Software Application In Early Blight Detection In Tomatoes Using Modified Mobilenet Architecture - Appati, J.K.; Wellu, Z.P.; Amissah, D.K.; Boante, L.M. (Scientific Reports, 2026)
This study presents an automated framework for early blight detection in tomato plants using a modified MobileNet architecture. Addressing the limitations of traditional labor-intensive methods, this study proposes a two-stage pipeline combining (1) transfer learning with depthwise separable convolutions for efficient feature extraction and (2) a meta-learned ensemble of Random Forest, SVM, and Gradient Boosting classifiers to handle real-world variability in lighting and environmental conditions. The approach introduces two custom convolutional layers (Custom_Feature_Extraction_ Block) that improve F1-score by + 3.8 points over the MobileNet baseline, with the ensemble contributing an additional + 2.1 points. Evaluated on a balanced PlantVillage dataset (1,982 images) with extensive augmentation to simulate variable lighting and orientations, the system achieved up to 100% accuracy with selected classifiers on a held-out validation subset of 30 images under controlled conditions. To assess generalization, we further validated the framework on an independent dataset (tomato_dataset_v2, 30, 609 images, 10 classes) containing field-acquired tomato leaf images, where the model attained 94.5% accuracy, confirming robustness beyond control environments. Comparative analysis with 10 recent methods demonstrates superior accuracy-efficiency trade-offs, offering practical on-device decision support for smallholder farmers. The framework’s lightweight design (4.2 M parameters, 23 ms/image on Raspberry Pi 4) and validated scalability underscore its potential for mobile and drone-based agricultural deployment. This addresses critical needs in global food security through accessible plant disease detection.
Research Article
Keywords: MobileNet; Custom_Feature_Extraction_Block; Support vector machine; Random forest; Gradient boosting; Classifier; Ensemble; Overfitting