000			03552nam a2200373 a 4500
005			20260817161322.0
008			260218s20259999th mm 000 eng d
035			_a.b12476699
099		9	_aAIT Thesis no.DSAI-25-05
100	1		_aDoula, Md Shafi Ud
245	1	0	_aA multi-modal framework for context-aware plant disease classification and segmentation integrating visual and textual features
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2025
300			_a74 leaves : _bill.+ _e1 online resource
490	1		_aThesis ; _vno. DSAI-25-05
500			_aA 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
502			_aThesis (M. Eng.) - Asian Institute of Technology, 2025
520			_aPlant 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.
650		0	_aPlant pathology _xData processing
650		0	_aNatural language processing (Computer science)
650		0	_aAgriculture
700	0		_aChutiporn Anutariya, _eChairperson
700	0		_aMongkol Ekpanyapong, _eExamination Committee
700	0		_aCherdsak Kingkan, _eExamination Committee
710	2		_aAIT Scholarship, _eScholarship Donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. DSAI-25-05
856	4	0	_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B23568
907			_a.b12476699 _bmnarc _ca
902			_a260309
998			_b0 _c260218 _dm _eh _fa _g0
945			_lmnarc
942			_c67
909			_aBarcode : - _bCREATED : 2026-02-18 _cRECORD # : i13573652 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
999			_c835 _d835