable for direct deployment in resource-constrained settings. This fundamental trade-off between efficiency and accuracy presents a major challenge for practical agricultural monitoring systems.
To address the distributed nature of agricultural data, federated learning (FL) is adopted to enable collaborative model training across multiple data sources without requiring centralized data aggregation. FL improves generalization and preserves data privacy; however, it does not resolve the inherent computational disparity between LR and HR models. Consequently, models trained under FL still face limitations when deployed in real-time LR environments.
To bridge this gap, this work proposes a hybrid adaptive inference framework that integrates LR and HR models within a unified system. The framework dynamically selects between efficient LR inference and more accurate HR processing based on prediction confidence, enabling effective utilization of computational resources while maintaining high detection performance. Furthermore, a Bayesian optimization strategy is employed to determine optimal decision thresholds, ensuring a balanced trade-off between accuracy, processing time, and energy consumption.
Experimental results demonstrate that the proposed framework significantly reduces computational cost while preserving high detection accuracy, enabling scalable and efficient plant disease detection in real-world UAV-based agricultural systems.
"]},{"key":"dc:title","label":"Title","values":["A Unified Framework for Distributed and Resource-Aware Plant Disease Detection Using Federated and Adaptive Hybrid Deep Learning"]}]}],"canonical_facts":{"dc:contributor":["Dr. Rabab Abdelfattah","Dr. Sarah Lee","Dr. Ahmed Sherif"],"dc:creator":["Stokes, Ticauris S"],"dc:date.available":["2026-05-20T07:00:00Z"],"dc:description.abstract":["Plant disease detection in precision agriculture must operate under significant computational constraints, particularly in low-resource (LR) environments such as unmanned aerial vehicles (UAVs) and edge devices. While LR models enable efficient real-time inference, they often lack the representational capacity required to accurately detect complex disease patterns. In contrast, high-resource (HR) models achieve superior detection performance due to their deep architectures but are computationally expensive and unsuitable for direct deployment in resource-constrained settings. This fundamental trade-off between efficiency and accuracy presents a major challenge for practical agricultural monitoring systems.
To address the distributed nature of agricultural data, federated learning (FL) is adopted to enable collaborative model training across multiple data sources without requiring centralized data aggregation. FL improves generalization and preserves data privacy; however, it does not resolve the inherent computational disparity between LR and HR models. Consequently, models trained under FL still face limitations when deployed in real-time LR environments.
To bridge this gap, this work proposes a hybrid adaptive inference framework that integrates LR and HR models within a unified system. The framework dynamically selects between efficient LR inference and more accurate HR processing based on prediction confidence, enabling effective utilization of computational resources while maintaining high detection performance. Furthermore, a Bayesian optimization strategy is employed to determine optimal decision thresholds, ensuring a balanced trade-off between accuracy, processing time, and energy consumption.
Experimental results demonstrate that the proposed framework significantly reduces computational cost while preserving high detection accuracy, enabling scalable and efficient plant disease detection in real-world UAV-based agricultural systems.
"],"dc:identifier":["https://aquila.usm.edu/masters_theses/1185"],"dc:title":["A Unified Framework for Distributed and Resource-Aware Plant Disease Detection Using Federated and Adaptive Hybrid Deep Learning"],"thesis:degree_level":["Masters Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T05:45:55Z"}