TY  - BOOK
AU  - Doula,Md Shafi Ud
AU  - Chutiporn Anutariya,
AU  - Mongkol Ekpanyapong,
AU  - Cherdsak Kingkan,
ED  - AIT Scholarship,
TI  - A multi-modal framework for context-aware plant disease classification and segmentation integrating visual and textual features
T2  - Thesis
PY  - 2025///
CY  - Pathum Thani, Thailand
PB  - Asian Institute of Technology
KW  - Plant pathology
KW  - Data processing
KW  - Natural language processing (Computer science)
KW  - Agriculture
N1  - A thesis submitted in partial fulfillment of the requirements for the  degree of Master of Engineering in Data Science and Artificial Intelligence, School of Engineering and Technology; Thesis (M. Eng.) - Asian Institute of Technology, 2025
N2  - Plant diseases substantially challenge agricultural productivity and global food security.  Hence, better intelligent and interpretable diagnostic frameworks are needed. An auto mated disease identification system can reduce the human effort in checking large farms,  and early detection and identification will minimize the loss, which ultimately positively  affects the economy. Traditional image-based deep learning models, particularly Convo lutional Neural Networks (CNNs), often struggle to distinguish visually similar diseases  due to the absence of contextual information. To address these limitations, we present  an innovative multi-modal deep learning framework that effectively combines visual  and textual data to improve plant disease classification and segmentation. Initially, the  framework incorporates a linguistically enriched Text Encoder, where disease-related  descriptions are preprocessed using natural language processing (NLP) techniques to extract salient noun, numerical, adjective, and adverbial features. These refined textual  representations are then encoded using a fine-tuned transformer-based language model,  capturing domain-specific semantics crucial for disease differentiation. Concurrently,  CNN-based Vision Encoder extract discriminative hierarchical features, which are dy namically fused with textual representations via a multi-head attention mechanism, en suring adaptive cross-modal feature alignment. Unlike conventional fusion techniques,  our approach learns complex inter-dependencies between textual cues and visual pat terns, enhancing classification accuracy and segmentation precision. Finally, we demon strate our proposed framework{u2019}seffectiveness byevaluating it on the Plant Disease Diag nosis Multimodal Dataset (PDDM) and achieving state-of-the-art (SOTA) segmentation and classification performance
UR  - http://203.159.5.9/ait-thesis/detail.php?q=B23568
ER  -