Journal Article2025Cited by 3

Beyond classification: Benchmarking object detection models for efficient tomato leaf disease identification on a real-world dataset

Fahim Mahafuz Ruhad, Md Fahim, Mir Sazzat Hossain, Md Fahad Monir, Ashraful Islam, M Ashraful Amin

Smart Agricultural Technology

Elsevier, Vol. 12, pp. 101336, ISBN: 2772-3755

  • Zero Hunger — Relevance: 48%
DOI: 10.1016/j.atech.2025.101336Open AccessURL

Abstract

Tomato is one of the most economically significant horticultural crops worldwide, yet its cultivation is frequently hindered by foliar diseases such as Late Blight, Bacterial Spot, and Septoria Leaf Spot. These diseases not only reduce yield but also result in substantial financial losses, particularly for smallholder farmers. Traditional manual inspection methods are labor-intensive and error-prone, highlighting the need for automated and accurate disease detection systems. While image classification models have been widely explored for plant disease identification, they often fail to capture spatial symptom details, leading to reduced interpretability and accuracy—especially for visually ambiguous diseases. In this study, we propose an object detection-based approach to localize and identify tomato leaf diseases more effectively. To facilitate this, we introduce a novel, regionally diverse dataset comprising nine common tomato diseases collected across three Bangladeshi regions over three months. We benchmark a range of classification and object detection models, demonstrating that YOLO-v9 achieves superior performance (F1-score: 97.86, mean Average Precision (mAP): 79.44). Furthermore, for real-time deployment on resource-constrained edge devices, we present YOLO-v8-CSwin, a lightweight model with competitive accuracy and significantly fewer parameters (11.12M). Our findings highlight the advantages of object detection over classification for plant disease diagnosis and offer practical insights for deploying AI-driven solutions in real-world agricultural settings.

Keywords

  • Septoria leaf spot
  • bacterial spot
  • YOLOv9
  • Object detection
  • tomato leaf disease
  • plant disease diagnosis
  • lightweight models
  • late blight
  • edge deployment
  • disease localization

CCDS Authors

MD Fahim
Md Fahim

MD Fahim

  • Research Assistant, CCDS

Published at

ACL, EMNLP, ECAI, WACV, LREC, ICPR, BEA, BLP, Information Sciences, Smart Agricultural Technology

Research Interests

Machine Learning, Natural Language Processing

Mir Sazzat Hossain
Mir Sazzat Hossain

Mir Sazzat Hossain

  • Research Assistant, CCDS

Bachelors

Independent University, Bangladesh

Published at

WACV, IJCNN, ICIP, Smart Agricultural Technology

Research Interests

Machine Learning, Computer Vision

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

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Cited By

  1. 1.Zimbini Coka, Markus A. Monteiro, Brent D. Jammer. (2026). Technology-Driven or Farmer-Centred? A Systematic Review of Artificial Intelligence Research for Smallholder Agriculture. Agriculture, 16(14), 1518[10.3390/agriculture16141518]
  2. 2.Sani Isnain, Muhammad Anas, Anees Ali, Sezai Erci̇şli̇. (2026). Artificial intelligence for physiological and molecular monitoring, detection, and forecasting of potato late blight. Physiological and Molecular Plant Pathology, 144, 103260[10.1016/j.pmpp.2026.103260]
  3. 3.Madhavi, Dileep Kumar Yadav, Thipendra P. Singh. (2026). CapsNet-XAI: exploring the fusion of capsule networks and XAI for transparent leaf disease identification & classification. Evolutionary Intelligence, 19(2)[10.1007/s12065-026-01158-7]
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