Intelligent smart sensing with ResNet-PCA and hybrid ML–DNN for sustainable and accurate plant disease detection

Abstract

Introduction Diseases of plants remain one of the greatest threats to sustainable agriculture, with a direct adverse effect on crop productivity and threatening food security worldwide. Conventional detection methods rely heavily on manual detection and laboratory analysis, which are time-consuming, subjective, and unsuitable for large-scale monitoring. The use of the most recent progress in computer vision and artificial intelligence has opened up a prospect of automated, scalable, and precise disease diagnosis. Methods This paper introduces a feature-efficient hybrid model that trains classical Machie Learning (ML) classifiers with Deep Neural Network (DNN) using ResNet-based feature extraction and Principal Component Analysis (PCA). The PlantVillage dataset with mixed crop-disease pairs is used to implement and thoroughly test five hybrid models. Results Wide-ranging experiments proved that the Logistic Regression (LR)+DNN hybrid resulted in the best classification accuracy of 96.22% as compared to other models and available benchmarks. Besides being able to outperform other techniques in terms of predictive power, the framework displayed good training stability and robustness to class imbalance as well as a higher degree of interpretability based on LIME-based analysis. Discussion The obtained results confirm the hybrid ML+DNN paradigm as a safe, transparent, scalable disease recognition framework when applied to plant diseases. Providing opportunities for timely and accurate disease detection, the proposed framework can help with precision agriculture, where pesticide use can be reduced, consequently, and a significant contribution to sustainable farming can be achieved.

Plant phenotyping relevance

植物病害を対象に、ResNet特徴抽出・PCA・ML/DNNを組み合わせた画像ベースの病害認識手法を開発・評価しており、植物状態の推定方法が研究の中心である。

Code and data availability

The paper uses the public PlantVillage dataset (cited prior work, Mohanty et al., 2016) but provides no authors' public code, trained models, or paper-specific data deposit; no data or code availability URL is given in the supplied blocks.

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