Towards practical AI for agriculture: A self-supervised attention framework for Spinach leaf disease detection
Nilavro Das Kabya, MD Shaifullah Sharafat, Rahimul Islam Emu, Mehrab Karim Opee, Riasat Khan, Ali Mohammad Alqudah, Ali Mohammad Alqudah, Ali Mohammad Alqudah
TL;DR
This paper introduces an AI framework for detecting spinach leaf diseases in Bangladesh, using self-supervised learning and attention mechanisms to improve accuracy and interpretability.
Contribution
A novel self-supervised attention framework for spinach leaf disease detection with interpretable model decisions and a publicly available dataset.
Findings
The SimSiam-CBAM-ResNet-50 model achieved 97.31% test accuracy and strong robustness to noise.
The proposed framework outperforms large-scale pretrained models in parameter efficiency and deployment feasibility.
Grad-CAM and similar tools highlight biologically relevant lesion regions for interpretable predictions.
Abstract
Malabar spinach is a nutrient-dense leafy vegetable widely cultivated and consumed in Bangladesh. Its productivity is often compromised by Alternaria leaf spot and straw mite infestations. This work proposes an efficient and interpretable deep learning framework for automatic Malabar spinach leaf disease classification. A curated dataset of Malabar spinach images collected from Habiganj Agricultural University and supplemented with public samples was categorized into three classes: Alternaria, straw mite, and healthy leaves. A lightweight SpinachCNN established a strong baseline, while Spinach-ResSENet, enhanced with squeeze-and-excitation modules, improved channel-wise attention and feature discrimination. A customized Vision Transformer (SpinachViT) and SwinV2-Base were further investigated to assess the benefits of transformer-based architectures under limited data. To mitigate…
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Taxonomy
TopicsSmart Agriculture and AI · Plant Disease Management Techniques · Advanced Neural Network Applications