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<p>This study explores the use of AI-driven machine learning algorithms for the early detection of fungal diseases in tomato plants, a method that enhances diagnostic accuracy and enables more effective crop protection. The research was conducted in 2023 in Shandong Province, China, using two tomato cultivars – Dongfeng-1 and Gypsy. Ten greenhouse plots (2,000&#xa0;m² each) and ten open-field plots (5,000&#xa0;m² each) were studied, with image data collected via high-resolution cameras, multispectral sensors, and microclimate sensors, yielding approximately 20,000 annotated images. Five machine learning models were tested: convolutional neural networks (CNNs), random forests, gradient boosting, support vector machines (SVMs), and k-nearest neighbors (KNN). CNNs demonstrated superior accuracy in both greenhouse (95.2% ± 0.3) and open-field plots (92.5% ± 0.5), with corresponding AUC-ROC values of 0.96 and 0.93 (<i>p</i> = 0.001). The false positive rate for CNNs was 4.1% in greenhouses and 5.3% in open-field plots, while diagnostic time was shorter in greenhouses (8.3&#xa0;s vs. 10.5&#xa0;s). Compared to visual inspection, CNNs significantly improved diagnostic accuracy and reduced fungicide use. To ensure robustness, the models were evaluated under varying lighting and microclimate conditions. Assessments on both GPU and CPU platforms demonstrated the model’s feasibility for deployment on edge devices and cloud-based systems.</p>

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Fungal infections: Classification performance and detectability with machine learning models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月5日

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  • Shihui Jiang,
  • Nidal Al Said,
  • Xiaowei Yin

摘要

This study explores the use of AI-driven machine learning algorithms for the early detection of fungal diseases in tomato plants, a method that enhances diagnostic accuracy and enables more effective crop protection. The research was conducted in 2023 in Shandong Province, China, using two tomato cultivars – Dongfeng-1 and Gypsy. Ten greenhouse plots (2,000 m² each) and ten open-field plots (5,000 m² each) were studied, with image data collected via high-resolution cameras, multispectral sensors, and microclimate sensors, yielding approximately 20,000 annotated images. Five machine learning models were tested: convolutional neural networks (CNNs), random forests, gradient boosting, support vector machines (SVMs), and k-nearest neighbors (KNN). CNNs demonstrated superior accuracy in both greenhouse (95.2% ± 0.3) and open-field plots (92.5% ± 0.5), with corresponding AUC-ROC values of 0.96 and 0.93 (p = 0.001). The false positive rate for CNNs was 4.1% in greenhouses and 5.3% in open-field plots, while diagnostic time was shorter in greenhouses (8.3 s vs. 10.5 s). Compared to visual inspection, CNNs significantly improved diagnostic accuracy and reduced fungicide use. To ensure robustness, the models were evaluated under varying lighting and microclimate conditions. Assessments on both GPU and CPU platforms demonstrated the model’s feasibility for deployment on edge devices and cloud-based systems.