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Plant Science Today (PST)

Research Articles

Vol. 13 No. sp2 (2026): Recent Advances in Agriculture

An ensemble of MobileNetV2 and DenseNet121 for tomato leaf disease detection

DOI
https://doi.org/10.14719/pst.13105
Submitted
6 December 2025
Published
06-05-2026

Abstract

Agriculture plays a crucial role in the global economy and crop production is heavily affected by phytopathological factors, especially plant leaf diseases. Early and precise detection of these diseases is essential because they directly affect crop yields, economic stability and the market value of agricultural produce. This research presents an advanced deep learning framework for accurate detection and classification of tomato leaf diseases. It employs a feature concatenation ensemble approach integrating MobileNetV2 and DenseNet121. MobileNetV2 provides efficient, lightweight feature extraction, while DenseNet121 offers rich hierarchical features through enhanced feature propagation and reuse. The features extracted by both models are concatenated to create a comprehensive representation, improving overall classification performance. The proposed ensemble achieves state-of-the-art accuracy of 99.45 %. This significantly improves disease diagnosis and enables effective crop management, ultimately boosting agricultural productivity.

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