Masters Theses
Abstract
Accurate and timely prediction of pest outbreaks is a cornerstone of Agriculture 5.0, which emphasizes intelligent, data-driven, and sustainable decision-making in crop production. This research presents a multimodal deep learning framework that integrates heterogeneous data sources, including weather parameters, satellite-derived vegetation indices, and static and dynamic soil attributes, to forecast pest population dynamics under varying management and ecological conditions. The proposed framework employs modality-specific deep encoders to capture distinct temporal and spatial representations from each data stream and merges them through a late-fusion architecture that learns cross-modal dependencies critical to pest emergence. The design further incorporates treatment-aware and multiclass extensions, allowing the model to differentiate pest dynamics across management regimes such as Conventional Management and Integrated Pest Management (IPM), while simultaneously forecasting multiple pest categories within a unified framework. This multimodal approach enhances interpretability, scalability, and ecological relevance by linking environmental, spectral, and agronomic factors to observed pest patterns. The results demonstrate the framework’s potential to support precision pest forecasting and sustainable decision-making across diverse cropping systems, establishing a foundation for real-time, adaptive pest management aligned with the principles of Agriculture 5.0.
Advisor(s)
Das, Sajal K.
Committee Member(s)
Arifuzzaman, Md
Morales, Ricardo
Department(s)
Computer Science
Degree Name
M.S. in Computer Science
Publisher
Missouri University of Science and Technology
Publication Date
Fall 2025
Pagination
x, 76 pages
Note about bibliography
Includes_bibliographical_references_(pages 70-75)
Rights
© 2026 V N S Kameswari Sri Sindhu Manchikanti , All Rights Reserved
Document Type
Thesis - Open Access
File Type
text
Language
English
Thesis Number
T 12571
Recommended Citation
Manchikanti, V N S Kameswari Sri Sindhu, "Multimodal Spatio-Temporal Pest Prediction in Precision Agriculture" (2025). Masters Theses. 8271.
https://scholarsmine.mst.edu/masters_theses/8271
