AI-driven crop disease detection with efficient NetB3 hybrids for sustainable agriculture

Abstract

In precision agriculture, crop disease detection can be a highly valuable undertaking in which scalable and correct solutions may save considerable amounts of money and loss of yield. This paper introduces a comparative analysis of state-of-the-art deep learning models with special attention to EfficientNetB3 hybrids, which are trained on a balanced subsample of the PlantVillage dataset with 33 classes based on nine crops. To overcome the shortcomings of the previous studies, which used unbalanced sample, a leakage-free balancing approach was used, resulting in 13,200 training and 3,300 validation samples. Custom head transfer learning was used where it was tested using two strategies; FreezeUnfreeze fine-tuning, and Singlephase training. MobileNetV2, InceptionV3, DenseNet121, GhostNet, in addition to other baseline CNNs, were compared to baseline Convolutional Neural Networks (CNNs). The findings indicate that EfficientNetB3 hybrids are superior with an accuracy of ≥99.5% and 99.9% Area Under the Curve (AUC) and specificity than the previous CNN-based systems. The paper logically defines a performance ladder between model options and real-life deployment demands, such as lightweight mobile applications to precision agriculture systems, and points out future trends in the field-based validation.

Plant phenotyping relevance

植物画像から病害状態を推定する深層学習モデルの比較・改良が研究の中心であり、植物病害表現型の取得・推定手法に該当する。

Code and data availability

The paper uses the public PlantVillage dataset, but that is a cited prior resource (ref [26]), not a paper-specific deposit. No author code, trained models, curated balanced dataset, or supplementary assets are mentioned with any availability statement or URL.

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