Deep Learning for Plant Disease Detection: A Practical Framework for Image-Based Crop Diagnosis

Author: Debabrata Pruseth
Publication Date: 2025/09/13
Document Type: Technical Note / Research Article
Language: English

Abstract

Plant diseases can significantly affect crop productivity, food quality, and agricultural sustainability. Early identification of disease symptoms is therefore an important use case for computer vision and applied artificial intelligence in agriculture. This study presents a reproducible deep learning framework for multi-class plant disease classification using leaf images from the PlantVillage dataset. The work builds on the author’s original implementation and compares a foundational EfficientNetB0 transfer learning baseline with an advanced EfficientNetB4-based model. The baseline model used ImageNet-pretrained EfficientNetB0, class weighting, sparse categorical cross-entropy, and partial fine-tuning of the final layers. It achieved 19.52% test accuracy, 39.69% top-3 test accuracy, and a macro F1-score of 0.0911, indicating limited performance across the 38 plant-disease classes. The advanced model used EfficientNetB4 with higher-resolution inputs, alpha-balanced categorical focal loss, mixed precision training, lightweight data augmentation, and fullbackbone fine-tuning. The EfficientNetB4 model achieved a best validation accuracy of 99.59% and a best validation loss of 0.00494 after full fine-tuning. These results suggest that model capacity, image resolution, full-backbone adaptation, and class-imbalance-aware training can substantially improve plant disease classification performance on PlantVillage. However, because the current advanced model results are based on validation performance and the dataset largely consists of controlled leaf images, further test-set evaluation and external validation on field images are required before the framework can be considered deployment-ready.

Keywords
plant disease detection, deep learning, EfficientNet, transfer learning, PlantVillage, focal loss, Grad-CAM, agricultural AI, image classification, crop diagnosis.

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Deep Learning for Plant Disease Detection: A Practical Framework for Image-Based Crop Diagnosis

Suggested Citation
Pruseth, D. (2025). Deep Learning for Plant Disease Detection: A Practical Framework for Image-Based Crop Diagnosis.

Companion Note
This page provides the abstract and full-text PDF for the research version of the article. A companion blog post explains the same work in a more narrative and implementation-focused style.

Read the companion blog:
https://debabratapruseth.com/detecting-plant-diseases-with-ai-a-beginner-friendly-deep-learning-project/