PlantDiseaseNet-RT50: Optimized Plant Disease Detection

• The paper demonstrates that strategic layer freezing and a custom classification head transform ResNet50 into a domain-specialized detector for plant diseases.
• It achieves a balanced performance with accuracy, precision, and recall all near 98%, significantly outperforming the baseline models.
• Additionally, the model’s efficient architecture and advanced training regimen enable real-time deployment and rapid adaptation to diverse agricultural datasets.

PlantDiseaseNet-RT50 is a fine-tuned deep learning architecture derived from ResNet50, designed for high-accuracy automated plant disease detection across diverse crop species and disease categories. Leveraging a strategically modified backbone, custom classification head, advanced regularization, and dynamic learning rate scheduling, this model addresses critical agricultural challenges posed by plant diseases, which are responsible for 70–80% of crop losses globally. PlantDiseaseNet-RT50’s technical innovations and optimization protocol yield a domain-specialized detector with balanced accuracy, precision, and recall near 98%, and computational efficiency suitable for real-time field deployment (Sagnika et al., 20 Dec 2025).

1. Architectural Modifications and Fine-Tuning Protocol

The PlantDiseaseNet-RT50 architecture employs a ResNet50 backbone pretrained on ImageNet, utilizing all original weights except for the 1000-class classification head. A selective freezing technique is applied:

• All network layers up to “conv4_x” are frozen, preventing weight updates and preserving learned general-purpose representations.
• The last 50 layers—including the “conv5_x” residual stage (all bottleneck blocks) and the terminal global pooling and dense layers—are unfrozen, allowing task-specific adaptation via backpropagation.
• Trainable parameters commence at the first bottleneck of block “conv5_1,” extending through the final layer.

A custom classification head replaces the original ImageNet top layers. This head comprises:

1. GlobalAveragePooling2D
2. Dense layer (128 units) with BatchNormalization, LeakyReLU activation (α=0.01), Dropout (p=0.30)
3. Dense layer (64 units) with BatchNormalization, LeakyReLU (α=0.01), Dropout (p=0.40)
4. Dense layer (41 units) with Softmax activation

The output head produces a 41-way categorical probability distribution over species–disease combinations.

2. Training Regimen, Optimization, and Regularization

The training procedure integrates several key strategies:

• Batch size: 32 per iteration
• Epochs: Maximum of 12, with early stopping activated if validation loss does not improve after 5 epochs (restore-best-weights policy)
• Learning Rate Scheduling: Reduction on plateau (factor = 0.1, patience = 3, minimum lr = 1e-6) complements a cosine decay schedule:

$$\eta(t) = \eta_\text{min} + \frac{1}{2}(\eta_\text{max} - \eta_\text{min}) (1 + \cos(\pi t / T))$$

where $\eta_\text{max}$ = 1e-3, $\eta_\text{min}$ = 1e-6, and $T$ is the number of epochs (12).

• Loss Function: Categorical cross-entropy applied over the 41 output classes:

$L = - \sum_{i=1}^{41} y_i \cdot \log(\hat{y}_i)$

with one-hot encoded ground truth $y_i$ and predicted probabilities $\hat{y}_i$.

• Regularization: The architecture utilizes batch normalization and dropout at multiple points in the head (rates 0.30 and 0.40), bolstering generalization and minimizing overfitting. No explicit L2 weight decay is reported beyond these methods.

3. Dataset Construction and Input Pipeline

The Kaggle plant-disease dataset serves as the foundation, containing an estimated 54,000 images (exact count not specified). Class composition spans 41 categories: various plant species coupled with disease types and healthy leaf images.

• Splits: 80% training, 20% validation, with a disjoint held-out test set for final evaluation.
• Image Preprocessing: All samples are resized to 224×224 pixels and pixel values normalized to [0,1].
• Data Augmentation: Random rotations (±20°), horizontal/vertical flips, zoom (±10%), and contrast modifications are applied dynamically during training, improving robustness against real-world visual variability.

4. Performance Metrics and Comparative Analysis

PlantDiseaseNet-RT50’s performance is evaluated using comprehensive metrics, all presented in LaTeX format in the source:

• Accuracy: $$\mathrm{Accuracy} = \frac{\mathrm{TP} + \mathrm{TN}}{\mathrm{TP} + \mathrm{TN} + \mathrm{FP} + \mathrm{FN}}$$
• Precision (per class): $$\mathrm{Precision} = \frac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FP}}$$
• Recall: $$\mathrm{Recall} = \frac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FN}}$$
• F1-Score: $$\mathrm{F1} = 2 \cdot \frac{\mathrm{Precision} \times \mathrm{Recall}}{\mathrm{Precision} + \mathrm{Recall}}$$

Aggregate and per-model results on the test set are as follows:

Model	Accuracy	Precision	Recall	F1-Score
AlexNet	0.79	0.83	0.76	0.79
VGG16	0.91	0.93	0.90	0.92
DenseNet121	0.93	0.92	0.94	0.93
ResNet50 (baseline)	0.38	0.91	0.04	0.08
PlantDiseaseNet-RT50	0.9772	0.98	0.98	0.98

AUC (aggregated) for PlantDiseaseNet-RT50 is 0.9993.

Significant improvements are observed relative to the vanilla ResNet50 baseline: +59.0 pp accuracy (38%→97.7%), +7 pp precision (91%→98%), +94 pp recall (4%→98%), and +90 pp F1-score (8%→98%).

Selected per-class highlights include 1.00 precision and recall for Apple scab, Black rot, Cedar-apple rust, Healthy Apple, and Cherry powdery mildew. Performance bottlenecks are observed in instances such as “Chili leaf curl” (precision=0.56, recall=0.90, F1≈0.69), likely due to insufficient sample size and high interclass visual similarity; Coffee Rust attains F1≈0.71.

5. Computational Considerations and Deployment Feasibility

While explicit FLOPs and per-image inference times are not provided, PlantDiseaseNet-RT50’s memory footprint (backbone ≈25M parameters plus compact classification head) and batch-wise implementation (batch size=32, GPU usage) indicate suitability for deployment on modern farm-side edge devices and mobile GPUs.

Early convergence is notable, with ≥90% validation accuracy achieved by epoch 2 and overall training capped at just 12 epochs. This suggests the model architecture and training protocol support rapid retraining and adaptation for new disease or crop datasets, critical in dynamic agricultural environments.

6. Contextual Significance and Applications

PlantDiseaseNet-RT50 advances AI-driven agricultural diagnostics by demonstrating that conventional ResNet50 architectures, when strategically fine-tuned and augmented with targeted regularization and adaptive learning-rate schedules, can transition from ineffective (38% accuracy baseline, recall 4%) to state-of-the-art, domain-specialized detectors with balanced accuracy, precision, recall, and F1 all approaching or exceeding 98% on highly granular plant disease classification tasks.

A plausible implication is that similar methodologies (layer freezing/unfreezing, custom head engineering, aggressive regularization, and advanced LR scheduling) may be broadly applicable for repurposing generic CNNs to other specialized domains where class granularity and sample diversity impose substantial generalization challenges.

7. Limitations and Prospective Extensions

Limitations noted within PlantDiseaseNet-RT50’s evaluation include reduced per-class performance for visually ambiguous or data-scarce disease categories, as exemplified by “Chili leaf curl” and “Coffee Rust.” The absence of explicit computational cost metrics constrains quantitative comparative analysis for ultralow-power deployment scenarios. Future extensions could address these gaps via advanced imbalance-handling techniques, more granular dataset annotation, and systematic characterization of inference-time resource consumption.

PlantDiseaseNet-RT50 provides a reference implementation for high-throughput, accurate plant disease identification, illustrating the practical impact of domain-aware architectural fine-tuning in applied computer vision for agricultural informatics (Sagnika et al., 20 Dec 2025).