PalmRachis-BiLSTM-Attn: An Anatomically Guided Explainable Deep Learning Framework for Spatial–Temporal Progression Modeling of Date-Palm Leaf Diseases

Early and interpretable detection of foliar diseases in date palms is essential for sustainable agriculture in arid regions, yet conventional deep-learning models treat symptoms as static visual patterns without considering their biological progression along the rachis. This study introduces PalmRachis-BiLSTM-Attn, a bio-inspired and explainable deep-learning framework that models palm-leaf pathology as a spatial–temporal progression process guided by the anatomical ordering of the rachis. The method begins with a CNN backbone that extracts textural cues from leaf images, which are then reorganized into rachis-aligned sequences and processed through a bidirectional LSTM with temporal attention to capture both forward (disease spread) and backward (regression) vascular dynamics. An integrated explainability module combines Grad-CAM spatial maps with temporal-attention timelines, generating biologically meaningful “where–when” visualizations of disease development. Evaluated on an eight-class date-palm leaf dataset, the framework achieves 98.1% classification accuracy and outperforms static CNNs and non-anatomical sequence baselines across all metrics (precision = 0.988, recall = 0.984, F $1=0.984$ ). By aligning model behavior with real infection pathways, PalmRachis-BiLSTM-Attn advances progression-aware, transparent, and biologically grounded disease monitoring, offering a deployment-oriented foundation for IoT-enabled crop-health systems and future smart-agriculture.

• Publication year

  2026

• Venue

  IEEE Access

• Publication date

  Unknown publication date

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/ACCESS.2026.3669421
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture

  2025cited by this paper
• CBAM Integrated Attention Driven Model For Betel Leaf Diseases Classification With Explainable AI

  2025cited by this paper
• Multipeeling of Homogeneous Stationarity and Heterogeneous Nonstationarity With Differentiated Learning for Process Monitoring

  2025influential reference
• Enhanced plant disease classification with attention-based convolutional neural network using squeeze and excitation mechanism

  2025influential reference
• Leaf disease detection and classification in food crops with efficient feature dimensionality reduction

  2025cited by this paper
• A Deep Learning Approach to Classification of Diseases in Date Palm Leaves

  2025influential reference
• A systematic review of explainable artificial intelligence for spectroscopic agricultural quality assessment

  2025cited by this paper
• Prediction of sugar beet yield and quality parameters using stacked-LSTM model with pre-harvest UAV time series data and meteorological factors

  2025cited by this paper
• Plant leaf disease detection and classification using artificial intelligence techniques: a review

  2025cited by this paper
• An optimal model for identification and classification of corn leaf disease using hybrid 3D-CNN and LSTM

  2024cited by this paper
• Palm Leaf Health Management: A Hybrid Approach for Automated Disease Detection and Therapy Enhancement

  2024cited by this paper
• DPXception: a lightweight CNN for image-based date palm species classification

  2024cited by this paper
• xLSTM: Extended Long Short-Term Memory

  2024cited by this paper
• You Only Need Less Attention at Each Stage in Vision Transformers

  2024influential reference
• Improving Plant Disease Classification With Deep-Learning-Based Prediction Model Using Explainable Artificial Intelligence

  2024cited by this paper
• EfficientNet-Based Approach for Enhanced Sugarcane Disease Classification: Implementation and Evaluation

  2024cited by this paper
• TempSAL - Uncovering Temporal Information for Deep Saliency Prediction

  2023influential reference
• FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision

  2023influential reference
• Channel Attention-Guided Lightweight CNN Meets Extreme Learning Machine: Multi-Class Crop Leaf Disease Classification with Explainable AI

  2023cited by this paper
• Explainable AI for Tomato Leaf Disease Detection: Insights into Model Interpretability

  2023cited by this paper
• A new hybrid algorithm for intelligent detection of sudden decline syndrome of date palm disease

  2023cited by this paper
• A hybrid CNN-LSTM model for diagnosing rice nutrient levels at the rice panicle initiation stage

  2023cited by this paper
• Palm tree disease detection and classification using residual network and transfer learning of inception ResNet

  2023influential reference
• Classification of Palm Trees Diseases using Convolution Neural Network

  2022cited by this paper
• A multi-objective Gray Wolf algorithm for routing in IoT Collection Networks with real experiments

  2021cited by this paper
• Moving Vehicle Candidate Recognition and Classification Using Inception-ResNet-v2

  2020cited by this paper
• Application of thermal imaging for plant disease detection

  2020cited by this paper
• A comparative analysis with validation of NSGA-III and MOEA/D in resolving the 3D indoor redeployment problem in DL-IoT

  2017cited by this paper
• Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

  2016influential reference
• Deep Residual Learning for Image Recognition

  2015cited by this paper
• Neural Machine Translation by Jointly Learning to Align and Translate

  2014cited by this paper
• Framewise phoneme classification with bidirectional LSTM networks

  2005influential reference
• Long Short-Term Memory

  1997influential reference
• : Enhancing

  year unknowncited by this paper

• No citing papers are available for this paper.