1Department of Computer Science and Engineering, P.A. College of Engineering, Mangaluru, Affiliated to Visvesvaraya Technological University, Belgaum, India
2Department of Artificial Intelligence and Machine Learning, School of Engineering, St Aloysius (Deemed to be University), Mangalore, India
Accurate and timely detection of plant diseases is a critical component of modern agricultural biosecurity, directly impacting crop yield, food safety, and the health of farming communities. Plant diseases cause annual agricultural losses that exceed $220 billion worldwide, yet accurately quantifying how severely a plant is infected remains one of the least-addressed problems in automated crop monitoring. This paper presents ParaLeafNet-Severity, a deep parallel convolutional neural network designed to perform disease identification and four-level severity grading simultaneously within a single forward pass. The architecture draws complementary feature representations from two lightweight backbones — MobileNetV2 and MobileNetV3Small — running in parallel, fuses their outputs through channel-wise Squeeze-and-Excitation (SE) attention, and routes the resulting shared embedding to two task-specific output heads. An optional K-Means clustering branch operates on the shared feature space to discover natural severity groupings without requiring additional expert labels. Experimental evaluation on the adapted PlantVillage dataset demonstrates that the proposed framework achieves strong performance in disease identification and severity classification, outperforming existing baseline approaches while maintaining robustness across multiple classes. The integration of an unsupervised clustering module further confirms that the learned feature representations preserve meaningful severity-related structure consistent with expert annotations. In addition, the model maintains a compact design and efficient inference characteristics, making it suitable for deployment on resource-constrained devices such as smartphones and edge platforms. Interpretability analysis using Grad-CAM indicates that the model focuses on pathologically relevant regions of the leaf, supporting transparent and reliable decision-making. Furthermore, the extended ParaLeafNet framework incorporates deployment-oriented optimization techniques that enhance performance without requiring modifications to the core architecture.
Keywords: plant disease severity, parallel convolutional neural network, MobileNetV2, MobileNetV3Small, squeeze-and-excitation attention, multi-task learning, unsupervised clustering, precision agriculture, PlantVillage dataset, biomedical image classification, agricultural health surveillance.
How to cite this article: Siraj B M, Ansari ZA. ParaLeafNet-Severity: A Parallel Deep CNN with Squeeze-and-Excitation Attention for Automated Plant Disease Severity Assessment and Agricultural Health Monitoring. Int J Drug Deliv Technol. 2026;16(15s): 62-71. DOI: 10.25258/ijddt.16.15s.8
Source of support: Nil.
Conflict of interest: None