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Related Experiment Video

Updated: May 24, 2026

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement
08:14

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

Published on: January 21, 2013

LeafDet: A lightweight and interpretable deep learning framework for tomato leaf disease detection.

Vaskor Mostafa1, Md Shafak Shahriar Sozol1, M Rubaiyat Hossain Mondal1

  • 1Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh.

Plos One
|May 22, 2026
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Light Acquisition02:16

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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LeafDet, a new YOLOv8-based model, accurately detects tomato leaf diseases using the balanced PlantTom dataset. This advancement improves precision in plant disease identification for global food security.

Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Global food security relies on accurate plant disease identification, but traditional methods lack precision.
  • Public datasets for plant disease detection often suffer from class imbalance, hindering reliable model evaluation.
  • Object detection models require specialized architectures for efficient and accurate disease identification.

Purpose of the Study:

  • To introduce LeafDet, an object detection model for tomato leaf disease identification based on YOLOv8 architecture.
  • To develop a balanced dataset, PlantTom, to address limitations in existing public datasets.
  • To enhance the accuracy and speed of plant disease detection in smart agriculture.

Main Methods:

  • Developed LeafDet, an object detection model integrating CBM, C2f, SPPF, ECA, BiFPN, GSConv, VoVGSCSP, and Shuffle Attention modules.

Related Experiment Videos

Last Updated: May 24, 2026

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement
08:14

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

Published on: January 21, 2013

  • Created the PlantTom dataset with 7836 images across 8 distinct tomato leaf disease classes.
  • Utilized the PIoUv2 loss function and Eigen-CAM for validation and interpretability.

    • Main Results:

    • LeafDet achieved 91.6% mAP@0.5 on the PlantTom dataset, a 2.2% improvement over YOLOv8n.
    • The model demonstrated faster inference time (2.4ms) with 2.69M parameters.
    • LeafDet outperformed other state-of-the-art models, including YOLOv11n and YOLOv12n.

    Conclusions:

    • LeafDet offers a deployable and interpretable framework for plant disease detection.
    • The balanced PlantTom dataset facilitates more reliable model testing and evaluation.
    • The proposed model significantly enhances precision and efficiency in smart agriculture applications.