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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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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.
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Maize disease classification using transfer learning and convolutional neural network with weighted loss.

Krisnanda Ahadian1, Novanto Yudistira1, Bayu Rahayudi1

  • 1Informatics Department, Faculty of Computer Science, Brawijaya University, 65145, Malang, Indonesia.

Heliyon
|November 11, 2024
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Summary

This study uses Convolutional Neural Networks (CNNs) to accurately classify maize plant diseases. Machine learning models like VGG16 and EfficientNet achieved high accuracies, aiding in timely crop management and disease intervention.

Keywords:
ClassificationConvolutional neural networkDeep learningMachine learningMaize disease

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Machine Learning

Background:

  • Maize is a vital crop for food and animal feed, but cultivation is threatened by crop failures due to delayed disease management.
  • Technological advancements, particularly in Machine Learning (ML), offer potential solutions for early disease detection and intervention in maize cultivation.

Purpose of the Study:

  • To develop and evaluate Convolutional Neural Network (CNN) models for accurate classification of maize plant diseases.
  • To compare the performance of different CNN architectures (ResNet-18, VGG16, EfficientNet) and optimization techniques for maize disease identification.

Main Methods:

  • Utilized two distinct maize plant image datasets (4,144 images, 4 classes and 5,155 images, 7-8 classes).
  • Addressed class imbalance in the second dataset using weighted cross-entropy loss.
  • Experimented with ResNet-18, VGG16, and EfficientNet architectures, employing Stochastic Gradient Descent (SGD) optimization.

Main Results:

  • VGG16 with a frozen layer achieved 97.146% accuracy on the first dataset.
  • EfficientNet without a frozen layer, combined with weighted loss, attained 94.798% accuracy on the second, imbalanced dataset.
  • Stochastic Gradient Descent (SGD) proved to be the optimal optimizer for both datasets.

Conclusions:

  • CNN models demonstrate high efficacy in classifying maize plant diseases, aiding agricultural management.
  • Model architecture selection and handling of data imbalance are critical for achieving optimal performance in maize disease identification.