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This is a Quantitative Study study.

September 29, 2025Open Access

Pepper Leaf Disease Detection Using Deep Learning Techniques

Key Points

The model achieved 97.03% accuracy for pepper classification and 94.63% for apples, demonstrating high efficacy.
Utilizing MobileNetV2 with optimization techniques, this study shows superior results compared to traditional CNN architectures.
Attention mechanisms and patch embedding were critical in improving feature extraction for the classification tasks.
The validation employed datasets specifically from PlantVillage for both pepper and apple crops, underscoring its broad applicability.

Abstract

Early diagnosis of pests and plant diseases is crucial for preventing significant crop losses. This study proposes a leaf disease detection system using MobileNetV2 integrated with multiple optimization techniques (Adam and learning rate scheduling). Evaluated on the Pepper PlantVillage dataset, the MobileNetV2 model employs patch embedding and attention mechanisms for feature extraction, with SoftMax used for final classification. The model was further validated on a multi-class Apple PlantVillage dataset. Results demonstrate high accuracy: 97.03% for pepper and 94.63% for apple classification. Comparative analysis with CNN architectures shows superior efficiency and faster convergence for our model, outperforming Inception v3 (96.81%) and VGG-19 (94.93%) on the pepper dataset.

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Cite This Study

Lafta R. Al-Khazraji (Thu,) studied this question.

synapsesocial.com/papers/68da58d8c1728099cfd1117b https://doi.org/https://doi.org/10.61710/kjcs.v3i3.118

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