Software application in early blight detection in tomatoes using modified MobileNet architecture.

Abstract

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.

Plant phenotyping relevance

トマト葉の病徴を画像から検出する深層学習パイプラインを開発し、独立データセットで性能検証しており、植物病害状態の表現型取得が中心である。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

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