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Multi-FusNet-convolutional neural network with improved Huber loss function for plant leaf disease detection and

B S Shruthi1,2, M S Narasimha Murthy2, Eman Abdullah Aldakheel3

  • 1Department of Computer Science and Engineering, Malnad College of Engineering, Hassan, India, Belagavi, India.

Frontiers in Plant Science
|May 20, 2026
PubMed
Summary

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Reducing Line Loss01:18

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...

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This summary is machine-generated.

This study introduces a novel Multi-FusNet-convolutional neural network (CNN) for accurate plant leaf disease detection and classification. The developed model achieves high performance, significantly aiding agricultural disease management.

Area of Science:

  • Agricultural Science
  • Computer Science
  • Machine Learning

Background:

  • Plant disease detection and classification are critical for agriculture, enabling farmers to prevent disease spread.
  • Overlapping disease characteristics present significant challenges in accurate identification.

Purpose of the Study:

  • To develop an advanced deep learning model for multi-class plant leaf disease classification.
  • To enhance the accuracy and generalization capability of plant disease detection systems.

Main Methods:

  • A Multi-FusNet-convolutional neural network (CNN) integrating a multipath residual network (Multi-RG) with cross-filtering and pixel shuffling fusion was developed.
  • An improved Huber loss function was incorporated to enhance outlier handling and model generalization.
Keywords:
Huber lossconvolutional neural networkgeneralization capabilityplant disease detectionresidual network

Related Experiment Videos

Main Results:

  • The Multi-FusNet-CNN achieved exceptional performance with 99.95% accuracy, 99.13% F1-score, 99.87% recall, 99.27% precision, and 99.93% specificity.
  • The model outperformed existing conventional techniques in plant leaf disease classification.

Conclusions:

  • The proposed Multi-FusNet-CNN model demonstrates improved generalization capabilities for plant leaf disease detection and classification.
  • This advancement offers a robust solution for early and accurate identification of plant diseases in agriculture.